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Quantitative methods, doctor of philosophy (ph.d.), you are here, a doctoral program focused on measurement and evaluation that trains students to create new research methodologies and design empirical data analyses. .

The Quantitative Methods Ph.D. program is designed to prepare future professors at research universities and principal investigators at research and assessment organizations in education, psychology, and related human services fields.

What Sets Us Apart

About the program.

Rigorous coursework across the field of education will prepare students with the tools needed to conduct cutting-edge research and assessment.  

Fall: 4 courses; Spring: 4 courses

Research apprenticeship Yes

Culminating experience Dissertation

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities as well as important responsibilities at research and assessment organizations. Graduates will be prepared to design first-rate empirical research and data analyses and to contribute to the development of new research methodologies. Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for the  M.S.Ed. degree in Statistics, Measurement, Assessment, and Technology (SMART)  and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation. 

For information about courses and requirements, visit the  Quantitative Methods Ph.D. program in the University Catalog .

Our Faculty

Affiliated Faculty

Eric T. Bradlow K.P. Chao Professor, The Wharton School Ph.D., Harvard University

Timothy Victor   Adjunct Associate Professor, Penn GSE 

"Penn GSE’s Quantitative Methods Ph.D. program equipped me with the methodological skills to do impactful applied education research as soon as I graduated."

Anna Rhoad-Drogalis

Our graduates.

Graduates go on to careers as university professors, researchers and psyshometricians for government agencies, foundations, nonprofits organizations, and corporations. 

Alumni Careers

• Assistant Professor, Texas A&M University-Corpus Christi
• Associate Director, Bristol-Myers Squibb
• Lead Psychometrician, American Institute of Certified Public Accountants
• Research Analyst, Penn Child Research Center, University of Pennsylvania
• Senior Director, Educational Testing Service
• Senior Researcher, Mathematica

Admissions & Financial Aid

Please visit our Admissions and Financial Aid pages for specific information on the application requirements , as well as information on tuition, fees, financial aid, scholarships, and fellowships.

Contact us if you have any questions about the program.

Graduate School of Education University of Pennsylvania 3700 Walnut Street Philadelphia, PA 19104 (215) 898-6415 [email protected] [email protected]

Christine P. Lee Program Manager (215) 898-0505 [email protected]

Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.

All Ph.D. students are guaranteed a full scholarship for their first four years of study, as well as a stipend and student health insurance. Penn GSE is committed to making your graduate education affordable, and we offer generous scholarships, fellowships, and assistantships.

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You May Be Interested In

Related programs.

• Education Policy M.S.Ed. 
• Education, Culture, and Society Ph.D. 
• Higher Education Ph.D. 
• Quantitative Methods M.Phil.Ed.
• Statistics, Measurement, Assessment, and Research Technology M.S.Ed.

Related Topics

Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle. 
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at  [email protected]

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How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD . There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs.

Firstly we'll discuss what sort of candidates you will be competing against when considering going for interview. Secondly, we'll discuss how to make an honest assessment of your PhD and what you got out of it that might be relevant to quantitative finance roles. Finally, we'll consider whether it is necessary to return to school in order to train up in a quant-specific qualification.

The Competition

I've made it rather clear on QuantStart that the competition for some of the top quantitative trading researcher roles can be extremely tough. In the UK the best roles tend to be filled well upstream of any "front door" interview process. Usually extremely bright academics in mathematics, physics, computer science, economics or mathematical finance are head-hunted for a particular skill set, such as deep expertise on market microstructure, insight into high-frequency trading algorithms, novel stochastic calculus techniques for certain derivatives pricing regimes or extensive statistical machine learning knowledge that applies to datasets used by such funds.

When such quant researcher roles ARE opened up to the public they will often state that they are looking for "only the best and brightest", which in the UK usually means "Top Five" universities (Cambridge, Oxford, Imperial College, LSE and UCL). In the US this will mean high-end Ivy League institutions. The adverts will often state that they want to see evidence of consistent Mathematical Olympiad prizes and an extensive publication list in a relevant field.

While this is certainly true of the top roles, there are plenty of other (very well paying and prestigious) jobs that also need filling. Bear in mind that there are only so many Mathematical Olympiad winners, after all! Thus one should not be disheartened when seeing numerous adverts asking for such qualifications. There are plenty of smaller funds and boutique outfits that do not have the resources to aggressively hunt for the ultimate talent and so will be more than willing to employ bright PhDs who might not necessarily have an Olympiad track record.

Honestly Assess Your PhD

The first task to carry out when applying for quant roles is an honest assessment of your PhD and what you achieved with it . Primarily you need to consider the level of mathematical ability you were able to attain as well as your computational programming skill.

Quant roles in the derivative pricing space, known traditionally as the "quant analyst" or "financial engineer", require a reasonable amount of mathematical sophistication. Specifically, expertise in stochastic calculus, probability and measure theory. These are topics usually taught in an undergraduate mathematics course, but can form a component of taught graduate school PhDs. In addition they require a good understanding of scientific programming usually in C++, Python or MatLab. Since the role of a quant analyst is often to code up an implementation of a particular algorithm from a research paper, under heavy deadlines, it is quite naturally suited to those with PhDs of this type.

Quant roles in the algorithmic trading and quant hedge fund world are almost exclusively going to require novel methods for generating "alpha" (i.e. excess return above a benchmark). Usually this is accomplished via time series analysis and econometrics, but more recently statistical machine learning techniques have been applied, as have methods related to sentiment analysis. Some of the best quant funds make extensive use of even more advanced graduate level mathematics in the realms of algebraic geometry, number theory and information theory. Hence anything highly mathematically, statistically or physically oriented is likely to be of interest to a top quant hedge fund.

As for computer scientists and strong scientific software developers, generally there is always work available for quantitative developer roles. Although you will be competing against those with industry experience in rigourous software engineering. Hence "academic code" of the "20,000 line single-file of Fortran" variety might be a bit of a hindrance! Make sure to brush up on the more modern software development methodologies such as OOP , Agile , etc.

I want to discuss specific PhD fields as well, to give you an idea of where you might consider focusing your efforts based on what you have previously studied:

• Pure Mathematics - The top funds generally hire the pure mathematicians from esoteric realms such as algebraic geometry and information theory. Banks will also take individuals who study stochastic calculus to a high level for their derivatives research teams.
• Mathematical Finance - Portfolio optimisation and derivatives pricing are two common themes studied in mathematical finance PhDs. You will often have collaborated with banks during your PhD, so it is unlikely your job prospects will be slim! If you are struggling, it can be very helpful to contact department heads as they will often have a strong network.
• Theoretical Physics - Funds will be very interested in your ability to model physical phenomena, either through direct or statistical approaches. Some theoretical physics areas are highly mathematical (Cosmology, String Theory, Quantum Field Theory etc) and so the advice given to theoretical physics PhDs is similar to pure mathematicians.
• Computational Physics/Engineering - The main skillset taught here is how to take an algorithm and produce a robust scientific computing implementation, perhaps in a parallelised fashion. This is an extremely useful skill for quant work both in banks and funds, especially for developing infrastructure. Make sure however to brush up on core topics such as statistics and stochastic calculus prior to interview.
• Statistics/Econometrics - Statisticians and theoretical econometricians will be in good demand from technical quant funds, especially in the Commodity Trading Advisor (CTA)/Managed Futures space. The time series modelling will be highly appropriate here.
• Computer Science/Machine Learning - Many funds are now making extensive use of machine learning and optimisation tools, which are the natural domain of the theoretical computer scientist and, more recently, the "data scientist". Familiarity with statistical machine learning and Bayesian methods will be highly attractive.
• Bioinformatics - Bioinformaticians also make extensive use of machine learning tools on "big data" sets. For interview you will want to emphasise your familiarity with such tools and your programming capability. Depending upon your background you may need to brush up on your (pure) mathematics for interview questions.
• Economics/Finance - Economics and Finance PhDs do not always teach you the mathematical maturity necessary for pure quant work, but it really depends on the project. You will need to be honest with yourself about where you lie on the mathematical spectrum. In addition you will need to consider your programming ability.

Heading Back To School

An extremely common question that I receive in the QuantStart mailbag is whether to return to school for finance-specific training subsequent to a PhD.

I've previously documented my views on Masters in Financial Engineering (MFE) programs as related to quantitative trading . In essence I believe that MFEs are not hugely suitable for quantitative trading research work, but they are a good entry point into investment banking quant work.

If your PhD was not heavy on quantitative or programming work, but you have a sufficiently mature mathematical background, then it can make good sense to take a MFE assuming that you can afford to fund the course. A MFE at a top-tier school will provide you with a solid network of other candidates (and thus people who might later help you secure a role), a relatively healthy recruitment position upon graduation and a useful skillset for investment banking derivatives pricing work.

I would advise against returning to school if you have a strong quantitative PhD as you simply won't need the additional qualifications and you should be able to pick up the necessary interview material yourself, albeit with a lot of study.

If you have a PhD in a non-quantitative field and your background is not sufficiently mathematical, then you should definitely consider that you will likely need to return to school if you truly want to work in quantitative finance. In particular you will need to study an undergraduate degree that has a strong quantitative component such as Mathematics or Physics as these two degrees will generally let you transition into other quantitative fields easily.

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What Do Quantitative Analysts Do?

Where do quant analysts work.

• Skills and Education

The Right Career for You?

The bottom line.

• Career Advice

Quants: The Rocket Scientists of Wall Street

Quantitative analysts are professionals who understand the complex mathematical models that price financial securities and are able to enhance them to generate profits and reduce risk. As financial securities have become increasingly complex, demand has grown steadily for quantitative analysts , often called simply "quants," or even the colloquially affectionate "quant geeks."

Because of the challenging nature of the work—which needs to blend mathematics, finance, and computer skills effectively—quant analysts are in great demand and able to command very high salaries. Here's a look at what they do, where they work, how much they earn, and what knowledge is required, to help you decide whether this may be the career for you. 

Key Takeaways

• Quantitative analysts, or quants, combine their skills in finance, math, and computer software to analyze and predict the markets, creating complex models that can be used to price and trade securities.
• They tend to work in investment banks and for hedge funds, although insurance companies, commercial banks, and financial software and information providers may also hire them.
• Quants work in major financial centers in the U.S. and in London and Asia, among other places across the globe.
• Firms often look for candidates who have a master's degree or a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics.
• Compensation can be in the low-to-middle six figures.

Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds , but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.

Quants who work directly with traders , providing them with pricing or trading tools, are often referred to as " front-office " quants. In the " back office ," quants validate the models, conduct research, and create new trading strategies . For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.

The high demand for quants is driven by multiple trends:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies  

Quantitative analyst positions are found almost exclusively in major financial centers with trading operations . In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts and Stamford, Connecticut.   Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant's brainpower and abilities. But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one. 

What Do Quants Earn?

Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend. It is not uncommon to find positions with posted salaries of $250,000 or more, and when you add in bonuses, a quant could earn $500,000+ per year. As with most careers, the key to landing the high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities. 

The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm's earnings, also known as the profit and loss (P&L) . At the other end of the pay scale, an entry-level quant position may earn only $125,000 or $150,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.

Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software-development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that's about as high as the compensation package generally would go.

Despite the high pay level, some quants do complain that they are "second-class citizens" on Wall Street and don't earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.

Estimated total pay of a quantitative analyst in the U.S. Google is among the 10 highest paying companies for a quant, offering an annual salary of $279,284.

Quants Skills and Education

Financial knowledge.

Many financial securities, such as options and convertibles , are easy to understand conceptually but are very difficult to model precisely. Because of this hidden complexity, the skills most valued in a quant are those related to mathematics and computation rather than finance. It is a quant's ability to structure a complex problem that makes them valuable, not their specific knowledge of a company or market.

A quant should understand the following mathematical concepts:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Key financial topics include:

• Portfolio theory
• Equity and interest rate derivatives , including exotics
• Credit-risk products

Some quants will specialize in specific products, such as commodities , foreign exchange (Forex) or asset-backed securities .

Computer Competency

Software skills are also critical to job performance. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS or a similar package. Pricing knowledge may also be embedded in trading tools created with Java, .NET or VBA , and are often integrated with Excel. Monte Carlo techniques are essential. A majority of the work is also realized in Python, as scripting-type languages are good for running lots of data and multiple scenarios.

Education and Certifications

Most firms look for at least a master's degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master's degrees in financial engineering or computational finance are also effective entry points for quant careers. Generally, an MBA is not enough by itself to obtain a quant position, unless the applicant also has a very strong mathematical or computational skill set in addition to some solid experience in the real world. 

While most financial certifications, such as the Chartered Financial Analyst (CFA) designation likely wouldn't add much value to a prospective quant's resume, one that may is the Certificate in Quantitative Finance (CQF) —which you may earn globally via distance learning in a six-month intensive program.

Clearly, you need to have "the right stuff" to be a quantitative analyst. It requires both the intellectual ability to master complex and abstract mathematical domains and a willingness to tackle challenges that can seem insurmountable—all while under considerable pressure—which only a select few can do.

But that also doesn't mean that everyone who has the ability to be a quant should become one. The financial problems that quants face are very abstract and narrow. Unlike fundamental or qualitative analysts , quants don't read annual reports , meet with management, visit operations, prepare roadshows, or talk to shareholders . Most of their time is spent working with computer code and numbers on a screen.

Individuals with strong analytical skills are valuable in many different areas of finance, such as economic and financial analysis , for example. Having to compete against the best and brightest quants every single day may not be the quickest path through the ranks, especially for those with broader skills and interests and a desire to manage.

Another career issue to consider is that many Ph.D. quants who come from academic environments find they miss the research environment. Instead of being able to study a problem for several months, when supporting a trading desk you need to find solutions in days or hours. This usually precludes making any breakthroughs in the field. 

Do Quants Get Paid Well?

Yes, quants tend to command high salaries, in part because they are in demand. Hedges funds and other trading firms generally offer the highest compensation. Entry-level positions may earn only $125,000 or $150,000, but there is usually room for future growth in both responsibilities and salary.

How Hard Is Quant Finance?

It take advanced-level skills in finance, math, and computer programming to get into quantitative trading , and the competition for a first job can be fierce. Once someone has landed a job, it then requires long working hours, innovation, and comfort with risk to succeed.

Do You Need a Ph.D. to Be a Quant?

Having a Ph.D. in a subject like math, finance, economics or statistics can be a definite plus for anyone wanting to become a quant. But a master's disease in computational finance or financial engineering can also be the ticket to a career as a quantitative analyst.

Success in quantitative analysis is largely based on knowledge, talent, merit, and dedication instead of the ability to sell, network, or play politics. The quants who work in the field are there because they can do the job well—an environment that many find remarkably refreshing—and they are justly rewarded for their work.

Bureau of Labor Statistics. " Financial Analysts ."

Open Quant. " The Various Types of Quants and Quant Employers ."

Bureau of Labor Statistics. " Financial Analysts: Work Environment ."

Glassdoor. " Quantitative Analyst Salaries ."

Bureau of Labor Statistics. " Financial Analysts: Pay ."

Glassdoor. " How Much Does a Quantitative Analyst Make? "

Duke University Career Center. " Quantitative Analysis ."

Bureau of Labor Statistics. " Financial Analysts: How to Become One ."

Certificate in Quantitative Finance. " Who Is It For? "

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Doctor of Philosophy in Statistics

Program description.

The Statistics PhD degree curriculum at The University of Texas at Dallas offers extensive coursework and intensive research experience in theory, methodology and applications of statistics. During their study, PhD students acquire the necessary skills to prepare them for careers in academia or in fields that require sophisticated data analysis skills.

The PhD program is designed to accommodate the needs and interests of the students. The student must arrange a course program with the guidance and approval of the graduate advisor. Adjustments can be made as the student’s interests develop and a specific dissertation topic is chosen.

Some of the broad research areas represented in the department include: probability theory, stochastic processes, statistical inference, asymptotic theory, statistical methodology, time series analysis, Bayesian analysis, robust multivariate statistical methods, nonparametric methods, nonparametric curve estimation, sequential analysis, biostatistics, statistical genetics, and bioinformatics.

Career Opportunities

Statisticians generally find employment in fields where there is a need to collect, analyze and interpret data — including pharmaceutical, banking and insurance industries, and government — and also in academia. The job of a statistician consistently appears near the top in the rankings of 200 jobs by CareerCast’s Jobs Rated Almanac based upon factors such as work environment, income, hiring outlook and stress.

For more information about careers in statistics, view the career page of American Statistical Association. UT Dallas PhD graduates are currently employed as statisticians, biostatisticians, quantitative analysts, managers, and so on, and also as faculty members in universities.

Marketable Skills

Review the marketable skills for this academic program.

Application Deadlines and Requirements

The university  application deadlines apply with the exception that, for the upcoming Fall term, all application materials must be received by December 15 for first-round consideration of scholarships and fellowships. See the  Department of Mathematical Sciences graduate programs website  for additional information. 

Visit the  Apply Now  webpage to begin the application process. 

Contact Information

For more information, contact [email protected]

School of Natural Sciences and Mathematics The University of Texas at Dallas 800 W. Campbell Road Richardson, TX 75080-3021 Phone: 972-883-2416

nsm.utdallas.edu

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

• PhD Program

Students with excellent quantitative training and an interest in applications are encouraged to apply for admission to the graduate program in statistics.

Qualifications

• Applicants for this doctoral program should have had courses in linear algebra, advanced calculus, and statistics.
• Candidates must have taken the GRE, although no set minimum is required for application consideration.
• TOEFL minimum score is 90. Lower scores will not be considered.
• Both GRE and TOEFL scores must be received directly from ETS.
• GRE and TOEFL Information

Students wishing to apply for admittance into the Department of Statistics and Data Science PhD program will apply through The Graduate School (TGS) of Northwestern University’s online application system. Please review the TGS Application Requirements for information on the application process, fees, required documents, and to access the online application. S tudents are not required to secure a research advisor prior to joining our  Statistics and Data Science PhD program.

The Department of Statistics and Data Science accepts applications for full time students entering in fall quarter only. Applications are accepted exclusively through the TGS online application system.

The online system opens: September 2023

Application Deadline: Applications (including supporting materials) must be received by January 5, 2024 .

Decisions: Admission decisions are made by April 15 . In general, admission is offered only for fall of each academic year.

Go to TGS application page

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Quantitative Researcher – PhD Graduate (US)

Chicago, New York

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Job Description

• Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
• Back test and implement trading models and signals in a live trading environment
• Use unconventional data sources to drive innovation
• Conduct research and statistical analysis to build and refine monetization systems for trading signals
• PhD degree in Mathematics, Statistics, Physics, Computer Science, or another highly quantitative field
• Strong knowledge of probability and statistics (e.g., machine learning, time-series analysis, pattern recognition, NLP)
• Prior experience working in a data driven research environment
• Experience with NoSQL databases (e.g., MongoDB)
• Experience with distributed computing using MapReduce
• Experience with translating mathematical models and algorithms into code (Python, R or C++)
• Independent research experience
• Ability to manage multiple tasks and thrive in a fast-paced team environment
• Excellent analytical skills, with strong attention to detail
• Strong written and verbal communication skills

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Department of statistics and applied probability - uc santa barbara, phd in statistics and applied probability.

Our doctoral program in Statistics and Applied Probability prepares graduate students to expand the boundaries of statistical theory and practice for use in real-world problem solving. Graduates are trained for a career in academics or industry where they are working in and contributing to the forefront of new methods and technology. This program provides rigorous mathematical training in statistics and probability that can be used to develop real-world methodologies applicable to a wide range of interdisciplinary fields including finance, environmental science, computer science, and biomedical science. Recent dissertations have been written in the areas of smoothing splines, spatial statistics, micro-array analysis, functional data models, empirical processes, mathematical and statistical finance, Bayesian inference, and bootstrap estimation methods.

Admission Requirements

Our doctoral program in Statistics and Applied Probability is open to those who hold a bachelor’s degree in Statistics, Mathematics, or other fields with strong quantitative requirements. Students must have a minimum overall grade point average of 3.0; one year of statistical theory that includes hypothesis testing, confidence intervals, best statistics and most powerful tests, regression and ANOVA concepts; and one course in linear algebra that includes vector spaces, bases in vector spaces, eigenvalues, and eigenvectors.

For further admissions requirements and procedures, please visit our admissions page .

Normative Time to Degree

The normative time to advancement to candidacy is 3 years. The normative time for completion of the PhD program is 5 years. Students are expected to have their core courses completed and written qualifying exams passed within the first 2 years.

Registration Expectations

In addition to department requirements, every UC Santa Barbara graduate student is required to follow University policy with regards to degree requirements and registration expectations. You can read over these requirements on the Graduate Division website: here.

Sample Study-Plan

Every full-time student at UC Santa Barbara is required to take 8 units of coursework per quarter. Financial support is contingent on normal progress towards the degree objective.

The following would be a typical program for a well-prepared student seeking a Ph.D. objective with no optional degree emphasis.

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• Dissertation Areas and Joint PhD Programs
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PhD in Econometrics and Statistics

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The Econometrics and Statistics Program provides foundational training in the science of learning from data towards solving business problems. Our students engage in extensive collaborative research on cutting-edge theory in Econometrics, Statistics and Machine Learning as well in applied research from a variety of fields within Booth (such as finance, marketing or economics).

Our program builds on a long tradition of research creativity and excellence at Booth.

Our PhD students become active members of the broad, interdisciplinary and intellectually stimulating Booth community. The program is ideal for students who wish to pursue an (academic) research career in data-rich disciplines, and who are motivated by applications (including but not limited to economics and business). As our PhD student, you will have a freedom to customize your program by combining courses at Booth (including business-specific areas such as marketing, finance or economics) with offerings at our partnering departments at the University of Chicago (Department of Statistics and Kenneth C. Griffin Department of Economics). You will acquire a solid foundation in both theory and practice (including learning theory, Bayesian statistics, causal inference or empirical asset pricing).

Our Distinguished Econometrics and Statistics Faculty

Chicago Booth’s Econometrics and Statistics faculty are committed to building strong collaborative relationships with doctoral students. We serve as research advisors and career mentors. Major areas of departmental research include: learning theory; causal inference; machine learning; Bayesian inference; decision theory; graphical models; high dimensional inference; and financial econometrics.

Bryon Aragam

Assistant Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar

Nabarun Deb

Assistant Professor of Econometrics and Statistics

Christian B. Hansen

Wallace W. Booth Professor of Econometrics and Statistics

Tetsuya Kaji

Associate Professor of Econometrics and Statistics and Richard Rosett Faculty Fellow

Mladen Kolar

Associate Professor of Econometrics and Statistics

Tengyuan Liang

Professor of Econometrics and Statistics and William Ladany Faculty Fellow

Nicholas Polson

Robert Law, Jr. Professor of Econometrics and Statistics

Veronika Rockova

Professor of Econometrics and Statistics, and James S. Kemper Foundation Faculty Scholar

Jeffrey R. Russell

Alper Family Professor of Econometrics and Statistics

Ekaterina (Katja) Smetanina

Assistant Professor of Econometrics and Statistics and Asness Junior Faculty Fellow

Panagiotis Toulis (Panos)

Associate Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow

Dacheng Xiu

Professor of Econometrics and Statistics

A Network of Support

Booth’s Econometrics and Statistics group has been partnering with several (data science and interdisciplinary) research centers and institutes that facilitate the translation of research into practice. Through these venues, our PhD students can foster a strong research community and find additional resources including elective courses, funding for collaborative student work, and seminars with world-renowned scholars.

Data Science Institute at the University of Chicago The Data Science Institute executes the University of Chicago’s bold, innovative vision of Data Science as a new discipline by advancing interdisciplinary research, partnerships with industry, government, and social impact organizations. Center for Applied Artificial Intelligence The Center for Applied AI incubates transformative projects that fundamentally shape how humans use AI to interact with each other and the world. The Center’s innovative research uses machine learning and behavioral science to investigate how AI can best be used to support human decision-making and improve society. Toyota Technological Institute at Chicago Toyota Technological Institute at Chicago (TTIC) is a philanthropically endowed academic computer science institute, dedicated to basic research and graduate education in computer science. Its mission is to achieve international impact through world-class research and education in fundamental computer science and information technology. The Institute is distinctive to the American educational scene in its unique combination of graduate education and endowed research.

The Becker Friedman Institute for Economics With a mission of turning evidence-based research into real-world impact, the Becker Friedman Institute brings together the University of Chicago’s economic community. Ideas are translated into accessible formats and shared with world leaders tasked with solving pressing economic problems. Committee on Quantitative Methods in Social, Behavioral and Health Sciences This is an interdisciplinary community of faculty and students interested in methodological research in relation to applications in social, behavioral, and health sciences. The goals are to create an intellectual niche, exchange research ideas, facilitate research collaborations, share teaching resources, enhance the training of students, and generate a collective impact on the University of Chicago campus and beyond. The Institute for Data, Econometrics, Algorithms, and Learning The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments.

The Fama-Miller Center for Research in Finance Tasked with pushing the boundaries of research in finance, the Fama-Miller Center provides institutional structure and support for researchers in the field. James M. Kilts Center for Marketing The Kilts Center facilitates faculty and student research, supports innovations in the marketing curriculum, funds scholarships for MBA and PhD students, and creates engaging programs aimed at enhancing the careers of students and alumni.

Scholarly Publications

Our PhD students' research has been published in top journals including Econometrica, Journal of Royal Statistical Society, Journal of Econometrics, Neural Information Processing Systems and Journal of Machine Learning Research. Below is a recent list of publications and working papers authored by our PhD students. Modeling Tail Index with Autoregressive Conditional Pareto Model Zhouyu Shen, Yu Chen and Ruxin Shi, Journal of Business and Economic Statistics, (40) 2022 Online Learning to Transport via the Minimal Selection Principle Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan, Proceedings of 35th Conference on Learning Theory (COLT), (178) 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting Boxin Zhao, Samuel Wang and Mladen Kolar, Journal of Machine Learning Research, (23) 2022 Approximate Bayesian Computation via Classification Yuexi Wang, Tetsuya Kaji and Veronika Rockova, Journal of Machine Learning Research (In press), 2022 Reversible Gromov-Monge Sampler for Simulation-Based Inference YoonHaeng Hur, Wenxuan Guo and Tengyuan Liang, Journal of the American Statistical Association (R&R). 2021. Data Augmentation for Bayesian Deep Learning Yuexi Wang, Nicholas Polson and Vadim Sokolov, Bayesian Analysis (In press), 2022 Pessimism Meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Boxiang Lyu, Zhaoran Wang, Mladen Kolar and Zhuoran Yang, In Proceedings of the 39th International Conference on Machine Learning (ICML), (162) 2022 Optimal Estimation of Gaussian DAG Models Ming Gao, Wai Ming Tai and Bryon Aragam, International Conference on Artificial Intelligence and Statistics (AISTATS), (151) 2022 Multivariate Change Point Detection for Heterogeneous Series Yuxuan Guo, Ming Gao, and Xiaoling Lu, Neurocomputing, (510) 2022 Disentangling Autocorrelated Intraday Returns Rui Da and Dacheng Xiu, Journal of Econometrics (R&R), 2021 When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility Rui Da and Dacheng Xiu, Econometrica, (89) 2021 Efficient Bayesian Network Structure Learning via Local Markov Boundary Search Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Structure Learning in Polynomial Time: Greedy Algorithms, Bregman Information, and Exponential Families Goutham Rajendran, Bohdan Kivva, Ming Gao and Bryon Aragam, Advances in Neural Information Processing Systems (NeurIPS), (34) 2021 Variable Selection with ABC Bayesian Forests Yi Liu, Yuexi Wang and Veronika Rockova, Journal of the Royal Statistical Association: Series B, (83) 2021  A Polynomial-time Algorithm for Learning Non-parametric Causal Graphs Ming Gao, Yi Ding, and Bryon Aragam, Advances in Neural Information Processing System (NeurIPS), (33) 2020 Uncertainty Quantification for Sparse Deep Learning Yuexi Wang and Veronika Rockova, International Conference on Artificial Intelligence and Statistics (AISTATS), (2018) 2020 Direct Estimation of Differential Functional Graphical Models Boxin Zhao, Samuel Wang and Mladen Kolar, Advances in neural information processing systems (NeurIPS), (32) 2019

The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation Chen Tong, Zhuo Huang, Tianyi Wang, and Cong Zhang, Journal of Empirical Finance (R&R), 2022

Spotlight on Research

Econometrics and statistics research from our PhD students and faculty is often featured in the pages of Chicago Booth Review.

Is There a Ceiling for Gains in Machine-Learned Arbitrage?

In a recent paper by Chicago Booth’s Stefan Nagel and Dacheng Xiu and Booth PhD student Rui Da, findings suggest that there are limits to statistical arbitrage investment.

How (In)accurate Is Machine Learning?

Three Chicago Booth researchers quantify the likelihood of machine learning leading business executives astray.

Would You Trust a Machine to Pick a Vaccine?

"If we understand why a black-box method works, we can trust it more with our decisions, explains [Booth's] Ročková, one of the researchers trying to narrow the gap between what’s done in practice and what’s known in theory. "

Inside the Student Experience

Damian Kozbur, PhD ’14, says PhD students at Booth have the flexibility to work on risky problems that no one else has examined.

Video Transcript

Damian Kozbur, ’14: 00:01 I went to graduate school in order to develop econometrics tools in conjunction with machine-learning tools in conjunction with economic theory in order to do inference for economic parameters. When you work in high dimensional estimation and you're dealing with problems where the number of variables you're looking at can potentially be in the millions, there's no way to visualize what's going on. Demands now really require that you can handle huge datasets. There's something really satisfying about studying a problem and studying it well. I would say Booth is an excellent place to do it. You have the flexibility to work on really risky problems where you're trying to navigate this landscape that nobody's ever really looked at before. You have an opportunity to dig deeper. You have an opportunity to be rigorous. The faculty is there to help you. They're trying to figure out the same kinds of problems. Things that you figure out cannot always be visualized and it cannot always be easily understood. That doesn't necessarily mean that it's not practical or not useful.

Damian Kozbur, ’14: 01:08 There's an incredible explosion in terms of the amount of data we have on everything. There is an incredible explosion in terms of our understanding of high dimensional econometrics. If you're doing innovative work right now, it will have an impact.

Current Econometrics and Statistics Students

PhD students in econometrics and statistics apply statistical methods to a wide range of business problems, from the effectiveness of machine-learning tools to video-game preferences. Our graduates go on to work in high-profile institutions, generally in academia, finance, or data science.

Current Students

Y ifei Chen Rui Da

Chaoxing Dai

Wenxuan Guo

Shuo-Chieh Huang

Shunzhuang Huang So Won Jeong

Boxiang (Shawn) Lyu Edoardo Marcelli

Zhouyu Shen

Shengjun (Percy) Zhai

Current Students in Sociology and Business

Jacy Anthis

Program Expectations and Requirements

The Stevens Program at Booth is a full-time program. Students generally complete the majority of coursework and examination requirements within the first two years of studies and begin work on their dissertation during the third year. For details, see General Examination Requirements by Area in the Stevens Program Guidebook below.

Download the 2023-2024 Guidebook!

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PhD in Statistics

Study with leading statisticians at a world-class university

  Applications for entry 2024/25 are open  

Funding deadlines: 15 January 2024 (Applications received by this date will be considered for available studentships; we may also be able to consider applications received by the end of March for funding, but this is not guaranteed.) Final application deadline: 23 May 2024  

How to Apply

A PhD offers the chance to undertake a substantial piece of supervised work that is worthy of publication and which makes an original contribution to knowledge in a particular field. Our PhD programme is designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of your topic of interest. 

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from our long history of active contributions to research and teaching in statistics. 

Our core research areas are:

• Data science
• Probability in finance and insurance
• Social statistics
• Time series and statistical learning

If you have any questions about our MPhil/PhD Statistics programme, please  email the Research Manager .  

Research environment

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. It has a rich research portfolio covering core areas of statistical inference and real applications, particularly in the economic, financial and actuarial, social and industrial arenas. The close collaboration with other LSE departments, our London location and strong international partnerships are reflected in the research life of the Department of Statistics through the members of staff, PhD students, postdoctoral research fellows and the thriving visitor and seminar programmes.

Research in the department is concentrated in the following areas and PhD proposals should normally be linked to one of these areas:

Data Science

Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.

Possible areas of research include:

• Bayesian inference and predictions
• Functional data analysis
• High-dimensional statistics
• Machine and statistical learning for relational data
• Network data models, inference and predictions
• Optimisation and machine learning
• Reinforcement learning
• Statistical learning methods in precision medicine
• Statistical models and inference for ranking data
• Stochastic models of epidemic processes
• Stochastic optimisation methods
• Stochastic processes in econometrics and finance

For more information about potential supervisors and their areas of interest, visit the Data Science research group .

Probability in Finance and Insurance

PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics.  Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc. 

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

Suggested research areas of PhD research projects include:

• Energy markets
• Excursions of Lévy processes and applications in finance and insurance
• Financial market with frictions
• Information asymmetry
• Interface between insurance and finance
• Lévy processes
• Optimal stopping
• Point processes in insurance and credit risk
• Quantile options and options based on occupation times
• Stochastic analysis and its applications in financial mathematics
• Stochastic control and analysis of partial differential equations in mathematical finance

This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the  Probability in Finance and Insurance research group . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal. 

Social Statistics

PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

• Analysis of complex survey data
• Disclosure risk assessment and statistical disclosure control
• Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
• Latent transition and latent class models for modelling diagnostic tests
• Latent variable models and structural equation models for categorical data
• Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
• Modelling response strategies and detection of outliers in educational and behavioural sciences
• Multilevel simultaneous equations modelling of correlated social processes

For more details about potential supervisors and their areas of interest, visit the  Social Statistics research group . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.

Time Series and Statistical Learning

PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.

Suggested PhD research areas include:

• Automating statistical advice
• Change detection for complex data
• Dimension reduction and factor modelling
• Estimation of stochastic volatility models
• Financial econometrics
• Functional data analysis including functional time series
• High-dimensional time series analysis
• High-dimensional variable selection
• Infectious disease modelling
• Inference for sequential data including change detection in multiple data streams
• Network time series analysis
• Nonparametric and semiparametric regression
• Non-stationary time series analysis
• Reinforcement learning for time-dependent data
• Robust statistical analysis for high-dimensional data
• Shape-constrained methods
• Spatial econometrics modelling
• Spatio-temporal modelling
• Statistical analysis of high-dimensional multi-type recurrent events

For more information, please see the  Time Series and Statistical Learning research group . 

PhD Programme Structure

PhD Your application

PhD Funding Opportunities

FAQs Frequently Asked Questions

PhD Handbook Our guide for current students

Past PhD students A listing of our PhD graduates

Visiting Research Students How to apply

PhD Academy A dedicated space for PhD students

Graduate Career Destinations What our PhD graduates have done next

PhD Admissions Requirements and Procedures

Requirements.

Thank you for your interest in applying to the doctoral program offered by the Department of Statistics.

• PhD in Statistics

Contact: stat-admissions-PhD [at] lists.stanford.edu ( stat-admissions-PhD[at]lists[dot]stanford[dot]edu )

All graduate applications are reviewed at the department level. Please read the following information and the Admissions FAQ page carefully. We have made a great effort to provide detailed, thorough and relevant information regarding the application process and hope that it will provide answers to your questions.

APPLICATION DEADLINE - Thursday, November 30, 2023 (11:59PM PST)

The deadline for all graduate application forms to be submitted online for the Department of Statistics for matriculation in Autumn Quarter 2024 is Thursday, November 30, 2023 . We do not matriculate students in any other quarter. The online application fee is $125 for all applicants, both domestic and international. Click here for details regarding the Application Fee Waiver .

Self-reported (unofficial) test scores that could not be included with the online application should be emailed to stat-admissions-PhD [at] lists.stanford.edu (stat-admissions-PhD[at]lists[dot]stanford[dot]edu) and must be received by the deadline above.

Recommenders must submit online recommendation letters by the deadline above.

IMPORTANT: Effective March 2021, the GRE Math Subject Test is no longer required. Applicants to our program are required only to take the GRE General Test.

Applicants who do not have a bachelor's degree from a university based in the U.S. should review the information for international student eligibility to apply to graduate school at Stanford . The applicant should be sure that the bachelor's degree or equivalent will be conferred before the intended program start date.

Offers of admission cannot be made without receipt of official test scores.

What do I need to submit?

Applicants can access the online application and all accompanying information beginning in mid-September. If you encounter any problems while completing or submitting the application, please contact the support team by clicking Request Application Support in the Instructions section of the application. Since the Stanford application software is not Stanford-based, we are unable to help with technical issues.

Explain the nature of your interest in one specific program in the department and reasons for wanting to study at Stanford. In the first sentence of your statement of purpose, indicate the name of the specific program to which you are applying. Include details about your preparation for this field of study, your future career plans, and any other aspects of your background and interests which may aid the admissions committee in evaluating your aptitude and motivation for graduate study. Your statement should be typed, single-spaced, and no longer than two pages, ensuring that your full name and the program to which you are applying is on each page. If you are applying to Stanford together with another person (siblings, couples, etc.) and would not accept admission if both were not admitted, please let us know. Note that once your application has been submitted, we cannot accept any revised statements of purpose, résumés or transcripts.

Do not upload academic papers, theses, or dissertations as part of your statement of purpose. These items are not required. If you wish to submit them, please do so separately by uploading them in either the Experience section of the application (if applicable) or the Additional Information section. We do not accept materials submitted via email or mail.

List every post-secondary institution where you were enrolled — or are currently enrolled — in an undergraduate or graduate degree program. Your list must include the institution where you earned your bachelor’s degree or its international equivalent as defined in Stanford’s minimum education requirements for graduate study.

For each institution you list, upload your transcript. The transcripts you upload as part of your application are considered unofficial and will suffice for the review process.

If you are offered admission to Stanford AND accept the offer, you will be required to submit official transcripts/degree conferral documents. Instructions are provided on the status page immediately after accepting the offer.

If you have participated in a study abroad program or transferred courses to your home institution, and the individual courses and grades are reflected on the transcript of the home institution, you do not need to submit separate transcripts for the study abroad/transfer institution.

Multiple page uploads are allowed for all transcripts. Do not send official copies of transcripts (print or e-transcripts) to the department during the application process. They are not required as part of the application and will not be uploaded to your application or acknowledged.

See the Graduate Admissions transcripts web page for additional guidance on transcripts.

GPA/Converting International GPA

GPAs of at least 3.5 are strongly recommended.

Applicants must include GPAs for all current and completed degree programs. If the applicant's school does not use any scoring system or only provides grades at the end of the degree program, please note this in the second free text field of the Academic History section of the application.

Applicants whose school's scoring system does not use the 4.0 scale should enter the original GPA and GPA scale (e.g., a scale of 1-30 or 1-100) as it appears on your transcript. Do NOT convert your GPA to a 4.0 scale if it's reported on a different scale.

GRE Math Subject Test: Effective March 2021, the GRE Math Subject Test is no longer required. Applicants to our program are now required only to take the GRE General Test. The GRE Math Subject Test will not be considered during the review of applications. GRE Math Subject Test scores submitted to Stanford University will not be made available to our department.

IMPORTANT: Please note that it can take 2-3 weeks for your official test scores to arrive from ETS. In addition, the department cannot see or access these scores until approximately two weeks after you have submitted your online application. Finally, the test score status in your checklist (viewable after submission of the application) may take an additional 10 business days to change to "official". Applications will be considered complete with unofficial scores, although no offers of admission can be made without receipt of official scores.

GRE General Test ( gre.org ): Applicants must take the Graduate Record Examination (GRE) General Test administered by the Educational Testing Service (ETS). Applicants who already hold a PhD degree from an accredited institution may request a waiver for the general test (see below).

Applicants wishing to submit an application for matriculation in 2024 must be able to provide at least self-reported (unofficial) test scores for the verbal and quantitative sections of the GRE general test by the Statistics Department's PhD application deadline. All official score reports must be sent electronically by ETS to Stanford University. Our institution code number for ETS reporting is 4704. No department number is required. Test scores may be no older than five years, dating back from the Statistics Department's PhD application deadline. The GMAT is not accepted as a substitute for the GRE.

Applications will be processed using the self-reported (unofficial) test scores. If you submit your online application before you have taken the test, you may add the scores later, as long as it is still prior to the Statistics Department's PhD application deadline. Offers of admission are contingent on receipt of the official scores.

While we have not established any particular GRE score necessary for admission, the average General GRE percentile scores of recently admitted applicants are Verbal 92%, Quantitative 94% and Analytical Writing 83%. If you submit results from more than one eligible test date we will consider the higher of the scores.

If you have already earned a PhD degree, or your PhD studies are in progress when you apply and your degree will be conferred prior to your intended start quarter, you may request a GRE General Test waiver by emailing stat-admissions-PhD [at] lists.stanford.edu (stat-admissions-PhD[at]lists[dot]stanford[dot]edu) with the following information and attaching an unofficial copy of your PhD transcript: full name, institution attended, degree earned, and degree conferral date. Use the subject heading "GRE General Test Waiver Request".

IMPORTANT: Please note that it can take 2 to 3 weeks for your official test scores to arrive from ETS. In addition, the department cannot see or access these scores until approximately two weeks after you have submitted your online application. Finally, the test score status in your checklist (viewable after submission of the application) may take an additional 10 business days to change 'official'. Applications will be considered complete with unofficial scores, although no offers of admission can be made without official scores.

Adequate command of spoken and written English is required for admission. Applicants whose first language is not English must submit an official test score from the Test of English as a Foreign Language (TOEFL). Stanford accepts only ETS (Educational Testing Service) scores. Our institution code number for ETS reporting is 4704. No department number is required. Test scores may be no older than 24 months, dating back from the Statistics Department's PhD application deadline. Scores expire after two years and will not be available from ETS.

We accept the TOEFL iBT Home Edition and TOEFL iBT Paper Edition if you are unable to take the traditional TOEFL iBT test in a test center. If you take the Home Edition or Paper Edition, you may be required to complete additional English placement testing prior to enrollment. We do not accept TOEFL Essentials scores or any other English proficiency test (e.g., IELTS, PTE).

Exemptions are granted to applicants who have earned (or will earn, before enrolling at Stanford) a U.S. bachelor’s, master’s, or doctoral degree from a regionally-accredited college or university in the United States (territories and possessions excluded), or an equivalent degree from an English-language university in Australia, Canada, Ireland, New Zealand, Singapore, and the United Kingdom.

You may request a waiver if you (will) have an equivalent degree from a recognized institution in a country other than Australia, Canada, Ireland, New Zealand, Singapore, and the United Kingdom in which English was the language of instruction. You must submit a Stanford application before submitting a TOEFL waiver request form. Note that U.S. citizenship does not automatically exempt an applicant from taking the TOEFL if the applicant’s first language is not English.

A minimum TOEFL score of 100 on the Internet based test (iBT) is required by Stanford University for all Ph.D. applicants. However, please note that the Graduate Admission's Required Exams webpage also states that incoming students who score below 109 on the TOEFL will likely be required to complete additional English placement testing prior to enrollment. Evidence of adequate English proficiency must be submitted before enrollment is approved by Graduate Admissions. The average TOEFL score of Ph.D. applicants admitted to the statistics department is 112. The Test of Written English (TWE) portion of the TOEFL is not required. Stanford accepts MyBest scores but does not currently accept TOEFL Essentials test scores.

Applications will be processed using the self-reported (unofficial) test scores. If you submit your online application before you have taken the test, you may add the score later, as long as it is still prior to the Statistics Department's PhD application deadline. iBT test-takers should be able to access their scores online approximately 10 days after the test date. We recommend that you take the test no later than early November 2023 to ensure that you meet the Statistics Department's PhD application deadline application deadline. Offers of admission are contingent on receipt of the official scores.

Three letters of recommendation are required. Letters of recommendation are managed via an online recommendation system, which is part of the online application. Applicants will be required to register the contact information of their recommenders who will then receive an email with directions on how to proceed. Recommendations must be submitted by the Statistics Department's PhD application deadline.

We do not accept emailed or paper recommendations. However, we do accept letters submitted by your university's letter service. If this applies to you, you will still need to enter information for each recommender in the online application, including e-mail addresses which will automatically generate the email to each recommender requesting a letter. It is your responsibility to contact them to let them know to disregard this email and to use the university's letter service. Please use stat-admissions-phd [at] lists.stanford.edu (stat-admissions-PhD[at]lists[dot]stanford[dot]edu) when routing through the letter service.

The recommendation process now supports letters submitted via Interfolio. The applicant registers a recommender using an email address that contains "interfolio.com". Please remember that letters written specifically for your Stanford graduate program tend to be stronger than letters written for general use purposes. Furthermore, the recommendation form displayed for a recommender using Interfolio will not include the evaluation questions displayed and required for other recommenders.

Your letters should be written by those who have supervised you in either an academic or employment setting. If possible, at least one should be from a university professor familiar with your academic work. Your recommendations should directly address your suitability for admission to the Statistics department. We pay extra attention to the potential for future excellence, as indicated in letters of recommendation. The most important factors we look at are: quantitative and analytic strength, communication skills in English, leadership, maturity and focus.

Note: If you have any concerns that one of your recommenders will not be able to submit their letter by the deadline, you may want to consider requesting letters from a total of four recommenders. This will increase the likelihood that three letters will be submitted by the deadline and that your application will be marked as complete.

Applicants must upload a resume/CV into the Experience section of the application. Applicants may also upload additional papers such as samples of your academic or published works in this section. Any additional papers will be included in your application file, though there is no guarantee that they will be reviewed by the admissions committee. Do not send any duplicates of materials that you have submitted online; they will not be added to your file.

Applicants who indicate that they would like to be considered for the master’s program in the Program Selection section of the online application must confirm this by submitting an email request within three business days of receiving the PhD admissions decision notification. Note that it is not guaranteed that the MS Admissions Committee will accept your application for review. You will be notified via email whether your request has been approved.

Notification of Received Application Materials

Please refer to your application checklist on the Graduate Application Status page of your online application to determine whether any materials have yet to be received.

Financial aid

Please note that requesting financial aid on the application form will not affect your chances of being admitted to one of our graduate programs. All prospective students should review the estimated expenses associated with graduate study at Stanford.

Financial support

All students accepted to the Ph.D. program are guaranteed 5 years of 12-month funding. Financial support typically is provided through teaching and research assistantship salary and tuition allowance.

Students are strongly encouraged to apply for outside scholarships, fellowships, and other forms of financial support. Students with outside support enable the department to stretch its own resources. The department will supplement outside awards to the level set for departmental support. More information about financial support.

For more information

For more information, please review the Stanford Graduate Admissions web site. If you still have questions after carefully reviewing this page and our FAQ page, please contact us at stat-admissions-PhD [at] lists.stanford.edu (subject: Admissions%3A%20LAST%20NAME%2C%20First%20Name) (stat-admissions-PhD[at]lists[dot]stanford[dot]edu) ; type "Admissions: LAST NAME, First Name" in the subject line of your message, specifying your last and first names. We receive a large number of requests for information, and therefore appreciate your understanding in the event that there are delays in receiving a response.

Good luck! We look forward to receiving your application.

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• Education Advice

Need advice: statistics Phd gets into IB

• Thread starter Karol Meng
• Start date 3/10/09

I'm a second year PhD in statistics at a good US stat department. Well, I know it's easy to get a stat/biostat job after graduation, but I just want to get into IB. I really need some advice about what I should do to find a IB job. Just take as many econ/MBA finance/CS phd courses as possible without getting any of these degree? Or, take CFA exams? Or, go to a MFE program after graduation? I don't know which is a better way to be more competitive in the job market. Thanks.  

Older and Wiser

how long before you graduate? Why do you want to go to an IB?  

I still have 3 years before graduation. Now I just want to have a clear plan of what to do in the next 3 years before entering job market. For the reason to go to IB...... Well, though it's easy to get a statistics job, our work is just a support of other professionals but never have the kernel techniques. Besides, I took asset pricing phd course last year. I found it interesting and not difficult for me...... There are many reasons to be interested in finance and investment bank.  

Hey Caroll I am in a similar situation. I got a Master in applied mathematics with emphasis in finite difference treatment of PDEs. I've just been offered admission in a couple of Phd programs in stat. I will sit for the qualifying examinations a week after the beginning of the fall semester. I want to be a quant as well and I think that as long as you know what beeing a quant is all about (u should do your homework to figure it out) you should choose the right focus for your phd thesis. This is what I came out with: 1-) Phd in mathematical -statistics with focus in stochastic processes 2-) PhD in statistics with application in risk management 3-) PhD in stat with focus on statistical arbitrage 4-) PhD in stat with application in quantitative finance (univ florida offers such an option) 5-) PhD in statistics with emphasis in financial modeling 6-) learn a lot of C++ programming and get some nice projects done in C++ along with an internship before graduation. It wouldn't hurt I guess to lay out there a couple of finance-related publications. there are many more options when you want to find a smooth transition from statistics to quant. I've designed my personal plan for the next two or three years in the stat phd program. - focus in risk management which overlaps heavily with econometrics. - get a master in financial mathematics with focus in derivative pricing (I've been told I should take no more than a year since I've taken all the required math classes pde,num analysis stochastics calculus,measure and integration etc...) In a totally different note do not forget that as a statistics pHd you are in good position to complete at least four exams in road to be an actuary. Which is also a well paid job and is consistently ranked among the best jobs in the country. Statistician have a lot of options out there :dance: It would be interesting to get an actual quant's opinion about all this anyway. Cheers !#-o  

Andy Nguyen

Statistician have a lot of options out there Click to expand...

“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.” The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm. Click to expand...

Lowly Undergrad

This thread is making me want to go for a Ph.D in Stats. Can any one make a list of the top programs?  

Go to USNews site and look at their ranking.  

3 years is a long time. Focus on what you are doing right now. Decide 1 year before you graduate.  

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As a Quantitative Systematic Trading Intern at SIG, you will work on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build foundational knowledge in quantitative finance.

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• Students in their 4th or 5th year of their PhD program studying in a quantitative field such as Mathematics, Physics, Statistics, Electrical Engineering, Computer Science, Operations Research, or Economics
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By applying to this role, you will be automatically considered for the Quantitative Research Internship program. There is no need to apply to both positions to be considered for both.

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Have you ever wondered what happens when a physics PhD collides with the high-stakes world of algorithmic trading? Join us as Tom, a former academic turned quant trading guru, shares his extraordinary pivot into finance. With a foundation in computer simulations, he has reshaped the stock market landscape using automated strategies, and he's here to peel back the curtain on his journey from skepticism to success. Learn how an analytical mind from the world of science approaches the chaos of the markets, wielding simplicity and statistical insight like a sword to cut through the noise of complex models. As the conversation unfolds, Tom takes us through the intricacies of AI, demonstrating its transformative power in trading and portfolio management. This episode isn't just about predicting market trends; it's about the holistic application of models for risk management and efficient trade execution. Discover how deep learning challenges old-school skepticism, and how AI, when harnessed with wisdom, can propel investment strategies to new heights. Tom's expertise doesn't stop at theory; he provides actionable insights into the synergy of technology and finance, pushing the boundaries of what's possible in today's rapid-fire trading arenas. The finale of our discussion celebrates the revival of Quantopian, a community platform where Tom has been instrumental in fostering a space for collaboration and education in quantitative finance. Through engaging workshops and a YouTube channel dedicated to demystifying the field, Tom champions the value of hands-on experience. He also touches on the fascinating phenomenon of double descent in financial modeling, stirring curiosity for the future of machine learning in finance. This is an episode that promises not only to enlighten but also to inspire, as we explore the intersection of human creativity and the evolving landscape of financial technology.

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