Preparing for Quantitative Finance Interviews After a Mathematics PhD
Summary
The document considers whether a mathematics PhD graduate should take time to refresh finance and quantitative subjects before applying for quant roles. The response recommends preparing for interviews by reviewing foundational mathematics, probability and statistics, stochastic processes and differential equations, risk measures, and core pricing models. It also points to programming ability and problem-solving questions as parts of the expected preparation.
The advice frames a PhD as a useful qualification but not a guarantee of employment, and suggests applying after building familiarity with the topics likely to be assessed. It does not compare the outcomes of immediate applications with taking a study break, nor does it offer evidence about how recruiters view a six-month pause. Interview content will vary by firm and role, so the topic list is general guidance rather than a definitive syllabus.
Key ideas
- Quant interviews may assess probability, statistics, stochastic analysis, differential equations, and financial models.
- Candidates should review risk measures and basic derivatives pricing concepts.
- Programming skills and problem-solving questions can also matter in the hiring process.
- An applied mathematics PhD can help, but it does not guarantee a quant role.
- The advice is broad and does not establish how a study break affects hiring outcomes.
Tags
Full text
# Answer by Leon (score 2) # Is it necessary to enter quantitative finance directly after a PhD or is taking time out to 'study up' considered acceptable? I will graduate with an applied mathematics PhD within the next few months. My research has been on mathematical/numerical methods for waves in liquids and gas. I have received unsolicited emails about quant finance positions from companies and recruitment services, and I am now considering going into this field. I would much prefer to enter the field after taking 6 months out post-PhD to thoroughly refresh my knowledge of probability/statistic/machine learning and to get up to speed on other relevant areas such as stochastic ODEs/PDEs and financial math and risk. None of these topics were relevant to my research so it is several years since I encountered them and some I have never covered at all. I would be extremely wary of walking into a situation where I felt compromised from day 1 due to being among other quants who specialized in these topics during a PhD or quant finance MSc. I can't stress that enough..of course no-one likes feeling compromised but I know from experience it affects my performance a lot more than the average and its not just a case of 'oh everyone feels like that when starting a new position'. Do you think I would still have a chance at getting into the field if I took this time out to study/refresh these topics for 6 months or would that look terrible? What about discussing this with one of the agencies that has contacted me..would it be a bad idea to mention that I would prefer to do this than interview for a position straight after the PhD? ## Answer by Leon (score 2) https://quant.stackexchange.com/a/37519 If you would like to obtain a job as a quant first you have to pass a job interview in a company. During the job inteview you will be asked about basic math (derivative, integral, limit, etc.), stochastic analysis (martingale, Wiener process, Poisson process, etc.), ODE, PDE, SDE, risk measures, Black-Scholes model, short-interest rate models, Value at Risk, probability, econometry, statistics, etc. What is more, it is very important to have at least basic programming skills (Excel, VBA, SAS, R, C++, Python, Matlab, etc.). You can also expect some brain teasers. More information about job interview details (how to prepare, what to expect) you can easily find on the Internet. Therefore, I would recommend you to prepare yourself in the mentioned above topics first and then apply to obtain a job as a quant (either by using agency or by yourself). PhD title in math is useful but does not guarantee a job...
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.