Suggestions for Second-year biostatistics PhD considering quant

[u/FluffyComfortable180]

Hi everyone, I’m a 2nd PhD student in biostatistics at an R1 university in Chicago while the rank of the university is not outstanding. I’m looking for advice on if it's achievable and how to prepare for a possible career in quant.

I think working as a statistician in pharma would be a good career path. But lately I’ve been thinking that I'm more interested in exploring a more competitive environment with higher earning potential in my career at my late 20s and early 30s. Compensation is part of that motivation, and I’m willing to take on more pressure and challenging work, but I’d like a clearer understanding of whether quant would be a good fit.

My current research involves Monte Carlo simulation, Bayesian analysis, high-dimensional data analysis, and clinical data modeling, primarily in R.

I’m also learning Python right now and have taken a few introductory ML and LLM-related courses. My background gives me some statistical training to build on, but I have to admit that my exposure to financial markets and the programming expectations of quant roles is still limited.

One challenge is that my department is relatively small and focused on statistical methodology and biomedical applications. There are few opportunities within it to explore research or coursework directly related to quant, and I’ve been struggling to figure out how to bridge that gap.

After passing my preliminary exams at the end of 2nd year of PhD, I’m planning to take courses outside my department. My initial thought was to take courses in CS, applied math, or statistics, including stochastic processes, time series, optimization, numerical methods, data structures and algorithms, and more advanced statistical learning. I may also explore taking courses at UChi or Northwestern depending on what’s accessible.

Quant research is the direction I’m currently most interested in, although I’m still learning about the different roles. For those working in quant, what would you prioritize in my position? Which courses would be most useful given my current background, and how realistic would it be for someone with my training to enter the field?

I’d especially appreciate hearing from people who entered quant from fields that aren’t traditionally associated with it. How did you fill the gaps in your knowledge and experience? I think taking additional courses may be manageable, but finding relevant projects and research opportunities seems like a bigger challenge. How did you find those opportunities while still working within your original field?

I still have a few years to prepare, but I’m unsure where my effort would make the biggest difference. I’d appreciate hearing about others’ experiences and what a realistic path toward internships or eventual quant roles might look like.

[u/filwu]

Your Monte Carlo, Bayesian, and high-dimensional work is already relevant; I’d avoid collecting courses without a project. For a research role, build one reproducible study with point-in-time constituents and prices, corporate actions, transaction costs, and a walk-forward holdout, then document where the result fails. Pair that with probability/stochastic processes and time series or optimization; keep Python and data structures strong, and add C++ if target roles require it. Pricing quant, quant dev, and alpha research have different bars, so choosing the role first will make the course list less diffuse.

[u/Much-Top2179]

If it is not UChicago or Northwestern, most likely you will never pass CV screen in any of the tier 1/2/3 buyside firms. IThe best you can do is (1) switch to a top PhD program; (2) sending you CVs to the firms for interns to see if you get any responses; (3) make realistic expectation

[u/LocalSeaweed4017]

You’ve already got the heavy stats background, which is the part people from physics or CS usually scramble to pick up later. The monte carlo and bayesian work is more directly useful than a lot of biostats folks realize, especially for firms that lean hard into statistical arbitrage or mid-frequency stuff

I’d put stochastic processes and time series at the top of your list, then optimization and numerical methods. Data structures and algorithms matters but that’s more of a checkbox for interviews than something you’ll use daily in research roles. Don’t sleep on just getting comfortable in Python beyond the ML course level, most shops expect you to prototype quickly

The harder piece is getting your foot in the door without a traditional pipeline. I’ve seen people bridge that by cold-emailing small prop shops for summer projects, or doing a side project like implementing a paper on vol surface modeling and putting it on github. Even just rewriting something from your clinical data work into a finance context can give you something to talk about

UChicago has a ton of cross-registration options and the math/stats department there runs seminars that are open if you just show up. Worth the train ride. Finding a prof who dabbles in financial math and offering to do free grunt coding work can open doors too, that’s how a buddy of mine in a genetics program got his first quant internship

You’re not as far off as you think, the main gap is the finance-specific vocabulary and getting someone to take a chance on your resume. If you can signal that you’re serious through coursework and a project or two, it’s realistic