I like thinking about and solving hard problems, and at SIG, that's how I spend most of my time. In academia, that's how I'd spend only some of my time. Most of my day here at SIG is spent working on a current problem or project, whereas during my PhD research, I might spend months writing up my research into a paper, and then repeatedly proofreading. I find actually solving a problem is much more enjoyable than meticulously writing up the solution.
While finishing my PhD, I was exploring my options outside academia. After completing a summer internship with SIG, I got a good sense of the company, its culture, and the type of work I would be doing, and accepted a full time offer to return the next fall.
Though the work I do at SIG is quite different from research in pure math, a doctorate degree teaches you how to think about difficult problems in creative ways, and to not make assumptions. One big difference I learned during my internship is that quantitative research is much more open-ended; you're trying to figure out which variables have an impact on what you're analyzing, or how strong the correlations are. In pure math, you're mainly trying to prove a specific result or a theorem either true or false. Completing a PhD also requires perseverance since you're working on one hard problem for a long time. This experience prepares you to work in a pressured environment with more short term projects.
All interns get the opportunity to work on a project that directly impacts our trading. For mine, I analyzed and calibrated one of our algorithms to determine the fair price of a stock. One important aspect over which I had to optimize was how much of the order book to include in the calculation. It was a project where I was really contributing something and could easily see the impact it would have on trading.
I like the culture at SIG. The firm heavily stresses education, so you never stop learning and you are encouraged to ask questions of the people around you. The environment here is also collaborative. While I may be good in pure math, I'll be sitting next to someone with machine learning and statistics experience, and we're encouraged to collaborate and combine our skills to get to the best possible solution.
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