(Traineeship for Computer Sciences, Information Science, Physics, Mathematics)
Jane Street is a quantitative trading firm that uses innovative technology, a scientific approach, and a deep understanding of markets to guide our business. We are a global liquidity provider and market maker, operating around the clock and around the globe, out of offices in New York, London, Hong Kong, and Amsterdam.
Researchers at Jane Street are responsible for building models, strategies, and systems that price and trade a variety of financial instruments. As a mix of the trading and software developer roles, both intern and full time work involves many things: analyzing large datasets, building and testing models, creating new trading strategies, and writing the code that implements them.
We’re constantly recruiting for Full Time Quantitative Researchers.
We also run a summer internship program, and intern applications are typically open from late summer until early winter. Our internship program is very competitively paid.
The research environment is intellectual and collaborative, relaxed and playful, with a strong focus on education. You’ll learn about trading and our technology stack through in-house classes, guest lectures, and on-the-job experiences. You’ll have the freedom to get involved in many different areas of the business and we’re small enough that you can quickly and clearly see the impact of your work.
Researchers are tasked with working on projects to help us refine our computational methods and improve our ability to analyze market data. These are true research projects in the sense that we ask our interns to investigate difficult questions to which we do not know the answers. Here are some questions past research interns have considered:
When numerically integrating many different (but related) functions, is there some transformation of the problem that allows some expensive part of the computation to be shared?
Common robust regression techniques, implemented naively, require "remembering" all of the data. Our data sets are often too big for this. How can we accomplish the same thing with a low memory footprint?
Sometimes the markets behave strangely in a way that is obvious to humans. Can we get a computer to recognize these situations?
Many beloved statistical methods assume the world is static, and that observations are independent, but these assumptions don't hold in practice. What can we do about it?
How should we measure the market impact of our own trading?
How can we efficiently filter Twitter feeds down to market-relevant content?
As a researcher, you should:
Be able to apply logical and mathematical thinking to all kinds of problems. Asking great questions is more important than knowing all the answers.
Write great code. We mostly write in OCaml, so you should want to learn functional programming if you don't already have experience with it.
Have good taste in research. The problems we work on rarely have clean, definitive answers. You should be comfortable pushing in new and unknown directions while maintaining clarity of purpose.
Think and communicate precisely and openly. We believe great solutions come from the interaction between diverse groups of people across the firm.
If you're interested, please click here to apply.