About the Position
We are looking for a Statistical Arbitrage Research Analyst who is excited to apply rigorous math and statistical methods to analyze a variety of input datasets to create novel alpha-focused trading strategies for Jane Street. Your work has the potential to span across any and all liquid asset classes, including, but not limited to, U.S. and global equities, equity and fixed income futures, FX, and corporate bonds.
Ideally, you will have previous experience working in a buy-side or sell-side financial firm with some combination of asset price returns data, non-returns-based traditional data, and “alternative” data sets. However, if you are an economist or data scientist in a different field (such as tech) we’re open to teaching you what you need to know to thrive in this role.
We are looking for someone who is eager to dig deep into the details of data sets to assess quality and consider outliers, dimensionality, feature engineering, causality, aligning dates across datasets, and more.
You’ll help us stay vigilant in our efforts to find and correct errors or mistakes in code, which inevitably happen — though we expect this role to involve as much time delving into the lovely messiness and complexity of data as it will on advanced statistical modeling.
The problems we work on rarely have clean, definitive answers, and they often require insights from people across the firm with different areas of expertise. We find that we make the most progress when team members collaborate and communicate fluidly. Your success in this role will depend on your ability to balance expertise and intellectual rigor with an open mind to a variety of techniques and modes of thinking.
We don’t believe in “one-size-fits-all” solutions; we are open to and excited about applying all different types of mathematical and statistical techniques, depending on what best fits a given problem. Progress takes place at different tempos on our team depending on the project, so you’ll need to be comfortable embracing both large leaps and incremental steps forward.
About You
- 2-6 years of professional experience working in a data-rich environment in quantitative research
- Team player with a highly collaborative mindset; communicate clearly and often and enjoy discussing research ideas and results in depth
- Open to a variety of techniques and modes of thinking
- Humble about what you do and don’t know; willing to admit mistakes
- Enjoy learning new skills and teaching others what you know
- Able to write code and analyze large datasets
- Experienced with statistical and ML modeling
- Knowledge of Python preferred, but not required
- Background knowledge of financial markets is a plus
If you're a recruiting agency and want to partner with us, please reach out to [email protected].
Top Skills
What We Do
Jane Street works differently. As a liquidity provider and market maker, we trade on more than 200 trading venues across 45 countries and help form the backbone of global markets. Our approach is rooted in technology and rigorous quantitative analysis, but our success is driven by our people.
Our bright, beautiful offices in the heart of New York, London, Hong Kong, and Amsterdam are open and buzzing with conversation. We come from many backgrounds and encourage travel between offices to share perspectives. Some of our best ideas come from bumping into a visiting colleague at the office coffee bar.
Markets move fast. Staying competitive as we’ve grown has required constant invention—of new trading strategies, technology, and processes. We’ve found this is easier when you hire humble, kind people. They tend to help each other, and prioritize teamwork over titles.
We invest heavily in teaching and training. There’s a library and a classroom in every office, because deepening your understanding of something is considered real work. Guest lectures, classes, and conferences round out the intellectual exchanges that happen every day.
People grow into long careers at Jane Street because there are always new and interesting problems to solve, systems to build, and theories to test. More than twenty years after our founding, it still feels like we’re just getting started.
