We are looking for quantitative researchers to help us build models, strategies, and systems that price and trade financial instruments. Our advanced proprietary trading models are the backbone of our operation, enabling us to identify profitable trading opportunities across hundreds of thousands of financial products daily, in over 200 trading venues globally.
Our success is rooted in a deep understanding of the markets, a collaborative spirit between researchers and traders, and a desire to continuously challenge the status quo. At Jane Street, you will work alongside a tight-knit team, utilizing petabytes of data, our computing cluster with hundreds of thousands of cores, and our growing GPU cluster containing thousands of A/H100s to develop trading strategies in adversarial markets that evolve every day. Signals are small; noise is high.
We don’t believe in “one-size-fits-all” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. Your ability to venture into new and uncharted territories of data analysis, while maintaining a clear focus on the ultimate goal, will be key.
At Jane Street, we blur the lines between trading and research, fostering a fluid environment where teams work in a tight loop to solve complex problems. The most successful researchers will be driven by their curiosity for how their contributions fit into the larger picture of our trading operations, and how to adapt their findings into actionable strategies.
If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind, a collaborative spirit, and a passion for solving interesting problems, we have a feeling you’ll fit right in.
If you’d like to learn more, you can read about our interview process and meet some of our newest hires.
About YouWe don’t expect you to have a background in finance or any other specific field — we’re looking for smart, ambitious people who enjoy solving challenging problems. Most candidates will have experience with data science or machine learning, but ultimately, we’re more interested in how you think and learn than what you currently know. You should be:
- Able to apply logical and mathematical thinking to all kinds of problems
- Intellectually curious — eager to ask questions, admit mistakes, and learn new things
- A strong programmer who’s comfortable with Python
- An open-minded thinker and precise communicator who enjoys collaborating with colleagues from a wide range of backgrounds and areas of expertise
- PhD or other research experience a plus
If you're a recruiting agency and want to partner with us, please reach out to [email protected].
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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.
