Quantitative Researcher

About the Programme

Our goal is to give you a real sense of what it’s like to work at Jane Street full time. As an intern, you are paired with full-time employees who act as mentors, collaborating with you on real-world projects we actually need done. Over the course of the summer, you will explore ways to approach and solve exciting problems through fun and challenging classes, interactive sessions, and group discussions — and then you will have the chance to put those lessons to practical use. At the end of the workday, we often host guest speakers and offer a variety of social events to take part in.

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.

About the Position

As a Quantitative Research intern, you’ll work side by side with full-timers to learn how we identify market signals, analyse large datasets, build and test models, and create new trading strategies.

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. We don’t believe in “one-size-fits-all” modelling 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.

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, in over 200 trading venues globally. We utilise 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.

During the programme you’ll focus on two projects, mentored closely by the key stakeholders who’ve worked on them. You may conduct a study of some new or existing dataset, build new tools that support the firm’s research, or consider big-picture questions that we’re still trying to figure out. The problems we work on rarely have clean, definitive answers — and they often require insights from colleagues across the firm with different areas of expertise.

You’ll gain a better understanding of the diverse array of research challenges we consider every day, learning how we think about dataset generation, time series analysis, feature engineering, and model building for financial datasets. Your day-to-day project work will be complemented by classes on the broader fundamentals of markets and trading, lunch seminars, and activities designed to help you understand the entire process of creating a new trading strategy, from initial exploration to finding and productionising a signal.

Most interns are current undergraduate or graduate students, but we also welcome applicants who have already graduated and are considering a new career in finance.

If you’d like to learn more, you can read about our interview process and meet some of our newest hires.

About You

We 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
• Research experience a plus
• Fluent in English

If you’re a recruiting agency and want to partner with us, please reach out to [email protected].