Jane Street
Quantitative Researcher
Eligibility
- Most interns are current undergraduate or graduate students; applicants who have already graduated and consider a career change in finance are also welcome.
An internship for undergraduate or graduate students and recent graduates as Quantitative Researcher, focused on working with experienced researchers on market signals, data analysis, and modeling for trading strategies at Jane Street.
Responsibilities
- Work alongside experienced quantitative researchers to identify market signals, analyze large datasets, build and test models, and create new trading strategies.
- Engage in learning about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets.
- Participate in classes on markets and trading fundamentals, lunch seminars, and activities to understand the trading strategy process from exploration to productionizing signals.
Requirements
- Ability to apply logical and mathematical thinking to various problems.
- Intellectual curiosity and eagerness to ask questions, admit mistakes, and learn new things.
- Strong programming skills, comfortable with Python.
- Open-minded thinker and precise communicator comfortable collaborating across diverse teams.
Skills
- Python
- Data analysis
- Machine learning
- Statistical modeling
Full job description
About the Position
Our goals are to give you a real sense of what it’s like to work as a Quantitative Researcher at Jane Street while also providing a truly unparalleled educational experience. You’ll work side by side with our experienced Quantitative Researchers to learn how we identify market signals, analyze large datasets, build and test models, and create new trading strategies.
At Jane Street, the lines between research, technology, and trading are intentionally blurry, and you'll have access to petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing tens of thousands of high-end GPUs. 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.
You’ll spend the bulk of your internship working closely with full-time researchers on projects drawn from their own work. You’ll gain a better understanding of the diverse array of challenges we consider every day, learning how we think about experiment design, 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 productionizing a signal.
About you
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. 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
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. Research experience is a plus.
If you’d like to learn more, you can read about our interview process and meet some of the team. Learn more about Jane Street’s internship program here.
If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.
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