Quantitative Researcher Internship, June - September - London
🚀 Off-cycle Internship
AI generated summary
- The ideal candidate should possess strong problem-solving skills, be intellectually curious, have programming experience, be a collaborative communicator, and be open to learning new things. Fluency in English is a must.
- The Quantitative Research intern at Jane Street will work closely with full-timers to analyze market signals, large datasets, and build models, while also participating in classes, seminars, and discussions to gain a deeper understanding of markets and trading.
- Our goal is to give you a real sense of what it’s like to work at Jane Street full time. Over the course of your internship, you will explore ways to approach and solve exciting problems within your field of interest through fun and challenging classes, interactive sessions, and group discussions — and then you will have the chance to put those lessons to practical use.
- 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. When you’re not working on your project, you will have plenty of time to use our office amenities, physical and virtual educational resources, attend guest speakers and social events, and engage with the parts of our work that excite you the most.
- 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.
- We don’t expect you to have a background in finance or any other specific field—we’re looking for smart people who enjoy solving interesting problems. 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—asking great questions is more important than knowing all the answers
- A strong programmer who is interested in learning to write code in OCaml, even if you’ve never done functional programming before
- An open-minded thinker and precise communicator who enjoys interacting with colleagues from a wide range of professional backgrounds and areas of expertise
- Eager to ask questions, admit mistakes, and learn new things
- Fluent in English
Area of Responsibilities
Research & Development
- 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, create new strategies, and write code to implement them.
- 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. During the programme you’ll work on two projects, mentored closely by the full-time employees who designed them. Some projects consider big-picture questions that we’re still trying to figure out, while others involve building new tools or systems.
- Your mentors will work in two distinct areas of research, so you’ll gain a better understanding of the diverse array of research challenges we consider every day, from reasoning about how best to analyse very noisy datasets to building practical models that allow us to better identify market signals. Your day-to-day project work will be complemented by classes on the broader fundamentals of markets and trading, lunch seminars, and real-time discussion on the desk.
- During the second half of the internship, you will also get to participate in one “elective” based on your interests. Electives consist of targeted classes and immersive activities, and are designed to give you a deeper and more nuanced look into one of the many aspects of what trading and research can look like at Jane Street:
- Algorithmic Trading and Market Microstructure
- You’ll learn the end-to-end process of developing an algorithmic trading strategy. Working in small teams, you'll learn how to analyse market data to develop a tradable fair value, implement a trading strategy in Python, and adapt that strategy to different market structures. Teams will compete to have the best performing strategy in several different simulated markets. Through classes and immersive activities, you'll also learn how to backtest your strategy and improve its performance from simulation, and how real-world trading differs from simulation.
- Modeling, Machine Learning, and Data Science
- You'll learn how Jane Street applies advanced machine learning and statistical techniques to model and predict large datasets. Through a series of classes and activities, you will analyse things such as real trading data, simulated marketdata, and prediction markets. You'll gain an understanding of the differences between textbook machine learning and its application to noisy and complex financial data.