As a Quantitative Research intern, you’ll work side by side with full-timers to learn how we identify market signals, analyze 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 program 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 analyze 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:You’ll learn the end-to-end process of developing an algorithmic trading strategy. Working in small teams, you'll learn how to analyze 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. 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 analyze 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.
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London