Our goals are to give you a real sense of what it's like to work as a Quantitative Trader at Jane Street while also providing a truly unparalleled educational experience. You'll be paired with experienced quantitative traders who will teach you how to identify market signals, analyze and execute strategies, construct quantitative models, conduct statistical analysis, and build trading intuition.
At Jane Street, the lines between research, technology, and trading are intentionally blurry, and youâll 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 work closely with two different mentors on projects relating to their day-to-day work, giving you a sense of the variety of problems we solve every day. Past projects have included analyzing new or existing datasets, training predictive models, simulating potential new trading strategies, writing tools that we use in production, and even working to answer big-picture questions we haven't yet figured out.
During the internship, your work is reinforced with intensive classes, workshops, and team-based mock trading sessions. These will expose you to many of the dynamics we observe in real markets, illustrate the role that we play in making markets more efficient, and help build intuition for how we think about both trading and collaborating.
As a quantitative trading intern, you'll also have the opportunity 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 quantitative trading can look like at Jane Street:
Machine Learning, Modeling, and Data Science
You'll learn how Jane Street applies advanced machine learning and statistical techniques to make models and predictions using large datasets of both real and simulated market data. You'll learn how to train and use a variety of ML models, and gain an understanding of the differences between textbook machine learning and its application to noisy and complex financial data.
Algorithmic Trading and Market Microstructure
You'll learn the end-to-end process of developing an algorithmic trading strategy. You'll analyze market data to develop a tradable fair value and implement a trading strategy in Python. Your algorithmic strategy will connect directly to simulated markets with different market structures, and you will learn how to optimize your strategy given the unique attributes of each market. You will discover how various market dynamics affect strategy behavior and learn how real-world trading differs from simulation.
Trading Strategy and Scenarios
Youâll be introduced to a rotating set of new trading scenarios inspired by real events on a particular trading desk. Youâll work in teams on multiple mock trading sessions related to each scenario and use the time between sessions to refine your strategies, write recaps, and hear how the story played out in real life from our seasoned full-time traders who lived through it.
If youâve never thought about a career in finance, youâre 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 youâll fit right in. Weâre more interested in how you think and learn than what you currently know. You should be:
A profitable trading strategy is only as strong as the technology it runs on, and we consider ourselves as much a technology company as a trading firm. General programming experience is a plus, but knowing a particular programming language is not required.
If youâd like to learn more, you can read about our interview process and meet some of the team. Learn more about Jane Streetâs internship program here.