🚀 Internship

Quantitative Trader Internship, June - September

Jane Street

Oct 18

🚀 Off-cycle

London

Rolling basis

Description

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.

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Area of Responsibilities

Banking & Finance

Responsibilities

As a Quantitative Trading intern, you’ll be part of an immersive experience, paired with experienced Traders who will teach you how to identify market signals, analyse and execute strategies, construct quantitative models, conduct statistical analysis, and build trading intuition. You'll work on projects alongside mentors in two different areas of trading, giving you a sense of the variety of problems we solve every day. In the past, these projects have included conducting studies on new or existing datasets, building quantitative models, writing tools, and even considering big-picture questions that we're still trying to figure out.Throughout the summer, you'll also participate in dozens of simulated interactive 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.This work is reinforced with intensive classes on the broader fundamentals of markets and trading, workshops on various tools we use, and interactive lunch seminars with senior Traders.  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 MicrostructureYou’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 back test your strategy and improve its performance from simulation, and how real-world trading differs from simulation. Trading Strategy and ScenariosEach week, you'll be introduced to a new trading scenario inspired by real events on a particular trading desk. You’ll work in teams on multiple mock trading sessions related to each scenario and use the time between sessions to refine your strategy, write recaps, and hear how the story played out in real life from our seasoned full-time Traders who lived through it. Modeling, Machine Learning, and Data ScienceYou'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 market data, 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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Requirements

•  A strong quantitative thinker who enjoys working collaboratively on a team •  Eager to ask questions, admit mistakes, and learn new things•  Fluent in English
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