Stellenticket Freie Universität Berlin

Financial services

About You
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:

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

About the Programme
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, guest speakers, and group discussions.

You will also have the chance to put those lessons to practical use, pairing up with full-time Jane Street employees who act as mentors and collaborate with you on real-world projects we actually need done.

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.

About the Position
As a Quantitative Trading Intern, you'll be 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 Microstructure

You'll learn the end-to-end process of developing an algorithmic trading strategy. Whilst being mentored by full-time traders, you'll analyse 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 optimise your strategy given the unique attributes of each market. Through classes and immersive activities, you'll explore how various market dynamics affect strategy behaviour and learn how real-world trading differs from simulation.

Trading Strategy and Scenarios

You'll be introduced to new trading scenarios 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 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 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.

To apply, please visit our website: https://www.janestreet.com/join-jane-street/position/7545296002/