Janestreet

Janestreet

Quantitative Trader

New York, New York, United States · Internship

Sponsorship not specifiedDetected 7 days ago
Machine LearningDeep LearningData ScienceStatisticsResearch

About the role

  • About the Position 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 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.
  • During the internship, your work is reinforced with intensive classes, workshops, and team-based mock trading sessions.

Responsibilities

  • 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.

Requirements

  • 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.

Nice to have

  • A strong quantitative thinker (no specific degree or major is required)
  • A clear and effective verbal and written communicator
  • Someone who enjoys working collaboratively on a team
  • Eager to ask questions, admit mistakes, and learn new things

Skills

  • About the Position

Benefits

  • 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.

This listing is sourced directly from Janestreet's careers page and normalized into a canonical job model.