Quantitative Researcher Internship, June-September

Quantitative Researcher Internship, June-September

Research real market signals and financial datasets using experiment design, time-series analysis, feature engineering, Python, statistical modelling, and machine learning.

Jane Street’s Quantitative Research internship puts interns alongside full-time researchers on projects taken from the firm’s real research pipeline. The goal is to expose you to the full process of finding a useful market signal, rather than giving you an isolated academic exercise.

What you will work on

• Searching for market signals in large financial datasets.
• Designing experiments and generating or cleaning datasets for research.
• Performing time-series analysis and feature engineering.
• Building, testing, and comparing statistical or machine-learning models.
• Developing ideas that can move from initial exploration toward a production trading strategy.

The research environment is tightly connected to both trading and engineering. Jane Street gives researchers access to petabytes of data, very large CPU compute clusters, and a large GPU cluster, and it does not impose one preferred modelling framework. Depending on the problem, researchers may use anything from linear methods to deep learning.

Most of the internship is spent on project work with experienced researchers. That is supplemented by classes on markets and trading, lunch seminars, and activities explaining how Jane Street takes a research idea from early exploration through signal discovery and productionisation.

What Jane Street looks for

You should be able to apply mathematical and logical reasoning broadly, communicate precisely, collaborate well, and program comfortably in Python. Most interns are undergraduate or graduate students, although Jane Street also considers graduates moving into finance. Previous research experience is useful but not mandatory.

Details

Role information

Location London, United Kingdom

Job type Internship

Category Quant Research

Company

New York, United States

Global quantitative trading and technology firm known for mathematical problem-solving, market making, and collaborative research-driven trading.

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