Quantitative Researcher Intern

Point72 is recruiting for Quantitative Researcher Intern in New York. This listing was last seen on the firm's own job board on August 22, 2026.

A multi-manager fund whose academy programmes are the clearest structured entry route on the buy side.

What sets Point72 apart: Academy programmes that take candidates with no prior finance experience. Discretionary and systematic sides hire on different criteria. Cubist is the systematic arm and runs its own quant process.

Related preparation: Quantitative Researcher (QR) Career Roadmap, Machine Learning Quant Career Roadmap, Quant Strategist (Desk Quant) Career Roadmap, Quant Data Scientist (Alternative Data) Roadmap, Quant Research Ops (MLOps) Roadmap and Financial Data Scientist Career Roadmap.

The role, as Point72 describes it, published on August 15, 2024 and reproduced from their job board:

JOB RESPONSIBILITIES

A highly collaborative, fast-growing team, Point72 Internal Alpha Capture (IAC) is developing scalable quantitative equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, a broad range of public and proprietary data sources, and unparalleled computing power.

We are looking for exceptional students to join us as quantitative researcher interns for Fall 2026, Spring 2027 and Summer 2027. Our interns will work closely with our team, receive comprehensive, in-depth training, and help develop novel signals that may have real impact.

You will gain exposure to a variety of activities, which may include:

Conducting full pipeline signal research, from ideation, via implementation and backtesting, to eventual application.

Adapting and extending existing results in the broad field of machine learning; conducting novel research as needed to potentially develop new signals that may enhance portfolio returns.

Analyzing very large data sets to extract features useful for predictive models.

Providing research assistance to the team.

Helping improve the team’s research infrastructure.

DESIRABLE CANDIDATES

Masters or PhD candidates in machine learning, computer science, finance, mathematics, or other quantitative disciplines.