Cubist Quantitative Researcher Intern

Point72 is recruiting for Cubist Quantitative Researcher Intern in Singapore. 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:

ABOUT CUBIST

Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.

ROLE

Quantitative alpha research requires mastery of multiple domains. The best research requires original research ideas, good intuition, strong data analysis skills, and good research planning. Our research directly drives our investment decisions. This role is highly selective. We have a long history of training extraordinarily talented academic researchers to succeed in the investment services industry. Prior experience in the financial services industry is not required.

RESPONSIBILITIES

Provide research assistance to full-time researchers

Assist with data collection and preliminary analysis

Follow, digest, analyze and improve upon the latest academic research

DESIRABLE CANDIDATES

Ph.D. candidates in finance, economics, mathematics, statistics, physics, computer science, or other quantitative discipline.

Programming in any of the following: R, Python, or C++.