This posting is no longer listed by Point72.
hirly last saw it live on 1 September 2026. Similar roles are on the live board.
Point72
Quantitative Researcher
London
Apply through hirly
hirly scores this role against your resume, shows its reasoning, then writes a resume and cover letter for it and fills the application with you. Free to start — no card required.
hirly's read of this role
- Seniority
- Mid level
- Country
- GB
- Work mode
- On-site / unstated
- First seen by hirly
- 1 Sept 2026
Derived automatically from the posting. Sign up to see how the role scores against your own resume.
the posting
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:
A new Cubist portfolio management team specializing in the systematic trading of equities is looking for a Quant Researcher whose core focus will be working on mid-frequency alpha strategies. Joining the team will provide a unique opportunity to be involved with the early stages of a product launch and develop within a growing team.
Responsibilities:
Perform rigorous and innovative research to discover systematic anomalies in the equities market
End-to-end development, including alpha idea generation, data processing, strategy backtesting, optimization, and production implementation
Identify and evaluate new datasets for stock return prediction
Maintain and improve portfolio trading in a production environment
Contribute to the analysis framework for scalable research
Requirements:
MS or PhD in a quantitative discipline
0-2 years of professional work experience
A background in financial markets is not necessary, but an interest in the field is essential
Proven expertise in Python and handling large datasets
Fluency in data science practices, e.g., feature engineering. Experience with machine learning is a plus
Highly motivated, curious, and critical thinker
Collaborative mindset with strong independent research abilities
Commitment to the highest ethical standards
Is this role actually a fit for you?
hirly answers with a score and its reasoning, then writes the resume and cover letter if you decide to go for it.
Score it against my resume