Quantitative Researcher
Quick Summary
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.
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…
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…
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.
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
~1 min read- →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
~1 min read- 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
Location & Eligibility
Listing Details
- First seen
- March 26, 2026
- Last seen
- May 10, 2026
Posting Health
- Days active
- 45
- Repost count
- 0
- Trust Level
- 31%
- Scored at
- May 10, 2026
Signal breakdown
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