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Point72

Quantitative Researcher

Reposted 8 Days Ago
Easy Apply
In-Office
London, England
Entry level
Easy Apply
In-Office
London, England
Entry level
The Quantitative Researcher will research market anomalies, perform data processing, optimize strategies, and maintain portfolio trading systems.
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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

Top Skills

Machine Learning
Python

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