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Salary
$56k – $159k per year (Estimated)
Location
In office (London)
Overview
Company
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Point72 is a global, multi-strategy hedge fund manager founded by Steven A. Cohen that invests across discretionary long/short equity, systematic, and macro strategies. The firm relies heavily on fundamental research, quantitative models, and advanced data analytics to allocate capital across international financial markets. Headquartered in Stamford, Connecticut, it also manages Point72 Ventures, an early-stage venture capital arm focused on investing in emerging technology startups.

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
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