Quantitative Researcher

AI overview

Contribute to the development of innovative systematic trading strategies by performing applied research and enhancing existing methods using data-driven insights.

ABOUT CUBIST

Cubist Systematic Strategies is one of the world’s premier investment firms. The firm 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.

RESPONSIBILITIES

  • Perform rigorous applied research to discover systematic anomalies in equities markets
  • Present actionable trading ideas and enhance existing strategies
  • Identify short term opportunities in the high frequency/intraday space
  • Participate in end-to-end development (i.e. data orchestration, alpha idea generation, simulation, strategy implementation, and performance evaluation)
  • Contribute towards the team’s research tooling and its efficiency
  • Help establish a collaborative mindset and shared ownership

REQUIREMENTS

  • Bachelor’s degree or higher in mathematics, statistics, computer science, or similar quantitative discipline
  • 3+ years of work experience in systematic alpha research in equities using high frequency/intraday data
  • Fluency in data science practices, e.g., feature engineering, signal combining
  • Technically comfortable handling large datasets
  • Comfortable coding in both C++ and Python in a Linux environment
  • Exposure working with cloud computing platforms such as AWS
  • Highly motivated and willing to take ownership of his/her work
  • Collaborative mindset with strong independent research ability
  • Commitment to the highest ethical standards

 

Point72 Asset Management, led by Steven Cohen, is a global firm specializing in diverse asset classes and strategies, prioritizing superior returns and ethical standards through innovative talent development and data-driven decision-making.

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