Overview
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
Full job description
Full Job Description
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
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Perform rigorous applied research to discover systematic anomalies in equities markets
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Present actionable trading ideas and enhance existing strategies
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Identify short term opportunities in the high frequency/intraday space
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Participate in end-to-end development (i.e. data orchestration, alpha idea generation, simulation, strategy implementation, and performance evaluation)
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Contribute towards the team’s research tooling and its efficiency
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Help establish a collaborative mindset and shared ownership
REQUIREMENTS
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Bachelor’s degree or higher in mathematics, statistics, computer science, or similar quantitative discipline
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3+ years of work experience in systematic alpha research in equities using high frequency/intraday data
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Fluency in data science practices, e.g., feature engineering, signal combining
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Technically comfortable handling large datasets
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Comfortable coding in both C++ and Python in a Linux environment
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Exposure working with cloud computing platforms such as AWS
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Highly motivated and willing to take ownership of his/her work
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Collaborative mindset with strong independent research ability
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Commitment to the highest ethical standards
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