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