Point72
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
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