- Location
- New York, Chicago · Chicago, Illinois, United States · New York, New York, United States
- Department
- Quant Management
- Experience
- 3+ years
- Source
- Greenhouse
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
- 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
Skills
PythonAWSLinuxData Science
