Cubist Systematic Strategies is looking to discover systematic anomalies in equities markets and identify short term opportunities in the high frequency/intraday space.
Requirements
- 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
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
Other
- Help establish a collaborative mindset and shared ownership
- Highly motivated and willing to take ownership of his/her work
- Collaborative mindset with strong independent research ability
- Commitment to the highest ethical standards
