About the role
Quantitative Software Developer at Point72 in New York. Posted June 10, 2025.
ABOUT CUBIST:
Cubist Systematic Strategies, an affiliate of Point72, 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:
- Building components for both live trading and simulation
- Refining and increasing automation and robustness of the research infrastructure including alpha estimation, risk modeling, and backtesting components
- Building tools for signal blending, simulation, portfolio construction, the research framework, and dashboards
- Maintaining and updating the platform, ensuring its stability, robustness, and security
- Developing robust data checking and storage procedures
- Troubleshooting and resolving any systems related issues and handle the release of code fixes and enhancements
REQUIREMENTS:
- Bachelor’s degree or higher in computer science or other STEM discipline
- Advanced proficiency in Python and its ecosystem (numpy, pandas, polars, scikit-learn), with an understanding of Python and library internals
- Experience contributing to core Python numerical libraries is a huge plus (numpy, tensorflow, torch, jax)
- Proficiency with Linux
- Hands-on experience with software architecture and engineering best practices (testing, CI/CD, monitoring, profiling, version control)
- Strong quantitative and analytical skills; command of linear algebra, statistics, and machine learning would be helpful
- Proficiency with C/C++ is a plus
- Experience with designing and implementing trading systems is a plus
- Commitment to the highest ethical standards
Prefer to apply yourself? Apply on Point72’s site
Quick answers
Where is this job based?
New York.
How do you apply?
Apply on Point72’s site using the link on this page. With a free Jobmagic account you can see how well you fit first.
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