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:
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Building components for both live trading and simulation
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Refining and increasing automation and robustness of the research infrastructure including alpha estimation, risk modeling, and backtesting components
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Building tools for signal blending, simulation, portfolio construction, the research framework, and dashboards
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Maintaining and updating the platform, ensuring its stability, robustness, and security
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Developing robust data checking and storage procedures
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Troubleshooting and resolving any systems related issues and handle the release of code fixes and enhancements
REQUIREMENTS:
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Bachelor’s degree or higher in computer science or other STEM discipline
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Advanced proficiency in Python and its ecosystem (numpy, pandas, polars, scikit-learn), with an understanding of Python and library internals
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Experience contributing to core Python numerical libraries is a huge plus (numpy, tensorflow, torch, jax)
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Proficiency with Linux
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Hands-on experience with software architecture and engineering best practices (testing, CI/CD, monitoring, profiling, version control)
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Knowledge of one or more of: distributed systems, databases, ML engineering, AWS
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Strong quantitative and analytical skills; command of linear algebra, statistics, and machine learning would be helpful
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Proficiency with C/C++ is a plus
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Knowledge of financial markets is a plus
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Commitment to the highest ethical standards
The annual base salary range for this role is $150,000-$250,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.