Quantitative Developer

JOB RESPONSIBILITIES A highly collaborative, fast-growing team, Point72 Internal Alpha Capture (IAC) is developing scalable quantitative equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, a broad range of public and proprietary data sources, and unparalleled computing power. You will gain exposure to a variety of activities, which may include: Developing automated equities trading applications and real-time analytics Optimizing data ETL pipelines, persistent / in-memory caches and SQL databases Work alongside quant researchers developing quant signals. Improving signal research and trading tools infrastructure DESIRABLE CANDIDATES Undergraduate or higher degree in computer science, machine learning, or other quantitative discipline 5+ years of experience at a hedge fund, major investment bank, or quantitative trading firm Strong programming experience in Python with production-level coding quality Experience with containerization and orchestration technologies, particularly Docker Experience with Unix environment and shell scripting Knowledge of profiling (e.g. VisualVM, async-profiler) and code optimization is a plus Knowledge of statistics and machine learning is a plus Willing to take ownership of his/her work, working both independently and within a small team Prior experience in the financial services industry is a plus Commitment to the highest ethical standards

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. Role: Cubist is looking for a support engineer/quantitative developer who will work on a growing L2/L3 support team for quantitative trading systems, high‑frequency trading infrastructure, real time analytics, and research infrastructure. This role will require close collaboration with multiple teams at Cubist, including multiple Portfolio Manager (PM) teams, Central Research Technology, and Execution Services. The role offers career growth through the opportunity to work on the development of the underlying systems. Role/Responsibilities: Troubleshooting/resolution of quantitative trading systems and other live analytics processes System monitoring, including development of better processes and tools New feature development in Python/C++ Configuration and change management Onboarding assistance Requirements: 2+ years of professional support and/or software engineering experience Experience with trading systems support (co-located low-latency systems a strong plus) Fluency with C++, Python and SQL Familiarity with software delivery lifecycle and production deployment Cloud experience is a strong plus Team player and strong communicator

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. ROLE The candidate will be responsible for some or all of parts of the research pipeline—including data processing, feature design, model training, portfolio construction and management, back-testing, and performance analysis. Successful researchers combine statistical analysis, machine learning techniques, intense passion, and curiosity to decipher the market, and aspire to gain great intellectual understanding and financial outperformance. RESPONSIBILITIES Finding alphas in global equity markets by applying rigorous statistical analysis on technical data or alternative data sets. Performing hypotheses testing, feature design, and backtesting to improve on alpha ideas. Maintaining and improving the research pipeline, including alpha generation, portfolio construction, back-testing, and monetization. Maintaining and improving portfolio trading in production environments. REQUIREMENTS Bachelors degree or higher in mathematics, statistics, computer science, or other quantitative discipline. 2+ years of experience in quantitative research. Experience in medium frequency equity research is a plus. Strong analytical and quantitative skills; solid knowledge in statistics, linear algebra, or machine learning. Proficiency in Python. Familiarity with scientific toolkits, such as Numpy and Pandas. Ability to work both independently and collaboratively within a team. Commitment to the highest ethical standards.