Quantitative Researcher

Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics

2-6 years of signal research experience in macro trading as part of a trading team

Specialization in swaps, fixed income, or commodities trading a plus.

Prior professional experience with feature engineering, modeling, or monetization

Ability to efficiently format and manipulate large, raw data sources

Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas

Perform rigorous and innovative research to discover systematic anomalies in global macro markets (futures, FX, etc.)

Perform feature engineering with price-volume, order book and alternative data at intraday to daily horizons in mid frequency trading space

Perform feature combination and monetization using various modeling techniques

Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation

Maintain and improve portfolio trading in a production environment

Contribute to the analysis framework for scalable research

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.