As a Quantitative Researcher at Cubist, you will develop systematic trading models in FICC markets, manage research from idea generation to implementation, and enhance production environments. The role involves feature engineering and working with large data sets for intraday to daily trading strategies.
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
Quantitative researcher to help build out a systematic macro (futures, FX, and vol) business. Core focus will be working on short-term to mid-frequency alpha strategies.
RESPONSIBILITIES
- Develop systematic trading models across fixed income, currency and commodity (FICC) markets
- Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
- Perform feature engineering with price-volume, order book, and alternative data for intraday to daily horizons in mid frequency trading space
- Perform feature combination and monetization using various modeling techniques
- Assist in building, maintenance, and continual improvement of production and trading environments coupled with execution monitoring.
- Contribute to the research infrastructure of the team.
REQUIREMENTS
- Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics
- 2-5 years of experience in macro quantitative trading, preferably FICC
- Experience synthesizing predictive signals for both cross-sectional and time-series models driven by statistical/technical, fundamental, and data driven signals
- Ability to efficiently format and manipulate large, raw data sources
- Strong experience with data exploration, dimension reduction, and feature engineering
- Demonstrated proficiency in Python. Familiarly with data science toolkits, such as scikit-learn, Pandas. Experience with machine learning is a plus
- Strong command of foundations of applied and theoretical statistics, linear algebra, and machine learning techniques
- Collaborative mindset with strong independent research abilities
- Commitment to the highest ethical standards
Top Skills
Python
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