Quantitative Researcher
London
Point72
We invest in Discretionary Long/Short, Macro, and Systematic strategies. Weβre inventing the future of finance by revolutionizing how we develop our people and how we use data to shape our thinking. Join our team to innovate, experiment, and be...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
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index π°
Tags: Computer Science Economics Engineering Feature engineering Finance Linear algebra Machine Learning Mathematics Pandas Python Research Scikit-learn Statistics Trading Strategies
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