Quantitative Researcher - Macro

Skills this role screens for

About the role

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) strategies. Core focus will be working on mid-frequency alpha strategies.

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Job Description

• Develop systematic trading models across FX, commodities, fixed income, and equity markets
• Alpha idea generation, backtesting, and implementation
• Assist in building, maintenance, and continual improvement of production and trading environments
• Evaluate new datasets for alpha potential
• Improve existing strategies and portfolio optimization
• Execution monitoring
• Be a core contributor to growing the investment process and research infrastructure of the team

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Desirable Candidates

The annual base salary range for this role is $150,000-$200,000 (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.

• Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus
• Experience in quantitative trading, ideally in FX or futures
• Experience with alpha research, portfolio construction and optimization
• Experience building statistical/technical, fundamental, and data driven signals
• Experience synthesizing predictive signals for both cross-sectional and time-series models
• Strong experience with data exploration, dimension reduction, and feature engineering
• Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity
• Experience managing and running risk is a strong plus
• Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc.
• Creative mindset
• Strong time management ability—the ability to manage multiple tasks and deadlines in a fast-paced environment
• High degree of drive and energy—must be a self-starter
• Ability to work cooperatively with all levels of staff and to thrive in a team-oriented environment
• Commitment to the highest ethical standards and who act with professionalism and integrity at all times