Quantitative Researcher - Macro
Point72 Asset Management - New York
Posted Aug 15, 2024
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Quantitative Researcher - Macro New York 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. 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 Desirable Candidates - 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
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