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Point72
Verified live 16h ago

Quantitative Researcher - Macro

Brief overview

New York, New York, United StatesIn-person
MastersOr in progress
$150k–$200k/yrStated range
135 H-1B approvalsDept. of Labor
43 green cardsCertified filings
Pythonnumpypandasscikit-learnmachine learningdata explorationdimension reductionfeature engineeringregression techniquesOLSMLSRidgeLassoBayesian inferencehandling auto-correlationhandling heteroskedasticityportfolio optimization

About the company

Point72 invests in multiple asset classes and strategies worldwide.

Visa sponsorship history

4 years sponsoring, last filed FY2026

Data powered by U.S. Department of Labor. This does not guarantee sponsorship for this specific role.
135H-1B approved
99%approval rate
30new H-1B hires
43PERM certified
$222,500median wage / yr
H-1B Petition ApprovalsVisas USCIS actually granted: the strongest sign the company sponsors.
202361
202472
20252
LCA Certified ApplicationsAn early filing step, not a visa approval: it signals intent, not confirmed sponsorship.
202324
202417
202517
202625
Green Card (PERM) FilingsCertified green card filings: a long-term commitment to international hires.
202310
202412
202518
20263
Top sponsored roles
Quantitative Software DeveloperQuantitative Strategist, Macro TechnologyData AnalystIT Operations Engineer, Application SupportQuantitative Strategist, Treasury Quant Strategy
Sponsored employees from
ChinaIndiaUnited KingdomMalaysiaGermany

Job description

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 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

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.

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