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Immediate Start - Quant Researcher - Systematic Commodities Hedge Fund

Moreton Capital Partners
CompanyMoreton Capital Partners
CategoryScience & Research
LocationToronto
RemoteOn-site (inferred)
EmploymentFull-time
LevelMid
SalaryNot stated by the employer
Posted21 Apr 2026
Last verified8 Aug 2026
SourceEmployer ATS (workable)
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Description
Immediate Start - Quant Researcher – Systematic Commodities Hedge Fund Moreton Capital Partners is rapidly expanding and seeking a talented Quantitative Researcher to join us in our Mexico City office to sit alongside a world class, international team. This is a high-impact role from day one. You'll take full ownership of designing, testing, and refining the next generation of alpha signals in commodity futures, with your models feeding directly into live trading portfolios. Our research is grounded in advanced machine learning, robust testing frameworks, and deep expertise across global commodity markets. We're looking for someone ready to hit the ground running and available to start immediately. In return, we offer a competitive salary, substantial performance share, comprehensive benefits, incredible work environment and a relocation package to make the move seamless. Key Responsibilities Research, prototype, and validate systematic trading signals across commodities using advanced ML methods. Design and implement rigorous backtests with realistic frictions, walk-forward validation, and robust statistical tests. Engineer and evaluate novel features from prices, fundamentals, positioning, options data, and alternative datasets (e.g., satellite, weather and global commodity cash pricing). Blend multiple alpha forecasts into meta-models and portfolio signals, leveraging ensemble and Bayesian methods. Develop portfolio construction and optimization techniques and analysis tools to be able to enhance performance and track effects on portfolio execution. Collaborate with developers to transition research into production-ready strategies. Monitor live performance, attribution, and model drift, ensuring continual improvement of the alpha library.