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Open nowPosted 36 days ago

Quant Researcher - Systematic Commodities Hedge Fund

Workable (global search)108,016 open roles

Where
Mexico City, CDMX, Mexico
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Your applicationOpen nowQuant Researcher - Systematic Commodities Hedge FundWorkable (global search) · Mexico City, CDMX, Mexico

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Share of postings closed within
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  2. 3.6%3 days
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  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 36 days ago

Workable (global search) median: 7 days open

The posting

Quant Researcher – Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a talented Quant Researcher to help build the next generation of alpha signals in commodity futures. Our research is grounded in advanced machine learning, robust testing frameworks, and a deep understanding of global commodity markets.

This role is central to our mission: you’ll take ownership of designing, testing, and refining predictive models that directly feed into live trading portfolios.

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, test, and maintain features from prices, fundamentals, positioning, and alternative datasets (e.g., satellite, weather and global commodity cash pricing). Feature work is a core part of this role.
  • 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.

Requirements

  • Masters or PhD in either Statistics, Economics, Computer Science.
  • Strong background in machine learning and statistical modelling (tree-based models, regularization, time-series ML).
  • Proficiency in Python (pandas, NumPy, scikit-learn, XGboost, PyTorch/TensorFlow).
  • Understanding of time-series forecasting, cross-validation techniques, and avoiding look-ahead bias.
  • Academic experience in research and proven ability to translate academic work to production code.
  • Prior exposure to systematic trading or financial modelling.
  • Ability to design experiments, interpret results, and iterate quickly in a research environment.

Bonus points for:

  • Knowledge of commodities (agriculture, energy) or macro markets.
  • Experience with feature engineering on non-traditional datasets (weather, satellite).
  • Experience collaborating in version control environments.
  • Familiarity with portfolio optimization, risk parity, or Bayesian model averaging.
  • Publications, Kaggle competitions, or research track record demonstrating applied ML excellence.

Benefits

  • Direct impact: Your alphas will go live into production portfolios, with real capital behind them.
  • Research-first culture: We value deep thinking, novel approaches, and systematic rigor.
  • Close collaboration across a global team.
  • Career growth: Clear trajectory to senior researcher roles as we scale AUM and expand product lines.
  • Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows.
  • Positive, inclusive and encouraging work environment.
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