Quant Researcher - Systematic Commodities Hedge Fund
Moreton Capital Partners · Mexico City
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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