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
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Research, prototype, and validate systematic trading signals across commodities using advanced ML methods.
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Design and implement rigorous backtests with realistic frictions, walk-forward validation, and robust statistical tests.
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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.
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Blend multiple alpha forecasts into meta-models and portfolio signals, leveraging ensemble and Bayesian methods.
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Develop portfolio construction and optimization techniques and analysis tools to be able to enhance performance and track effects on portfolio execution.
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Collaborate with developers to transition research into production-ready strategies.
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Monitor live performance, attribution, and model drift, ensuring continual improvement of the alpha library.
Requirements
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Masters or PhD in either Statistics, Economics, Computer Science.
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Strong background in machine learning and statistical modelling (tree-based models, regularization, time-series ML).
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Proficiency in Python (pandas, NumPy, scikit-learn, XGboost, PyTorch/TensorFlow).
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Understanding of time-series forecasting, cross-validation techniques, and avoiding look-ahead bias.
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Academic experience in research and proven ability to translate academic work to production code.
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Prior exposure to systematic trading or financial modelling.
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Ability to design experiments, interpret results, and iterate quickly in a research environment.
Bonus points for:
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Knowledge of commodities (agriculture, energy) or macro markets.
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Experience with feature engineering on non-traditional datasets (weather, satellite).
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Experience collaborating in version control environments.
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Familiarity with portfolio optimization, risk parity, or Bayesian model averaging.
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Publications, Kaggle competitions, or research track record demonstrating applied ML excellence.
Benefits
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Direct impact: Your alphas will go live into production portfolios, with real capital behind them.
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Research-first culture: We value deep thinking, novel approaches, and systematic rigor.
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Close collaboration across a global team.
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Career growth: Clear trajectory to senior researcher roles as we scale AUM and expand product lines.
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Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows.
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Positive, inclusive and encouraging work environment.