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Quant Researcher - Systematic Commodities Hedge Fund at Moreton Capital Partners. 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 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.. . 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, metals) or macro markets.. . Experience with feature engineering on non-traditional datasets (options positioning, 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.. . Company Location: Canada.