Quant Researcher Intern - Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a talented Quant Researcher Intern 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.

• 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.

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 optimisation, risk parity, or Bayesian model averaging.
• Publications, Kaggle competitions, or research track record demonstrating applied ML excellence.

Requirements

• Bachelors degree in either Statistics, Economics, Computer Science.
• Strong background in machine learning and statistical modelling (tree-based models, regularisation, 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.

Benefits

• Research-first culture: We value deep thinking, novel approaches, and systematic rigor.
• Direct exposure: Work alongside the CIO and senior researchers, with a direct line to decision-making.
• Learning curve: Deep exposure to commodity markets, ML research workflows, and institutional-grade trading systems.
• Close collaboration: Work alongside the CIO, Head of Quant Research, and Developers in a lean, highly motivated team.