Quant Researcher

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.