Quant Researcher - Systematic Commodities Hedge Fund

Sobre el puesto

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.