Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund

Machine Learning Researcher (PhD) – Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a Machine Learning Researcher to help design and improve the predictive models that power our systematic commodities trading strategies.

We trade global commodity futures using machine learning, alternative data, and institutional-grade portfolio construction. Our edge comes from research depth, disciplined experimentation, and robust production systems.

This role is for candidates completing or having recently completed a PhD with a strong machine learning, statistics, or applied mathematics focus who want to apply advanced research in a real capital environment.

You will work directly with the CIO and quant research team to turn cutting-edge ML ideas into live trading signals.

This is not a purely academic role.
Your research will ship to production and directly impact portfolio returns.

What you will work on

• Designing predictive models for cross-sectional and time-series commodity returns
• Developing new features from price, positioning, options, macro, and alternative datasets
• Improving signal robustness and reducing overfitting through rigorous validation
• Combining and blending multiple models into portfolio-level forecasts
• Regime detection, meta-models, and adaptive allocation frameworks
• Model diagnostics, explainability, and stability analysis
• Translating research ideas into production-ready implementations
• Collaborating with engineers to deploy models into live trading systems
  

Key Responsibilities

• Formulate research hypotheses and test them using clean, time-aware ML pipelines
• Build and evaluate models (tree-based, linear, ensemble, deep learning, etc.)
• Run walk-forward and out-of-sample experiments with realistic costs
• Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns
• Design feature engineering frameworks and reusable research tooling
• Document findings clearly and communicate results to portfolio managers

• Contribute to improving research standards, reproducibility, and processes

Requirements

• PhD (completed or near completion) in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or related quantitative field

• Strong Python skills and experience with scientific computing stacks
• Deep understanding of statistical learning and model validation
• Experience working with large datasets and experimental pipelines
• Ability to move from theory to practical implementation
• Intellectual curiosity and strong problem-solving mindset
• Comfortable working in a fast-paced, high-ownership environment
  

Bonus Points For

• Experience with financial markets or systematic trading
• Familiarity with time-series modelling or forecasting
• Experience with LightGBM/XGBoost, deep learning, or ensemble methods
• Exposure to portfolio construction or risk modelling
• Experience with cloud or distributed compute environments
• Published research or strong applied projects
  

Why this role is unique

• Direct impact: your research drives live trading capital
• Research freedom: explore ideas with fast feedback loops
• Real-world data: large, messy, multi-source datasets
• Small team: high ownership and rapid iteration
• Strong learning curve across ML, markets, and portfolio construction
• Clear path into Senior Researcher or Portfolio Manager responsibilities

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