Description 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 integral 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 Contri