Summary: Moreton Capital Partners seeks a PhD-level Machine Learning Researcher to design and improve predictive models for systematic commodities trading strategies, directly impacting portfolio returns. Highlights: 1. Direct impact: research drives live trading capital 2. Research freedom with fast feedback loops 3. Strong learning curve across ML, markets, and portfolio construction ### **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 **Benefits** * Market leading benefits * High responsibility from day one * Performance bonus tied to firm growth and personal performance (up to 3x salary)