Machine Learning Researcher - Systematic Commodities Hedge Fund

Moreton Capital Partners is seeking a Machine Learning Researcher to join our team. We are live trading across global commodity futures, supported by an investment process rooted in machine learning. 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 an applied ML specialist with several years of experience building and shipping models. A PhD is a plus, not required. You will work directly with the CIO and sit alongside the quant research team to turn ML ideas into live trading signals. 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 and improving features from price, weather, satellite, cash pricing, 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 Requirements Several years of applied machine learning experience in industry or a similarly production-oriented research environment 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 PhD in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or a related quantitative field 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…