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
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Designing predictive models for cross-sectional and time-series commodity returns
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Developing new features from price, positioning, options, macro, and alternative datasets
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Improving signal robustness and reducing overfitting through rigorous validation
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Combining and blending multiple models into portfolio-level forecasts
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Regime detection, meta-models, and adaptive allocation frameworks
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Model diagnostics, explainability, and stability analysis
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Translating research ideas into production-ready implementations
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Collaborating with engineers to deploy models into live trading systems
Key Responsibilities
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Formulate research hypotheses and test them using clean, time-aware ML pipelines
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Build and evaluate models (tree-based, linear, ensemble, deep learning, etc.)
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Run walk-forward and out-of-sample experiments with realistic costs
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Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns
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Design feature engineering frameworks and reusable research tooling
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Document findings clearly and communicate results to portfolio managers
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Contribute to improving research standards, reproducibility, and processes
Requirements
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PhD (completed or near completion) in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or related quantitative field
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Strong Python skills and experience with scientific computing stacks
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Deep understanding of statistical learning and model validation
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Experience working with large datasets and experimental pipelines
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Ability to move from theory to practical implementation
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Intellectual curiosity and strong problem-solving mindset
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Comfortable working in a fast-paced, high-ownership environment
Bonus Points For
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Experience with financial markets or systematic trading
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Familiarity with time-series modelling or forecasting
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Experience with LightGBM/XGBoost, deep learning, or ensemble methods
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Exposure to portfolio construction or risk modelling
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Experience with cloud or distributed compute environments
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Published research or strong applied projects
Why this role is unique
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Direct impact: your research drives live trading capital
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Research freedom: explore ideas with fast feedback loops
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Real-world data: large, messy, multi-source datasets
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Small team: high ownership and rapid iteration
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Strong learning curve across ML, markets, and portfolio construction
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Clear path into Senior Researcher or Portfolio Manager responsibilities
Benefits
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Market leading benefits
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High responsibility from day one
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Performance bonus tied to firm growth and personal performance (up to 3x salary)
