Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund
About The Position
Moreton Capital Partners is a rapidly expanding systematic commodities hedge fund seeking a talented Machine Learning Researcher. This role involves designing and improving predictive models for systematic commodities trading strategies in their Mexico City Office. The company trades global commodity futures using machine learning, alternative data, and institutional-grade portfolio construction, deriving its edge from research depth, disciplined experimentation, and robust production systems. This position is ideal for candidates completing or having recently completed a PhD with a strong focus in machine learning, statistics, or applied mathematics, who are eager to apply advanced research in a real capital environment. The successful candidate will work directly with the CIO and a world-class international quant research team to translate cutting-edge ML ideas into live trading signals, directly impacting portfolio returns. This is a hands-on role, not purely academic, requiring an immediate start. In return, Moreton Capital Partners offers a competitive salary, substantial performance share, comprehensive benefits, an incredible work environment, and a relocation package.
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
Nice To Haves
- 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
Responsibilities
- 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
- 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
Benefits
- Competitive salary
- Substantial performance share
- Comprehensive benefits
- Incredible work environment
- Relocation package to make the move seamless
- Highly competitive base salary and annual bonus that scales as the business grows
- Competitive benefits offering that includes health and life insurance, a year-end bonus, and generous paid time off
- Positive, inclusive and encouraging work environment
- Close collaboration across a global team
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
