Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund
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Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund at Moreton Capital Partners: Mexico City, Мексика; На місці; Повна зайнятість. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Mexico City, Мексика, На місці
- Role classification: Повна зайнятість
- Source freshness: checked by JobGrid on 2026-06-01.
- Application path: candidates continue to the employer application page with non-personal referral tags.
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