Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund

Sobre el puesto

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)