Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund at Moreton Capital Partners

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

20 Jul, 26

Salary

0.0

Posted On

21 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine learning, Python, Statistics, Applied mathematics, Time-series modeling, Predictive modeling, Feature engineering, Deep learning, Scientific computing, Data analysis, Financial markets, Systematic trading, Portfolio construction, Risk modeling, LightGBM, XGBoost

Industry

Investment Management

Description

Immediate Start - Machine Learning Researcher (PhD) – Systematic Commodities Hedge Fund Moreton Capital Partners is rapidly expanding and seeking a talented Machine Learning Researcher to help design and improve the predictive models that power our systematic commodities trading strategies in our Mexico City Office. 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 sit alongside a world class international quant research team to turn cutting-edge ML ideas into live trading signals. Your research will ship to production and directly impact portfolio returns. This is not a purely academic role. We're looking for someone ready to hit the ground running and available to start immediately. In return, we offer a competitive salary, substantial performance share, comprehensive benefits, incredible work environment and a relocation package to make the move seamless. 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 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 Market leading benefits High responsibility from day one Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows. Relocation package to our Mexico City office, along with a 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.

Responsibilities

The researcher will design and implement predictive models for commodity trading strategies using machine learning and alternative data. They will collaborate with engineers to deploy these models into live production systems and analyze their performance.