*quant researcher intern - systematic commodities hedge fund*:
moreton capital partners is seeking a talented quant researcher intern to help build the next generation of alpha signals in commodity futures. Our research is grounded in advanced machine learning, robust testing frameworks, and a deep understanding of global commodity markets.
This role is central to our mission: you’ll take ownership of designing, testing, and refining predictive models that directly feed into live trading portfolios.
*key responsibilities*:
- research, prototype, and validate systematic trading signals across commodities using advanced ml methods.
- design and implement rigorous backtests with realistic frictions, walk-forward validation, and robust statistical tests.
- engineer and evaluate novel features from prices, fundamentals, positioning, options data, and alternative datasets (e.g., satellite, weather and global commodity cash pricing).
- blend multiple alpha forecasts into meta-models and portfolio signals, leveraging ensemble and bayesian methods.
- develop portfolio construction and optimization techniques and analysis tools to be able to enhance performance and track effects on portfolio execution.
- collaborate with developers to transition research into production-ready strategies.
Monitor live performance, attribution, and model drift, ensuring continual improvement of the alpha library.
Bonus points for:
- knowledge of commodities (agriculture, energy, metals) or macro markets.
- experience with feature engineering on non-traditional datasets (options positioning, weather, satellite).
- experience collaborating in version control environments.
- familiarity with portfolio optimisation, risk parity, or bayesian model averaging.
- publications, kaggle competitions, or research track record demonstrating applied ml excellence.
*requirements*:
- bachelors degree in either statistics, economics, computer science.
- strong background in machine learning and statistical modelling (tree-based models, regularisation, time-series ml).
- proficiency in python (pandas, numpy, scikit-learn, xgboost, pytorch/tensorflow).
- understanding of time-series forecasting, cross-validation techniques, and avoiding look-ahead bias.
- academic experience in research and proven ability to translate academic work to production code.
- prior exposure to systematic trading or financial modelling.
- ability to design experiments, interpret results, and iterate quickly in a research environment.
*benefits*
- research-first culture: we value deep thinking, novel approaches, and systematic rigor.
- direct exposure: work alongside the cio and senior researchers, with a direct line to decision-making.
- learning curve: deep exposure to commodity markets, ml research workflows, and institutional-grade trading systems.
- close collaboration: work alongside the cio, head of quant research, and developers in a lean, highly motivated team.