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Quant Researcher Intern

hace 2 días

Ciudad de México, Ciudad de México Moreton Capital Partners A tiempo completo
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.

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.

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.

Benefits

  • Research-first culture: We value deep thinking, novel approaches, and systematic rigor.
  • Close collaboration across a global team.
  • Learning curve: Deep exposure to commodity markets, ML research workflows, and institutional-grade trading systems.