Quant Researcher - Systematic Commodities Hedge Fund at Moreton Capital Partners
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

07 Dec, 25

Salary

220000.0

Posted On

08 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Computer Science, Economics, Research, Numpy, Machine Learning, Pandas, Python

Industry

Information Technology/IT

Description

QUANT RESEARCHER – SYSTEMATIC COMMODITIES HEDGE FUND

Moreton Capital Partners is seeking a talented Quant Researcher 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.

REQUIREMENTS

  • Masters or PhD in either Statistics, Economics, Computer Science.
  • Strong background in machine learning and statistical modelling (tree-based models, regularization, 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.
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.
Loading...