Quant Researcher - 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

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine learning, Statistical modeling, Time-series forecasting, Pandas, NumPy, Scikit-learn, XGboost, PyTorch, TensorFlow, Backtesting, Portfolio optimization, Feature engineering, Bayesian methods, Commodity markets, Quantitative research

Industry

Investment Management

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. 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. 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. 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 optimization, risk parity, or Bayesian model averaging. Publications, Kaggle competitions, or research track record demonstrating applied ML excellence. Direct impact: Your alphas will go live into production portfolios, with real capital behind them. Research-first culture: We value deep thinking, novel approaches, and systematic rigor. Close collaboration across a global team. Career growth: Clear trajectory to senior researcher roles as we scale AUM and expand product lines. Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows. Positive, inclusive and encouraging work environment.

Responsibilities

The researcher will design, prototype, and validate systematic trading signals for commodity futures using advanced machine learning techniques. They will also collaborate with developers to transition research into production-ready strategies and monitor live portfolio performance.