Quant Researcher, Systematic Commodities Alpha

Moreton Capital Partners; Ciudad de México; Presencial; MXN 900.000 - 1.300.000; Jornada completa

Descripción de la vacante

Moreton Capital Partners seeks a Quant Researcher to design, test, and refine predictive models for commodity futures in a production-ready research environment. You’ll build ML signals, run backtests, and feed live trading portfolios.

The ideal candidate has a strong ML and time-series background, plus a track record translating academic work into robust production code. Masters or PhD in statistics, economics, or computer science is preferred.

Formación

• Strong background in machine learning and statistical modelling.
• Proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, PyTorch/TensorFlow).
• Understanding of time-series forecasting and cross-validation techniques.
• Academic research experience and ability to translate to production code.
• Prior exposure to systematic trading or financial modelling.
• Ability to design experiments and iterate quickly.

Responsabilidades

• Research, prototype, and validate systematic trading signals across commodities using advanced ML methods.
• Design and implement backtests with walk-forward validation and robust statistical tests.
• Engineer and maintain features from prices, fundamentals, and alternative datasets.
• Blend alpha forecasts into meta-models and portfolio signals.
• Develop portfolio construction and optimization tools to assess execution impact.
• Collaborate with developers to transition research into production-ready strategies.
• Monitor live performance, attribution, and model drift.

Conocimientos

Machine learning

Statistics

Python

Time-series

Cross-validation

Production code

Research experience

Quant finance

Model validation

Educación

Masters or PhD in Statistics/Economics/CS

Herramientas

pandas

NumPy

scikit-learn

XGBoost

PyTorch

TensorFlow

Descripción del empleo

Moreton Capital Partners seeks a Quant Researcher to design, test, and refine predictive models for commodity futures in a production-ready research environment. You’ll build ML signals, run backtests, and feed live trading portfolios.

The ideal candidate has a strong ML and time-series background, plus a track record translating academic work into robust production code. Masters or PhD in statistics, economics, or computer science is preferred.