Commodities Quant Researcher

Commodity Quantitative Researcher

Our client is a leading proprietary trading firm seeking a talented

Commodity Quantitative Researcher

to join its growing quantitative research team. This role offers the opportunity to work on the research and development of systematic trading strategies across global commodity markets, leveraging advanced statistical techniques, machine learning, and large-scale data analysis.

Working closely with traders, quantitative researchers, and software engineers, you will play a key role in identifying new sources of alpha, developing predictive models, and supporting the deployment of research into live trading strategies.

Key Responsibilities

• Conduct quantitative research across global commodity markets to identify and develop systematic trading opportunities.
• Analyse large volumes of market, fundamental, and alternative datasets to uncover predictive insights.
• Design, develop, and validate quantitative models and alpha signals using statistical and machine learning techniques.
• Perform robust backtesting and evaluate the performance and stability of trading strategies.
• Collaborate closely with traders and portfolio managers to translate research ideas into live trading models.
• Develop and maintain research tools, data pipelines, and analytical frameworks to support the research process.
• Work alongside engineering teams to improve research infrastructure and automate workflows.
• Monitor the performance of deployed models and continuously refine strategies based on market behaviour and new data.
• Stay informed of developments in quantitative finance, artificial intelligence, and data science to enhance research capabilities.

Requirements

• Bachelor's, Master's, or PhD in Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, Economics, or another highly quantitative discipline.
• Experience in quantitative research, systematic trading, data science, or a similarly analytical role.
• Strong programming skills in Python, with experience working with large and complex datasets.
• Solid understanding of probability, statistics, time series analysis, optimisation, and predictive modelling.
• Experience with SQL and Linux-based environments.
• Familiarity with machine learning techniques and libraries such as scikit-learn, PyTorch, or TensorFlow is advantageous.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication skills with the ability to collaborate effectively across research, trading, and technology teams.
• A genuine interest in financial markets and quantitative research.

Preferred Experience

• Experience researching commodity, futures, or other financial markets.
• Exposure to systematic trading strategies or quantitative portfolio construction.
• Experience working with alternative datasets, including weather, satellite imagery, shipping, inventory, or macroeconomic data.
• Knowledge of cloud computing, distributed computing, or high-performance research environments.
• Experience applying AI or machine learning techniques to financial markets.