Quantitative Developer - Commodities (Python) in London, United Kingdom

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

You will work within a quantitative development function supporting research and trading activities across Commodities, including Agriculture, Metals and Energy. The role focuses on developing Python applications, quantitative tools and data infrastructure that support trading decisions, research and risk management. You will collaborate closely with Quantitative Researchers, Traders, Data Engineers and infrastructure specialists to deliver solutions across alternative data, pricing, backtesting and statistical modelling.

Your future role within QRT

• Design, develop and deploy quantitative tools supporting trading and research, including pricing, backtesting, risk analysis and PnL reporting

• Build Python applications and data pipelines to explore, integrate and analyse alternative datasets, including weather and cargo data

• Collaborate with Quantitative Researchers and Traders to translate trading ideas and business requirements into production applications and quantitative models

• Architect, develop and maintain cloud applications in collaboration with infrastructure engineers, using AWS and related technologies

• Develop and apply statistical and machine learning models to support research and trading across Agriculture, Metals and Energy

• Implement quantitative optimisation methods and automate research and trading workflows

• Monitor, troubleshoot and enhance existing trading and research infrastructure to ensure reliability and performance

Your present skillset

• Minimum 2 years of professional software development experience, with strong Python programming skills or proficiency in another object oriented language

• Strong understanding of software design principles and experience developing maintainable applications

• Proficiency with Python numerical and data analysis libraries such as NumPy, Pandas, Polars and Matplotlib

• Solid SQL skills and experience working with relational databases

• Experience developing data processing pipelines, ideally using orchestration frameworks such as Airflow

• Familiarity with AWS or similar cloud platforms and infrastructure as code practices

• Good understanding of software development practices, including Git, unit testing, packaging and scripting

• Experience using AI development tools to support coding, debugging and software delivery

• Strong quantitative and analytical skills, with an interest in financial markets and trading

• Ability to work autonomously, manage priorities and collaborate effectively with Researchers, Traders and Engineers

• Previous experience developing quantitative tools or models within Commodities trading is beneficial

• Experience building dashboards and data visualisation applications using Plotly, Streamlit, Dash or htmx is advantageous

• Advanced AWS experience, including services such as DynamoDB, RDS, S3, Lambda and AWS CDK, is beneficial

• Familiarity with Snowflake or similar data platforms is advantageous

• Experience with parallel or distributed computing frameworks such as Slurm and Ray is beneficial

• Experience developing statistical or machine learning models is advantageous

• Previous experience mentoring junior developers or interns is beneficial

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.