Quantitative Developer - Python

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 alongside quantitative researchers and portfolio managers to develop the technology that underpins systematic trading strategies.

This role is embedded within the research environment, where engineering and quantitative research are closely integrated.

You will help develop research frameworks, productionise trading strategies, and improve the engineering standards that support systematic investment research.

Your future role within QRT

• Build, maintain and enhance the quantitative trading framework used for research and production
• Productionise quantitative trading strategies developed by researchers
• Collaborate with quantitative researchers and portfolio managers to improve research workflows, development practices and engineering standards
• Develop tooling and infrastructure that supports the full lifecycle of systematic trading strategies
• Contribute to machine learning and data driven research initiatives through software engineering and platform development
• Evolve the software platform to meet changing research and business requirements
• Support technically complex research projects and deliver robust production solutions

Your present skillset

• 5 or more years of professional experience as a Quantitative Developer or Quantitative Technologist working directly with quantitative research teams within a systematic investment environment
• Strong Python development skills
• Experience building software for quantitative research and systematic trading workflows
• Strong understanding of quantitative trading strategies, with the ability to review and improve research code while understanding the underlying investment logic
• Experience developing machine learning pipelines and supporting production deployment