Data Scientist

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 QRT’s 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 data focused function supporting Quantitative Researchers, Traders, and other stakeholders across the firm. The role focuses on designing and developing financial datasets that support systematic strategies and trading decisions, with particular emphasis on market data, pricing datasets, and reference data. You will combine financial market knowledge with Python and data engineering skills to build and own the automated production pipelines that create and maintain these datasets.

Your future role within QRT

• Collaborate with Quantitative Researchers and Traders to design financial datasets that support systematic strategies and trading decisions
• Develop Python code to extract, clean, normalise, and aggregate data from a range of financial market data sources
• Design, build and maintain automated data pipelines covering sourcing, extraction, transformation, validation and delivery
• Develop and maintain pricing and reference datasets across financial instruments and markets
• Investigate and resolve data quality and production issues to improve the reliability and availability of datasets
• Evaluate and implement new approaches to data extraction, processing and onboarding
• Take ownership of production data workflows and evolve them as data requirements, systems and business priorities change

Your present skillset

• 2 to 5 years of experience in a data focused role working with financial or market data
• Strong understanding of financial markets and instruments, including the behaviour and characteristics of pricing and reference datasets
• Advanced Python programming skills, including experience with data processing libraries such as Pandas or Polars
• Experience designing, building and maintaining automated production data pipelines
• Strong data analysis and problem solving skills, with the ability to investigate data quality issues and understand their underlying causes
• Experience working with market data platforms and APIs from providers such as Bloomberg or LSEG is beneficial, including products such as Datastream, DataScope Select, Real Time, BPIPE or Data License