Data Scientist - Fixed Income

Benefits

Parental leave

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Non-birth-parent leave

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Deadline

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About this role

Data Scientist - Fixed Income London 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. We are looking for an exceptional Data Scientist with a strong fixed income background. In this role, you will work between the Research and Trading desks, and the Engineering team to ensure the successful leveraging of data across the firm's fixed income strategies and beyond. Your future role within QRT This team is integral to the firm's success. As such, your responsibilities will include: - Collaborating with Quantitative Researchers and Traders, particularly within fixed income, to design datasets that drive systematic strategies and inform discretionary trading decisions - Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats, including fixed income market data, reference data, and pricing feeds - Working with Engineers to automate and optimise your code, ensuring robust data extraction processes - Managing the end-to-end process of onboarding new datasets, with a focus on fixed income instruments such as bonds, rates, credit, and derivatives - Proactively solving data related problems to minimise time to production - Innovating and experimenting with

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