Quantitative Researcher - Fixed Income Risk Models

Bloomberg’s Portfolio and Index Research group is responsible for the development of quantitative models for the analysis of portfolio risk & performance, as well as the research of investment strategies across asset classes that capture systematic premia or investor preferences. Within this group, the Fixed Income Analytics & Portfolio Research team supports the risk model, attribution, and scenario analysis for all fixed income cash bonds and derivatives using advanced quantitative techniques.

We are seeking a quantitative researcher to join an active research team responsible for fixed income risk model and performance attribution. The role will be primarily focused on the development and support of risk and attribution models for fixed income bonds and derivatives. The role reports to the head of the Fixed Income Analytics & Portfolio Research team.

We are looking for someone with strong programming skills who can demonstrate expertise in quantitative analysis techniques, including knowledge and experience with a range of data sources and statistical analysis. Additionally, we would like you to have experience with building a single security pricing model and analytics.

We’ll trust you to:

• Validate security level analytics generated by Bloomberg pricing models
• Develop and validate models covering risk forecast and performance attribution for fixed income and derivatives
• Collaborate with Data, Product, and Engineering teams
• Propose and substantiate new research ideas
• Communicate clearly through face-to-face meetings, presentations and written publications
• Deliver complex projects with multiple stakeholders

You'll need to have:

• PhD or equivalent experience
• 5+ years of experience within fixed income analytics or quantitative portfolio research*
• Programming skills in Python and database languages in addition to Linux, Shell Scripts, and Github
• Experience building single security pricing models for bonds and fixed income derivatives
• Experience implementing statistical models that apply cross-sectional and time-series econometrics, dimensionality reduction, and optimization techniques
• Demonstrated effective communication with both internal and external stakeholders

We’d love to see:

• Degree in Mathematics, Economics, Statistics, Quantitative Finance, or a similarly quantitative field

*Please note we use years of experience as a guide, but we will certainly consider applications from all candidates who are able to demonstrate the skills necessary for the role.

If this sounds like you: 

Apply if you think we're a good match! We'll get in touch with you to let you know what the next steps are.

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