Bloomberg's Fixed Income Portfolio Research team supports the PORT business in the development of portfolio risk and attribution models. The role reports to the head of the Fixed Income Portfolio Research team within the broader Portfolio Risk and Index Quant Research group.

We are seeking a quantitative researcher to join an active research team focused on fixed income portfolio research: building fixed income factor models and performance attribution models. Ideally, a successful candidate will be passionate about finding new ways to better quantify ex-post portfolio attribution and ex-ante portfolio risk and make the quantitative analysis a part of the portfolio management process.

Responsibilities

• Develop and validate factor models and attribution models for various asset classes: government bonds, corporate bonds, munis, derivatives, etc.
• 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
• Perform literature reviews and keep apprised of portfolio research

Qualifications

The candidate should demonstrate expertise in quantitative analysis techniques, including knowledge and experience with a range of data sources and statistical analysis. Additionally, the candidate should have either the knowledge or ability to learn research related to fixed income factor model construction and performance attribution models.

Key qualifications include:

• Master's or PhD degree in Mathematics, Economics, Statistics, Quantitative Finance or a similarly quantitative field
• Experience implementing statistical models that apply cross-sectional and time-series econometrics, dimensionality reduction, and optimization techniques
• Expertise in one or more statistical programming languages – Python is preferred
• Familiarity with software development tools such as GitHub
• Competency with computer science fundamentals such as data structures, algorithms and parallel task distribution
• 2+ years of experience working on a team dealing with data-intensive modeling

Exposure to one or more of the following areas is a plus:

• Machine learning techniques, including classifiers, filters, neural networks, and ensemble learning
• Factor-based models and portfolio optimization
• Fundamental financial analysis and corporate valuation
• Stochastic bond valuation models

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.