Bloomberg's Multi-Asset Risk System (MARS) is a comprehensive suite of risk management tools that delivers consistent, consolidated results for each client's entire firm. The Bloomberg Quantitative Risk Analytics group is responsible for all quantitative components of MARS, including regulatory capital calculations, CCAR scenarios, FRTB, SIMM and IFRS9 support, VaR, stressed VaR, ES, predictive stress, exposure calculations (PFE, EPE, etc), and credit modeling.

The Quantitative Risk Analytics group seeks a strong quantitative analyst to work on developing Bloomberg's default risk and portfolio credit risk models. These models are used by clients for credit risk analysis, Basel regulatory capital calculations and supporting IFRS9 regulations. The candidate will be responsible for validating data, researching and prototyping models, documenting models, and interacting with internal and external clients.

Core Responsibilities:

• Research, design, prototype, document and support statistical/econometric credit risk models, including probability of default (PD), loss given default (LGD), exposure at default (EAD), and portfolio loss calculations.
• Validating and cleaning the data used by the models.
• Communicate modeling concepts and model assumptions with clients, the business unit, the sales and support unit, and independent development teams.

Qualifications:

• Ph.D. in a technical discipline.
• Strong software development skills, especially in Python and C++.
• Strong oral and written communication skills.
• Proven ability to do independent research.
• Strong team-player comfortable in a multi-developer environment with a facility for interacting with quants, IT groups and product managers.
• 1-5 years experience in quantitative finance, especially in credit and default modeling.

We would love to see:

• Demonstrated understanding of the statistical and theoretical issues surrounding the joint measurement and estimation of default and recovery rates.
• Demonstrated record of designing, estimating and implementing risk-related statistical models, especially default risk models.
• Demonstrated understanding of market-relevant measures of risk, and, in particular, in the area of credit risk.
• Familiarity with Basel and IFRS9 regulations.
• Familiarity with modelling techniques including logistic regression, multivariate analysis, Monte Carlo analysis, and survival analysis.

If this sounds like you:

Apply if you think we're a good match and we'll get in touch with you to let you know next steps. In the meantime, check out http://www.bloomberg.com/professional.

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.