Senior Quant Analyst

New York City, NY, United States
Posted a day ago
Main Location
New York City, NY, United States
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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.
  • 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.
  • Expertise in stochastic processes, statistics, and numerical methods.
  • Familiarity with Basel and IFRS9 regulations.
  • Familiarity with modelling techniques including logistic regression, multivariate analysis, Monte Carlo analysis, and survival analysis.
  • Strong software development skills.
  • Skilled in C++ and Python.
  • Strong team-player comfortable in a multi-developer environment with a facility for interacting with quants, IT groups and product managers.
  • Good oral and written communication skills.
  • Ph.D. in a technical discipline (mathematics or physics).
  • 5-10 years experience in credit risk modeling.
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