Head of Credit Risk Analytics
About the Role
I am currently working with a leading financial institution seeking a hands-on leader to manage a team of quantitative analysts. The team supports a $250 billion wholesale loan portfolio by developing and maintaining advanced credit risk models, including CECL, probability of default, and internal rating models.
The ideal candidate will have deep technical expertise (ideally with a Ph.D. in a quantitative field) and be able to guide model development, manage regulatory interactions, and collaborate across business units. Candidates should have at least 12+ years of experience in credit risk modeling, strong programming skills in Python, R, SAS, and SQL, familiarity with regulatory frameworks, and experience managing a team.
Key Responsibilities
- Lead and mentor a team of quantitative developers focused on credit risk analytics for wholesale portfolios.
- Oversee the development, refinement, and implementation of models including CECL, PD, LGD, EAD, and internal ratings.
- Conduct quantitative research to align modeling practices with industry standards and regulatory expectations.
- Identify model limitations and design remediation strategies using advanced statistical techniques.
- Collaborate with stakeholders across risk, IT, and model governance teams to ensure effective model deployment and maintenance.
- Serve as a subject matter expert in regulatory and audit discussions.
- Define modeling standards and contribute to the development of model execution platforms.
Qualifications
- At least 12-15+ years of experience in credit risk modeling at a major financial institution.
- Minimum of 3 years managing a team of quantitative professionals.
- Advanced degree (Ph.D. preferred) in Quantitative Finance, Statistics, or a related field.
- Expertise in wholesale credit risk modeling and regulatory frameworks (e.g., CECL, CCAR/DFAST).
- Proficiency in Python, R, SAS, SQL, and experience with data visualization tools such as Power BI or Tableau.
- Strong foundation in advanced statistical methods, including time series modeling, Bayesian techniques, decision trees, and clustering.
- Familiarity with external credit data sources (e.g., S&P, GCD, Moody's Analytics).
- Demonstrated success in managing regulatory interactions and audit processes.
- Excellent communication skills, with the ability to translate complex quantitative concepts for non-technical stakeholders.
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