Vice President level quantitative credit risk. The VP will work on the Credit Risk Analytic team reporting into the Head of Risk Analytics. The credit risk analytics function is responsible for development and maintenance of quantitative models to support credit risk management and portfolio management requirements that cover the wholesale loans, repos, project finance, securitizations and IR derivatives portfolios. Candidate will join the Risk Analytics group that partakes in model development over the full life-cycle of modes: from methodology to design to implementation. The successful candidate will also provide analysis and feedback on changes to or introduction of new models at the firm.
- Develop credit risk models (ALLL, PD, LGD, and EAD) that support the loss estimation for CECL and stress testing.
- Develop and enhance counterparty risk models (PFE, EPE, XVA) that support credit capital assessment
- Maintain and enhance the existing models through various statistical tests, back-testing, and sensitivity testing, and perform gap and attribution analysis from enhancements
- Work on credit risk management initiatives such as stress, concentration risk, idiosyncratic risk, and CCP risk.
- Support model submission and validation review by providing requested materials and performing appropriate analysis. Work with internal model validation team to ensure successful model validation.
- Routinely monitor the model performance to support risk appetite and capital planning exercises.
- Develop Risk Analytics platform.
- Bachelor degree in a quantitative field such as mathematics, statistics, computer sciences, economics, quantitative finance or engineering; Advanced degree is a plus.
- At least 5 years of experience working for large and complex financial institutions as a quantitative model developer.
- Strong technical knowledge and experience designing framework and developing credit risk analytics such as EPE, PFE, XVA, ALLL, PD, LGD, Stress Testing models under SR11-7.
- Strong working knowledge of investment bank credit products (including loans, repos, CDS, and structured products) and the associated credit risk metrics.
- Strong understanding of derivative products and Credit RWA.
- Strong experience developing analytics for CECL; implementation of CECL end to end from either CCAR or DFAST built is a plus.
- Proficient programming skills in python and R (other languages such as SAS, C++, PERL is a plus).
- Database expertise: SQL, Sybase.
- Superior oral and written communication skills.
- Experience with stress testing and SR12-7 is a plus.