Senior Vice President - Risk Modelling/CCAR - Financial institution

Senior Vice President - Risk Modelling (CCAR) - 14+ Years - Mumbai

We are looking for an experienced professional with hands-on experience in developing and implementing state-of-the-art quant/stats models. We are looking for someone with strong experience in SAS, Python, or R.

If this sounds exciting, apply with us!

Location: Mumbai

Your Future Employer: One of the worlds Biggest Financial institutions with a strong global footprint and a huge customer base.

Responsibilities:

- Developing econometric forecasting models for key Balance sheet and income statement line items for capital and business planning purposes.

- This includes the calculation of Net Interest Income (NII") Non-Interest Revenue ("Non-NIR"), Interest Rate Exposure ("IRE"), Economic Value Sensitivity ("EVS"), and other associated interest rate risk metrics.

- Steering stakeholder conversations with Businesses, Finance, Treasury, and Risk to seek their sign-offs on Champion models.

- Manage thing Segmentation, Risk Identification, and overlay discussions with Businesses and Finance teams.

- Reviewing and timely submission of Model development documentation (MDDTs) for the entire PPNR modeling landscape to Model Risk Management.

- Developing and maintaining a comprehensive modeling system that supports a consistent approach to data quality and modeling methods, audit, backtest, tracking, and annual validation.

- Have managed a large team of a minimum 8-12 statisticians/econometricians in the previous role

Requirements:

- 14 years of relevant statistics/ economics experience in financial services

- Masters / Ph.D. in quantitative disciplines line such as Statistics, Economics, Finance or related discipline

- Deep understanding of statistical techniques such as Panel Regression, Error Correction Models, Seemingly Unrelated Regression, and Cointegration.

- Experience in CCAR Modeling / Reporting to OCC, FRB and FDIC.

- Experience in developing econometric and Panel regression models.

- Extensive hands-on experience in programming and modeling using SAS.