Senior Credit Modeling Lead - Hybrid/Remote (US)

Wilmington Trust; Washington (District of Columbia); Hybride; USD 124 000 - 206 000; Plein temps

Résumé du poste

M&T Bank in Buffalo, NY seeks a senior quantitative risk professional to support data, models, and forecasting for credit risk, interest rate risk, liquidity risk, CCAR, and economic capital. You will mentor analysts and collaborate with Model Risk Management on validation exercises.

The role requires 6+ years in quantitative behavioral modeling, strong Python/SAS skills, and experience with data environments. Hybrid in-office arrangement with potential remote US work.

Qualifications

• Bachelor’s degree with 6+ years of quantitative behavioral modeling experience, or 10 years combined education/work with 6+ years modeling.
• 6+ years’ experience with SAS, Python, Stata, or R.
• 6+ years’ experience with data management environments such as SQL Server Management Studio.
• Experience with credit modeling and commercial scorecard development.

Responsabilités

• Lead research and development for data and models related to credit risk, interest rate risk, liquidity risk, CCAR, and economic capital practices.
• Develop, implement, and validate behavioral models; facilitate model development challenges with governance groups.
• Explain model benefits, limitations, assumptions, and requirements to executive stakeholders.

Connaissances

Quantitative modeling

Leadership

Communication

Formation

Bachelor’s degree in a quantitative field

Master’s or PhD in statistics/economics/finance

Outils

SAS

Python

R

Stata

SQL Server Management Studio

Description du poste

M&T Bank in Buffalo, NY seeks a senior quantitative risk professional to support data, models, and forecasting for credit risk, interest rate risk, liquidity risk, CCAR, and economic capital. You will mentor analysts and collaborate with Model Risk Management on validation exercises.

The role requires 6+ years in quantitative behavioral modeling, strong Python/SAS skills, and experience with data environments. Hybrid in-office arrangement with potential remote US work.