Senior Credit Risk Modeling & Analytics Lead

Wilmington Trust; Buffalo (NY); Hybrid; USD 85,800 - 143,000; Full time

Job summary

M&T Bank in Buffalo, NY seeks a senior analyst to develop and maintain quantitative behavioral models for credit, interest rate, and liquidity risk. The role involves handling large data sets, running econometric analyses, and communicating findings to Treasury and risk teams.

The position requires Python, logistic regression, SQL proficiency, and model development experience, with a strong emphasis on documentation and regulatory compliance. Hybrid work with in-office four days weekly.

Qualifications

• Bachelor’s degree with minimum 2 years of quantitative modeling experience or 6 years combined education/work with relevant modeling exposure.
• Minimum 2 years’ Python on-the-job experience.
• Model development experience, including credit modeling, is required.
• Logistic regression experience is required.
• Minimum 2 years’ experience with SQL-based data environments (e.g., SQL Server).
• Experience analyzing large datasets and communicating results clearly.

Responsibilities

• Research and develop quantitative behavioral models for credit risk, interest rate risk and liquidity risk management.
• Prepare and analyze large loan, deposit and financial datasets using SQL or similar tools.
• Run regressions and econometric analyses; communicate results to stakeholders with visuals.
• Deploy models in production and monitor performance and risk impact.
• Develop model documentation and performance monitoring guidelines.
• Collaborate with Model Risk Management and other groups as needed.

Skills

Python

Logistic regression

Data analysis

Communication

Education

Bachelor’s degree

Tools

SQL Server Management Studio

SQL

Job description

M&T Bank in Buffalo, NY seeks a senior analyst to develop and maintain quantitative behavioral models for credit, interest rate, and liquidity risk. The role involves handling large data sets, running econometric analyses, and communicating findings to Treasury and risk teams.

The position requires Python, logistic regression, SQL proficiency, and model development experience, with a strong emphasis on documentation and regulatory compliance. Hybrid work with in-office four days weekly.