Credit Risk Modeling Analyst II — Hybrid (Quant)

Wilmington Trust; Buffalo (NY); Hybrid; USD 71,600 - 119,300; Full time

Job summary

M&T Bank is seeking an experienced analyst to support the development and analysis of quantitative behavioral models used for credit risk, interest rate risk, and liquidity risk management. The role includes data preparation, econometric analysis, and communicating results to Treasury and stakeholders.

The ideal candidate will have a Bachelor’s degree in a quantitative field (or equivalent experience), strong Python skills, and experience with SAS/R/Stata, plus familiarity with SQL Server

Qualifications

• Bachelor’s degree in a quantitative field or equivalent with at least 1 year of quantitative behavioral modeling experience.
• 1 year minimum experience with statistical software packages (SAS, Python, Stata, R).
• Strong Python skills required and model development experience including logistic and linear regression.
• Experience with data management environments such as SQL Server Management Studio.
• Experience analyzing large data sets and communicating results clearly through written reports and charts.

Responsibilities

• Assist in researching and developing quantitative behavioral models for credit risk, interest rate risk, and liquidity risk management.
• Prepare and analyze large loan/deposit data sets for econometric modeling.
• Run regressions and econometric analyses; communicate results to team members and management.
• Deploy models in production and monitor performance with stakeholders.

Skills

Python

SAS

R

Stata

SQL

Education

Bachelor’s degree in a quantitative field

Master’s or Doctorate (preferred)

Tools

SQL Server Management Studio

Python

SAS

Stata

R

Job description

M&T Bank is seeking an experienced analyst to support the development and analysis of quantitative behavioral models used for credit risk, interest rate risk, and liquidity risk management. The role includes data preparation, econometric analysis, and communicating results to Treasury and stakeholders.

The ideal candidate will have a Bachelor’s degree in a quantitative field (or equivalent experience), strong Python skills, and experience with SAS/R/Stata, plus familiarity with SQL Server