Credit Risk Model Development Quantitative Analyst II – Consumer Portfolio

Role Overview

• Provides experienced support in the development and analysis of quantitative/econometric behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning.
• Supports more experienced analysts and management in data analysis, model development efforts and ad-hoc analysis as needed.
• Provides guidance and direction to less experienced personnel as needed.
• Assist in researching and developing quantitative behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning, including but not limited to, loan delinquency, default and loss models, loan prepayment and utilization models, deposit attrition models and financial instrument valuation methods.
• Prepare, manage and analyze large customer loan, deposit and/or financial data sets for statistical analysis in Structured Query Language (SQL) or similar tool.
• Understand the context of the Bank’s data and businesses to ensure properly developed models.
• Run regressions (including time series and logistic regression), programming routines and other econometric analyses to specify models using appropriate statistical software; communicate results, including graphic and tabular forms, to fellow team members, Treasury management and Bank-wide stakeholders.
• Execute models in production environment; communicate analytical results to Bank-wide stakeholders.
• Track portfolio performance, model performance, campaign tracking and risk strategy results.
• Develop and maintain satisfactory model documentation

Requirements

• Bachelor’s degree and a minimum of 1 years’ proven quantitative behavioral modeling experience, or in lieu of a degree, a combined minimum of 5 years’ higher education and/or work experience, including a minimum of 1 years’ proven quantitative behavior modeling experience
• Minimum of 1 years’ on-the-job experience with pertinent statistical software packages (SAS, Python, Stata, R)
• Strong Python skills required
• Model development experience required, including familiarity with logistic regression and linear regression
• Minimum of 1 years’ on-the-job experience with data management environment, such as SQL Server Management Studio
• Minimum of 1 years’ experience in managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs

Tech Stack

• Python
• SQL

Benefits

• Health insurance
• 401(k) matching
• Paid time off
• Flexible work arrangements
• Professional development opportunities