Credit Model Development Quantitative Analyst II

• Research and develop quantitative behavioral models for credit risk, interest rate risk, liquidity risk management, balance sheet management and capital planning
• Prepare, manage and analyze large customer loan, deposit and financial data sets using SQL or similar tools
• Run time-series and logistic regressions, programming routines and econometric analyses using statistical software
• Communicate analytical results through clear narratives, graphic and tabular formats to Treasury management and Bank-wide stakeholders
• Execute models in production environments
• Track portfolio, model, campaign and risk strategy performance; improve predictive results and explain variances
• Develop and maintain model documentation, process narratives and performance monitoring guidelines
• Provide financial analysis and data support across the Bank
• Support Model Risk Management model validation engagements
• Guide less experienced personnel on data analysis, predictive statistical models and financial analysis
• Maintain compliance with regulatory guidance, risk controls, policies and procedures
• Identify risk-related issues requiring escalation and complete related duties as assigned

• 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)
• 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
• Fluency and high proficiency in econometric/statistical techniques, especially time-series analysis, panel data methods and logistic regression
• Experience in balance sheet management and mathematical modeling of financial instruments offered by banks
• Knowledge and familiarity with key aspects of model risk management and model validation, including SR-11-7 guidance on model risk management
• Proven track record for being able to work autonomously and within a team environment
• Demonstrated leadership skills
• Strong desire to learn and contribute to a group

Expertise in quantitative behavioral modeling, including econometric techniques such as time-series analysis and logistic regression, alongside strong data management skills using SQL and statistical software. Proven ability to communicate complex analytical results effectively to stakeholders while maintaining compliance with regulatory standards.

• Quantitative Behavioral Modeling
• SQL Server Management Studio
• SAS
• Python
• Econometric Techniques

• Time-Series Analysis
• Logistic Regression
• Panel Data Methods
• Data Management
• Financial Analysis

• Clear Communication
• Leadership
• Team Collaboration
• Autonomous Work
• Desire to Learn

• Model Risk Management
• Model Validation
• Balance Sheet Management
• Risk Controls
• Regulatory Compliance

• Statistical Software
• SAS
• Python
• Stata
• SQL