Lead Data Scientist – Hybrid

• Develop, enhance, and test company models for lending criteria and verification procedures
• Lead and perform independent validation of credit risk models
• Evaluate model fit for purpose, conceptual soundness, mathematical theory and assumptions, data and assumptions, and output reasonableness
• Conduct ad hoc statistical and financial analyses for risk management, marketing, and operational strategies
• Interpret, organize, and communicate data effectively to cross-functional teams
• Provide technical guidance and mentorship to team members
• Prepare model validation reports and technical documentation
• Interact with model stakeholders and business partners to gather information and provide effective challenge
• Support model releases into production
• Monitor model performance dashboards, build alerts, and
  ** escalate concerns**
• Improve model validation methodologies, processes, and governance through automation and custom tools

• Experience in developing and/or independently validating advanced credit risk models
• 5+ years of experience in quantitative analysis in the financial services industry and experience leading complex analytical or model validation projects is preferred
• Strong knowledge of statistical models and methodologies used in risk management
• Advanced programming skills in Python and the ability to write customized programs for meaningful data analysis
• Experience working with relational databases, such as SQL
• Demonstrated ability to provide technical guidance and communicate complex quantitative concepts to technical and non-technical stakeholders
• Eligible to work in the United States; visa sponsorship is not available

Demonstrates expertise in developing and validating advanced credit risk models, utilizing strong programming skills in Python and SQL for data analysis. Capable of effectively communicating complex quantitative concepts to diverse stakeholders while leading model validation projects.

• Advanced Credit Risk Model Development
• Quantitative Analysis in Financial Services
• Statistical Models and Methodologies
• Python Programming for Data Analysis
• SQL Database Management

• Credit Risk Model Validation
• Statistical Analysis
• Data Interpretation
• Model Performance Monitoring
• Model Validation Methodologies

• Technical Guidance
• Effective Communication
• Mentorship
• Cross-Functional Collaboration

• Financial Services
• Risk Management
• Quantitative Analysis
• Model Governance

• Python
• SQL
• Model Performance Dashboards
• Automation Tools