Develop credit risk strategies and enhancements for assigned loan portfolios to improve performance. Design and monitor credit risk oversight framework, developing credit risk appetite, risk metrics thresholds, and limits. Proactively analyze portfolio performance at the granular level using bureau data, cash flow data, and transactional data to identify emerging credit trends, conduct root-cause analysis, and isolate key performance drivers. Conduct A/B tests to reduce credit risk, improve customer experience, and optimize profitability on acquisition initiatives. Support cross-functional teams in credit decision processes and partner with stakeholders to ensure understanding of changes to risk factors and the underwriting/credit decision flow. Prepare reports and documentation for regulatory reviews and audit activities. Interface with Modeling/Scoring team to deploy risk strategies based on new models to improve effectiveness of the model deployment process. Develop and enhance the reporting structure for assigned markets, products, and channels, by developing/modifying scoring datasets/warehouse and developing monitoring reports for use in performance tracking. Utilize relational database and/or analytical programs to create data for analysis and monitoring of strategies and models. Forecast impact of strategy changes on key business performance metrics such as revenues, balances, and losses. Provide coaching and support to teammates to identify actionable insights, suggest recommendations, and influence the direction of the business. May telecommute from anywhere within the United States.
A Bachelors degree (or foreign equivalent) in statistical science, economics, finance, mathematics, operations management, or related quantitative field; plus 60 months of experience in job offered or in a position involving credit risk analytics.
A Master's degree plus 36 months of experience accepted in the alternative.
Requires demonstrated experience in: SQL; SnowFlake or similar relational databases; Credit risk models, mitigation, and underwriting; Analytical tools (SAS, Python, or R); Data visualization tools (Tableau, Sigma, or Power BI). Also requires knowledge of financial services with emphasis on risk management/analytics of open-end products.

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