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Tier-1 bank, metro location, and mid-level analytics role increase candidate competition.
Specialized unsecured lending and decision-engine experience limits transferability across industries.
Explicit 2+ years plus mandatory SAS/SQL, decisioning tools, and credit-risk domain skills increase screening strictness.
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Support Risk Business and Development teams by validating business rules and strategies through various testing types including functional, UAT, and regression testing.
Provide analytics to improve efficiency and effectiveness across unsecured consumer lending products (Cards, Co-Brand, Retail Services, Personal Loans, Flex Loan).
Collaborate with US and India teams to optimize processes with zero defect leakage and support new product launches and business-as-usual releases involving decision engine implementations (e.g., Zoot Webrule, FICO DMP).
2+ years of experience in credit risk analytics, specifically unsecured lending (credit card and personal loan products).
Bachelor's degree in Engineering, Technology, Mathematics, Econometrics, Computer Science, or related field.
Strong hands-on experience with SAS and SQL for data analysis, validation, and testing.
Experience with decision engine tools (Zoot Webrule Builder, FICO DMP, Experian PowerCurve) and understanding of credit risk strategies and unsecured lending lifecycle.
Experienced in end-to-end strategy validation, testing, and production deployment in credit risk analytics within unsecured consumer lending.
Familiar with automation scripting (VB Script, Selenium/UFT, UNIX shell) and Agile delivery processes, contributing to sprint planning and execution.
Capable of cross-geography collaboration (US and India teams) to ensure process optimization and compliance with risk strategies in a regulated banking environment.