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Strong employer brand and Hyderabad metro increase competition, but niche credit-risk toolset limits applicant pool.
Strong dependence on credit risk decisioning, regulatory knowledge, and bank-specific tools limits cross-industry transferability.
Mandatory 2+ years and specific SAS/SQL, decision-engine, testing and automation skills impose high filtering.
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Support development and risk business teams by validating business rules and strategies through various testing (functional, UAT, regression).
Provide analytics to improve efficiency and effectiveness of risk strategies for unsecured consumer lending products (Cards, Loans).
Collaborate with US and India teams to optimize processes with zero defect leakage and support new product launches and BAU releases.
2+ years experience in credit risk analytics domain focused on unsecured lending (credit cards, personal loans).
Bachelor's degree in Engineering, Technology, Mathematics, Econometrics, Computer Science, or related field.
Strong hands-on experience with SAS and SQL; experience with decision engines like Zoot Webrule Builder, FICO DMP, Experian PowerCurve.
Work Experience Required: Minimum 2 years in credit risk analytics for unsecured lending products.
Experience implementing and validating credit risk strategies across acquisition, portfolio management, and collections stages using decision systems.
Proficient in end-to-end testing and validation frameworks (SAS/SQL) and managing UAT and production deployments with strong issue resolution skills.
Familiar with automation (VB Script, Selenium/UFT, UNIX shell scripting), Agile processes, and has domain knowledge of unsecured lending lifecycle and risk policies.