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Tier-1 bank, mid-level analytics role in Hyderabad with generalist skills increases candidate competition.
Strong credit-risk and decision-engine domain specificity reduces cross-industry transferability.
Mandatory 2+ years, SAS/SQL, decisioning systems, and domain-specific testing increase selection rigidity.
Support development and risk business teams in validating credit risk business rules and strategies via various testing methods (functional, UAT, regression).
Provide analytics to improve efficiency and effectiveness of credit risk operations mainly involving unsecured consumer lending products like credit cards and personal loans.
Collaborate with US and India teams to optimize processes and ensure zero defect leakage in assigned tasks related to credit risk decisioning and strategy validation.
Minimum 2+ years experience in credit risk analytics domain, specifically unsecured lending (credit cards, loans).
Bachelor’s degree in Engineering, Technology, Mathematics, Econometrics, Computer Science, or related field.
Hands-on experience with SAS and SQL for data analysis, validation, and testing; familiarity with databases like Teradata, Oracle, SQL Server, or DB2.
Experience with decision engines/tools (Zoot Webrule Builder, FICO DMP, Experian PowerCurve) and end-to-end testing (Unit, SIT, UAT, Regression).
Experience working with cross-functional teams across locations (US and India), indicating ability to manage distributed collaborations.
Technical proficiency in risk analytics with automation scripting skills (VB Script, Selenium/UFT, UNIX shell scripting) to drive process efficiency.
Understanding of credit risk strategies, segmentation, scorecards, and regulatory landscape of unsecured lending products.