Job
Description
Role Overview: At ZET, as a Risk Analytics Manager, you will play a crucial role in designing, developing, and validating risk models to optimize credit decisioning. You will be responsible for conducting portfolio analytics, leveraging statistical and machine learning techniques, and working closely with cross-functional teams to implement data-driven risk policies. Key Responsibilities: - Design, develop, and validate risk models such as scorecards, look-alike models, and behavioural risk models to optimize credit decisioning. - Conduct portfolio analytics to monitor delinquency trends, vintage analysis, and risk-return trade-offs. - Leverage statistical and machine learning techniques to enhance customer underwriting, fraud detection, and collection strategies. - Collaborate with credit, product, and technology teams to implement and refine data-driven risk policies. - Identify early warning signals and propose changes in credit approval criteria. - Perform A/B testing and back-testing of models to ensure robustness and accuracy. - Partner with credit bureaus, data providers, and internal teams to integrate new data variables. - Create dashboards, MIS, and insights for leadership to facilitate risk-based decision-making. - Demonstrate adaptability in dealing with ambiguity in a fast-paced environment. - Successfully collaborate across functions to achieve objectives. Qualifications Required: - Possess 3+ years of experience in risk analytics within an NBFC or fintech industry. - Strong exposure to AI/ML, credit risk modelling, portfolio analytics, and policy formulation. - Hands-on experience with look-alike models, scorecards, and data modelling techniques. - Proficiency in SQL, Python/R, and statistical modelling techniques. - Familiarity with credit bureau data (CIBIL, Experian, Equifax, CRIF) and alternate data usage. - Strong analytical, problem-solving, and communication skills. - Educational background in Statistics, Mathematics, Economics, Engineering, or related fields (Tier-1/Tier-2 institute preferred).,
