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Strong bank brand, popular data scientist role, and Bangalore metro drive high competition.
Statistical and coding skills transfer across industries, but credit-risk and regulatory expertise limit cross-industry fit.
Explicit 1–3 year requirement, mandatory R/Python/SQL skills and regulatory credit-risk knowledge increase shortlisting strictness.
Lead initiatives to develop, maintain, and enhance credit risk models for calculating credit risk RWA, provisioning, and obligor creditworthiness.
Build statistical models and perform analyses using advanced statistical and econometric techniques (e.g., Time Series Analysis, Macroeconomic Modelling, Non-linear Regression) primarily with R and Python.
Collaborate with multiple teams across the Group for model development, data preparation, validation, and impact analysis of credit portfolios.
1-3 years of relevant work experience in quantitative modelling or data science.
Tertiary qualification in a quantitative discipline such as mathematics, statistics, econometrics, actuarial science, finance, engineering, or data science.
Proficiency in R or Python for modelling and experience with Teradata SQL or Microsoft SQL for data handling.
Work Experience Required: 1-3 years in relevant modelling or analytics role.
Experienced in credit risk modelling specifically related to credit risk RWA, provisioning, or creditworthiness assessment within a banking or financial risk management context.
Able to independently deliver advanced statistical models and insights with minimal supervision, showing a clear understanding of business and regulatory impacts.
Demonstrates strong collaboration skills interfacing with validation, risk, IT, and business units across a large regulated financial institution.