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Medium — metro Data Scientist title but niche credit-risk senior requirement reduces generic applicant density.
High — credit-risk scorecard and regulatory modeling requires specialized BFSI domain experience, reducing cross-industry fit.
High — requires proven credit-risk scorecard experience, regulatory model validation, and mandatory Python/SQL skills.
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Design and build credit risk scorecards for application, behavioral, and collections to predict creditworthiness and optimize account management.
Develop, implement, monitor, and validate machine learning models for collections performance and delinquency prediction following regulatory standards.
Collaborate with business and data teams to translate credit risk requirements into modeling specifications and document modeling processes for governance and audit.
Proven experience in credit risk scorecard development and collections/account management modeling.
Strong proficiency in Python (including pandas, scikit-learn) and SQL for data manipulation and modeling.
Bachelor’s degree in Engineering or Master’s degree in Statistics or Mathematics.
Experience working with credit risk model metrics (Delinquency, KS, Gini, AUC) and familiarity with BFSI environment and credit bureau data.
Experienced in operating within BFSI domain, specifically credit risk modeling and collections.
Capable of end-to-end model development including feature engineering, model monitoring, backtesting, and recalibration.
Able to collaborate effectively with cross-functional teams to convert business requirements into technical modeling deliverables and ensure regulatory compliance.