About this role
Design and build credit risk scorecards (application, behavioral, and collections scorecards) to predict creditworthiness and optimize account management strategies.
Extract, clean, and preprocess data from various sources using SQL.
Develop and implement machine learning models for collections performance and delinquency prediction using Python (including libraries like pandas, scikit-learn).
Monitor model performance, conduct backtesting, recalibration, and validation adhering to regulatory standards.
Collaborate with business and data teams to translate credit risk requirements into modeling specifications.
Document modeling processes, assumptions, and results for internal governance and audit purposes.
Strong proficiency in Python and SQL for data manipulation, modeling, and automation.
Proven experience in credit risk scorecard development and collections/account management modeling.
Understanding of credit risk model metrics (Delinquency, KS, Gini, AUC, etc.)
Experience with machine learning algorithms, feature engineering, and model monitoring techniques.
Bachelor’s in Engineering or Master’s degree in Statistics, Mathematics
Familiarity with credit bureau data and experience working in a BFSI environment.