C1 - Credit Risk Modeler

EXL; Gurugram District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

EXL is seeking a credit risk modeling specialist in India to design and implement scorecards predicting creditworthiness and optimizing account management strategies.

You will extract data via SQL, preprocess it, and build ML models using Python libraries such as pandas and scikit-learn. The role requires monitoring, backtesting, recalibration, and regulatory-compliant validation to ensure governance and audit readiness.

Qualifications

• Experience designing credit risk scorecards.
• Proficiency in SQL for data extraction and preprocessing.
• Hands-on ML modeling in Python with common libraries.
• Knowledge of regulatory standards in credit risk modeling.

Responsibilities

• Design and build credit risk scorecards for predicting creditworthiness.
• Extract, clean and preprocess data from multiple sources using SQL.
• Develop ML models for collections performance and delinquency prediction in Python (pandas, scikit-learn).
• Monitor model performance, backtest, recalibrate and validate per regulatory standards.
• Collaborate with business and data teams to translate risk requirements into modeling specs.
• Document modeling processes, assumptions and results for governance and audit purposes.

Skills

Credit risk modeling

Data preprocessing

Backtesting

Regulatory compliance

Collaboration

Tools

SQL

Python

Pandas

scikit-learn

Job description

Job Description:

• 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.