Description

Build and deploy predictive models for credit risk using AWS SageMaker, PySpark, and advanced machine learning techniques.

This role is on-site.

Responsibilities

• Develop and validate predictive models for credit risk using bureau and internal datasets.
• Build and deploy application scorecards and credit scorecarding frameworks for underwriting decisions.
• Perform feature engineering on credit bureau data to improve risk prediction performance.
• Utilize AWS SageMaker and PySpark for large-scale data processing and model deployment.
• Monitor key risk KPIs such as NPA, DPD, and Collection Efficiency to drive business recommendations.

Required Skills

• 5-8 years of experience in Data Science, Analytics, and Credit Risk Modeling.
• Strong hands-on experience with AWS SageMaker for ML development and deployment.
• Proficiency in PySpark for large-scale data processing and analytics.
• Deep knowledge of machine learning techniques, including Random Forest and advanced modeling.
• Experience in credit scorecard development, application scoring, and feature engineering.
• Familiarity with credit bureau data and decisioning workflows.
• Background in BFSI/NBFC domain with exposure to retail lending analytics.

Preferred Skills

• Experience with Retail Analytics and Risk Analytics tools.

Key Skills

Education

Any Graduate