Description

Key Skills: Predictive Modeling, Credit Risk Modeling, Credit Scorecarding, Credit Bureau Data, Feature Engineering, AWS SageMaker, PySpark, Machine Learning, Random Forest, Retail Analytics, Risk Analytics, Data Processing

Roles & 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 and decision-making.
• Perform feature engineering on credit bureau and internal data to improve risk prediction performance.
• Design and implement machine learning models for retail lending and credit risk use cases.
• Utilize AWS-based ML platforms such as SageMaker for model development and deployment.
• Perform large-scale data processing and analysis using PySpark.
• Analyze and monitor key business and risk KPIs such as NPA, DPD, Collection Efficiency, Bounce Rate, and disbursement metrics.
• Translate model outputs into actionable risk insights and business recommendations.
• Collaborate with business teams and stakeholders to understand requirements and deliver analytics solutions.
• Support model monitoring, optimization, and continuous improvement initiatives.

Experience:

• 5-8 years of relevant experience in Data Science, Analytics, and Credit Risk Modeling.
• Strong experience in predictive modeling for credit risk use cases.
• Hands-on experience in credit scorecard development and application scoring frameworks.
• Experience with credit bureau data, feature engineering, and decisioning workflows.
• Strong experience with AWS tools and machine learning model deployment.
• Experience with PySpark for large-scale data processing and analytics.
• Knowledge of machine learning techniques including Random Forest and advanced modeling approaches.
• Experience in BFSI/NBFC domain with exposure to retail lending analytics.

Education:  Any Graduation

Key Skills

Education

Any Graduate