Job Description
Credit Risk Machine Learning Model Specialist
Location: Gurgaon/Bangalore/Pune
Department: Analytics & Risk Management
Experience Level: 4+ years
About the Role
We are seeking a talented Credit Risk Machine Learning Model Specialist to join our dynamic team at EXL. In this role, you will develop and deploy advanced ML models to drive credit risk strategies across the consumer lending lifecycle, from underwriting and account management to collections. You'll collaborate with cross-functional teams to enhance decision-making, ensure regulatory compliance, and deliver impactful business outcomes in a fast-paced financial services environment.
Key Responsibilities
- Design, build, and optimize machine learning models for credit risk assessment, including underwriting, account management, and collections strategies.
- Analyze loan products and credit risk dynamics across consumer lifecycle stages, leveraging data to inform business strategies.
- Implement model explainability techniques (e.g., SHAP) and fairness assessments to promote transparent and equitable lending practices.
- Extract and manipulate large datasets using SQL and Python to support model development and performance monitoring.
- Partner with stakeholders to translate business needs into actionable ML solutions, contributing to US lending initiatives where applicable.
Qualifications
- Minimum 4 years of hands-on experience in credit risk modeling and lending strategies within banking and financial services, with exposure to consumer lending lifecycles.
- Strong domain knowledge of loan products and credit risk management.
- Proficiency in Python (e.g., scikit-learn, XGBoost, Pandas) and SQL for data processing and model building.
- Experience with model explainability (SHAP, LIME) and fairness testing is a plus.
- Familiarity with US lending business practices is a plus.
- Exposure to additional tools like Spark, AWS, or Tableau for scalable analytics is a plus.
Apply for this Position
Ready to join ? Click the button below to submit your application.
Submit Application