Title : Applied Scientist - Credit Risk Analytics

Location : Gurugram / Noida

Experience : 5-7 Years

Education : B.Tech + M.Tech (Mandatory)

Role Overview :

We are looking for an Applied Scientist with strong expertise in Credit Risk Analytics, Machine Learning, and Statistical Modeling to build and deploy predictive models for lending, collections, fraud detection, and portfolio risk management.

Key Responsibilities :

- Develop Application Scorecards, Behavioral Scorecards, PD/LGD Models, and Underwriting Models.

- Build Delinquency Prediction Models, Early Warning Systems, Roll-Rate Models, and Vintage Analysis Frameworks.

- Design Fraud Detection and Anomaly Detection Models using supervised and unsupervised ML techniques.

- Perform feature engineering using WOE, IV, PSI, CSI, and model calibration techniques.

- Monitor model performance using AUC, Gini, KS, Gains/Lift, and stability metrics.

- Own the end-to-end model lifecycle including data extraction, feature engineering, model development, validation, deployment, and monitoring.

- Collaborate with Risk, Credit, Collections, and Business teams to solve complex business problems through data science.

Required Skills :

- 5-7 years of hands-on experience in Data Science / Machine Learning.

- Strong Python and SQL skills.

- Experience with Credit Risk Modeling and Financial Analytics.

- Expertise in Logistic Regression, XGBoost, LightGBM, Random Forest, and other ML algorithms.

- Experience working with large-scale datasets using PySpark.

- Strong understanding of Model Validation and Performance Monitoring.

- Knowledge of RBI guidelines and Model Risk Management is preferred.

Tools & Technologies : Python, SQL, PySpark, Pandas, Scikit-learn, XGBoost, LightGBM, Git, Jupyter, Tableau, Power BI.

Preferred Domain Experience : NBFC, Banking, Fintech, Credit Risk, Collections Analytics, Fraud Analytics, Portfolio Risk Management.