Applied Data Scientist

Role Overview We are seeking an Applied Data Scientist with strong expertise in Credit Risk Modeling to build scalable AI/ML solutions for lending and risk decisioning. The ideal candidate should have hands-on experience in BFSI/Lending, statistical modeling, machine learning, and credit risk frameworks. Key Responsibilities Build and deploy Application Scorecards, Behavioral Models, and Portfolio Risk Models.Develop PD, LGD, and ECL models aligned with BASEL II and IFRS9.Apply Logistic Regression, GLM, XGBoost, and other ML techniques for credit risk.Perform feature engineering, EDA, and large-scale data processing using SQL, Spark/PySpark.Use PCA and K-Means for customer segmentation and risk analysis.Collaborate with Product, Engineering, and Business teams to deliver production-ready ML solutions.Improve model performance, explainability, and governance.Mentor junior team members and contribute to ML best practices.Required Experience 47 years in Applied Data Science, Risk Analytics, or Credit Risk Modeling within BFSI/Lending.Strong experience with Credit Bureau data, scorecards, behavioral models, and regulatory risk frameworks.Hands-on knowledge of Machine Learning, Statistical Modeling, and Deep Learning.Exposure to Generative AI, RAG, Agentic AI, Prompt Engineering, or LLMs is a plus.Technical Skills Programming: Python, SQLData Processing: Pandas, Spark/PySparkML Libraries: Scikit-learn, XGBoost, TensorFlow, PyTorchModeling: Logistic Regression, GLM, XGBoost, PCA, K-MeansDomain: Credit Risk, PD, LGD, ECL, BASEL II, IFRS9, Credit Bureau Data .