Remote: Hybrid

Applied Data Scientist – Credit RiskLocation: Gurgaon (Hybrid)Experience: 4–7 YearsRole OverviewWe are looking for an Applied Data Scientist – Credit Risk with solid expertise in Credit Risk Modeling, Machine Learning, Statistical Modeling, and Risk Analytics within the BFSI/Lending domain. The ideal candidate should have hands-on experience building and deploying credit scorecards, behavioral models, and risk solutions using advanced ML techniques.Key ResponsibilitiesDevelop and optimize Credit Risk Models, Application Scorecards, Behavioral Models, PD, LGD, and ECL frameworks.Build predictive models using Logistic Regression, GLM, XGBoost, and Machine Learning techniques.Apply PCA, K-Means Clustering, and other analytical techniques for customer segmentation and risk assessment.Work with large-scale datasets, perform feature engineering, EDA, and model validation.Leverage Credit Bureau Data and lending datasets to improve risk decisioning.Ensure compliance with BASEL-II, IFRS9,



and regulatory risk frameworks.Collaborate with Product, Risk, Engineering, and Business teams to deliver production-grade AI/ML solutions.Mentor junior team members and contribute to model governance and best practices.Required Skills4–7 years of experience in Data Science, Credit Risk Analytics, or Machine Learning within BFSI/Lending.Strong expertise in:Credit Risk ModelingApplication & Behavioral ScorecardsPD, LGD, ECLBASEL-II & IFRS9Credit Bureau DataHands-on experience with:PythonSQLPandasSpark / Py SparkScikit-LearnXGBoostTensor Flow / Py TorchStrong understanding of:Logistic RegressionGLMPCAK-Means ClusteringStatistical Modeling & Machine LearningGood to HaveExposure to Generative AI, Agentic AI, RAG, LLMs, Prompt Engineering, and Model Governance.Experience with Deep Learning in financial services use cases.Domain Preference: BFSI, Banking, Fin Tech, NBFC, Lending Platforms.

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