Job Title

Role Overview: We are seeking a highly skilled professional with strong expertise inCredit Risk analyticsandMachine Learning (ML) model development . The ideal candidate will be responsible for building, validating, and optimizing predictive models that support credit decisioning, risk assessment, and portfolio monitoring across the lending lifecycle. This role requires deep analytical capabilities, strong statistical knowledge, and hands-on experience working with large-scale financial datasets. Key Responsibilities: Develop, validate, and enhancecredit risk models(PD, LGD, EAD, scorecards, underwriting models, early warning models, etc.). Build and deploymachine learning modelsfor credit decisioning, customer segmentation, fraud detection, and risk forecasting. Analyze credit portfolio performance to identify risk patterns, portfolio trends, and actionable insights. Work closely with product, underwriting, policy, and engineering teams to implement models into production. Conductdata exploration, feature engineering, and model performance monitoringusing large and complex datasets. Ensure adherence toregulatory standards , model governance guidelines, and documentation requirements. Collaborate with risk and compliance teams to ensure models meet audit, regulatory, and internal risk management expectations. Create comprehensive model documentation including methodology, assumptions, performance metrics, and validation results. Required Skills & Experience: 4–8 years of experience inCredit Risk Analytics ,Risk Modeling , orData Sciencewithin financial services or fintech. Strong hands-on experience inML model development , including regression, classification, time series, and ensemble techniques. Proficiency inPythonorR , SQL, and data processing frameworks. Solid understanding ofcredit lifecycle , scorecards, bureau data, demographic/behavioral data, and financial risk indicators. Experience working withlarge datasetsand cloud platforms (AWS, GCP, or Azure). Strong knowledge ofmodel validation, monitoring, and governance frameworks . Excellent analytical reasoning, problem-solving, and communication skills. Preferred Qualifications: Experience withretail lending, unsecured lending, BNPL, credit cards, or SME lending . Exposure toMLOps , model deployment, and API integration frameworks. Familiarity with regulatory guidelines (e.g., RBI, Basel norms, IFRS9). Background instatistics, mathematics, computer science, data science , or related fields.