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Medium due to niche credit-ML specialization, metro location, and recognizable employer.
High because credit-risk modeling demands domain-specific lending, bureau data, and governance experience.
High due to explicit years requirement, mandatory credit-modeling domain expertise, and specific tech stack.
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Manage credit and fraud models throughout their lifecycle including development, evaluation, validation, monitoring, implementation testing, and documentation.
Lead model monitoring efforts, perform root cause analysis for performance issues, and coordinate with stakeholders to implement corrective actions.
Own the model risk management process as single point of contact, collaborating with cross-functional global teams and supporting strategic business initiatives.
Bachelor's degree in quantitative field (Risk, Economics, Finance, Mathematics, Statistics, Engineering) with minimum 5+ years' analytical/quantitative experience in consumer lending or 7+ years without degree.
Experience in credit risk and fraud model development end-to-end including application scorecards and fraud detection models.
Proficiency in Python, Pyspark, SAS/SQL and reporting tools like Tableau; Experience with AWS for ML lifecycle including data processing, feature engineering, model development, and deployment.
Experience leading small teams; Work hours aligned with US Eastern Time availability (06:00 AM–11:30 AM ET); flexibility with some onsite presence in Indian Regional Engagement Hubs.
Experienced leader capable of managing end-to-end credit and fraud modeling projects including technical and operational oversight.
Strong quantitative and technical skills with hands-on experience in machine learning techniques like XGBoost, Random Forest, LightGBM and familiarity with transactional and bureau data sources.
Comfortable working across global teams and managing multiple projects with complexity, delivering within deadlines while ensuring regulatory and model governance compliance.