Senior Data Scientist - Fraud Model Validation

**What You Will Do** * Perform independent end\-to\-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models. * Review and challenge first\-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit\-learn, LightGBM, graph models, anomaly detection techniques, GenAI components). * Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies. * Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC\-AUC, PR\-AUC, cost\-sensitive metrics, fraud rate capture, business impact trade\-offs). * Evaluate model stability, drift detection, retraining strategies, and production monitoring practices. * Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations. * Review large\-scale transaction datasets and feature pipelines (e.g., \>100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias. * Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy. * Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI\-based systems. * Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda). * Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python\-based validation libraries). * Collaborate closely with first\-line fraud data scientists, ML engineers, product, and business stakeholders to ensure transparent communication of model risks and validation findings. * Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and external regulatory expectations. * Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management. **Who you are** * Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering. * 3\+ years of hands\-on experience in fraud\-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc). * Strong expertise in machine learning methods used in fraud detection, including tree\-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques. * Deep understanding of the end\-to\-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage. * Strong programming skills in Python and SQL; experience with PySpark/Spark and large\-scale data processing. * Experience building agentic AI workflows. * Familiarity with cloud\-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows. * Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations. * Experience assessing model bias, fairness, explainability, and privacy risks. * Excellent analytical thinking and structured problem\-solving skills, with the ability to assess complex models and clearly articulate risks and limitations. * Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non\-technical audiences. * Ability to work independently while constructively challenging first\-line teams in a collaborative manner. **Awesome to have** * Experience in BNPL, credit cards, payments, or other transaction\-heavy financial products. * Experience validating models in highly regulated environments. * Experience mentoring junior validators or leading validation reviews. * Exposure to inference of rejected transactions and understanding of fraud/credit overlap. * Familiarity with AI governance frameworks and emerging AI regulatory requirements.