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.