Senior Data Scientist - Fraud Model Validation

pIn this second-line role, you validate fraud models used to protect payments, logins, and identity at Klarna. You’ll reproduce results, stress-test methodologies, and ensure governance and production readiness across the full model lifecycle. You work closely with first-line teams to surface risks and maintain model trust at scale. You’ll shape validation tooling and agentic AI workflows to keep validation pace with rapid development. This is a mission-driven opportunity to strengthen fraud defenses at a large, data-driven fintech. /pullicompetitive compensation /lilihybrid/onsite work (2–3 days in office) /lilidiverse, inclusive culture /liliopportunity to work with cutting-edge AI /liliimpactful role in safeguarding payments /lilicareer growth and cross-team collaboration /liliAssess model performance using fraud-specific metrics and balance business trade-offs /liliReview large transaction datasets and feature pipelines for representativeness and leakage /liliEvaluate drift detection, retraining strategies, and production monitoring /liliAssess CI/CD and deployment controls (Docker, Jenkins, AWS) for model environments /liliEvaluate governance documentation, explainability, and regulatory compliance /liliValidate emerging techniques (graph networks, anomaly detection, GenAI-based systems) and document risks /liliCommunicate validation outcomes and risks to data scientists, ML engineers, and stakeholders /lili3+ years hands-on fraud modeling /liliFluency in Python and SQL; experience with PySpark or Spark /liliExperience with tree-based models (LightGBM), anomaly detection, graph/network models /liliExperience across ML lifecycle from feature engineering to deployment and monitoring /liliAbility to explain complex models and communicate to non-technical stakeholders /liliKnowledge of model risk governance, bias, fairness, and privacy considerations /liliExperience building or validating agentic AI workflows /liliBonus: advanced degree in quantitative field; domain experience in BNPL or payment products /liliMentor or lead validation discussions is a plus /liliability to challenge approaches constructively /lilidetail-oriented with risk awareness /liliLightGBM /lilianomaly detection /li /ul #J-18808-Ljbffr