Senior Data Scientist - Fraud Model Validation
Pubblicato il 18-10-2026 - Klarna in Milano
Overview In 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.
Retribuzione / Benefits competitive compensation hybrid/onsite work (2–3 days in office) diverse, inclusive culture opportunity to work with cutting-edge AI impactful role in safeguarding payments career growth and cross-team collaboration Responsabilità Assess model performance using fraud-specific metrics and balance business trade-offs Review large transaction datasets and feature pipelines for representativeness and leakage Evaluate drift detection, retraining strategies, and production monitoring Assess CI/CD and deployment controls (Docker, Jenkins, AWS) for model environments Evaluate governance documentation, explainability,
and regulatory compliance Validate emerging techniques (graph networks, anomaly detection, GenAI-based systems) and document risks Communicate validation outcomes and risks to data scientists, ML engineers, and stakeholders Requisiti fondamentali ~3+ years hands-on fraud modeling ~ Fluency in Python and SQL;
experience with PySpark or Spark ~ Experience with tree-based models (LightGBM), anomaly detection, graph/network models ~ Experience across ML lifecycle from feature engineering to deployment and monitoring ~ Ability to explain complex models and communicate to non-technical stakeholders ~ Knowledge of model risk governance, bias, fairness, and privacy considerations ~ Experience building or validating agentic AI workflows ~ Premio: advanced degree in quantitative field; domain experience in BNPL or payment products ~ Mentor or lead validation discussions is a plus ~ strong communication skills ~ ability to challenge approaches constructively ~ detail-oriented with risk awareness ~ LightGBM ~ anomaly detection ~ graph models