Senior Data Scientist
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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
- 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
- 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
- Bonus: 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
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