Senior Data Scientist - Fraud Model Validation
Pubblicato il 13-10-2026 - 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