Senior Data Scientist - Fraud Model Validation

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

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