Senior Data Scientist - Fraud Model Validation

First-line fraud teams at Klarna build models against real-time attacks on payments, logins, and identity - trained on transaction volumes north of 100 million records and pipelines with hundreds of features. Your job is to make sure those models actually hold up: independently reproducing results, building challenger models, and stress-testing every assumption from data pipeline to production deployment before a model earns trust at scale.

This is a second-line position, reviewing methodologies built with scikit-learn, LightGBM, graph models, anomaly detection, and increasingly GenAI-based components. You’ll also build your own tooling - agentic AI systems that read model documentation and code and surface risks automatically, so validation keeps pace with how fast first-line teams ship.

The scope spans the full model lifecycle: data integrity and feature engineering, conceptual soundness, deployment design across Docker, Jenkins, and AWS, and the monitoring and drift detection that keeps a model honest after launch.

What You’ll Do

• You’ll assess model performance using fraud-specific metrics - precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, and fraud capture rate - and weigh each against its real business trade-off.
• You’ll review transaction datasets exceeding 100 million records and feature pipelines with hundreds of features for representativeness, leakage risk, and bias.
• You’ll evaluate drift detection, retraining strategies, and production monitoring practices to confirm they catch degradation before it costs the business.
• You’ll assess CI/CD and deployment controls - Docker, Jenkins, and the AWS SageMaker, S3, Athena, and Lambda environments models run in.
• You’ll evaluate model governance documentation, explainability approaches, and compliance with regulatory expectations on model risk, fairness, and data privacy.
• You’ll validate emerging techniques as first-line teams adopt them - graph networks, behavioral biometrics, anomaly detection, and GenAI-based systems.
• You’ll document validation outcomes and communicate model risks directly to first-line data scientists, ML engineers, and business stakeholders., * Working together: we value co-located teams; most teams currently meet in the office 2-3 days per week, and this varies by team and can change over time.
• Non-obvious backgrounds are welcome. Diversity of skills, perspectives, and backgrounds is how we create, innovate, and disrupt like no other.
• Final compensation will be based on the candidate’s qualifications, skills, and experience.
• This is a second-line, independent validation position - you’ll work closely with first-line fraud data science and ML engineering teams, without reporting into them.

• You’ve spent 3+ years hands-on in fraud-related modeling - transaction fraud, account takeover, identity fraud, or payments fraud.
• You know tree-based models like LightGBM, anomaly detection techniques, and graph or network models well enough to challenge someone else’s implementation choices, not just build your own.
• You’ve worked across the full ML lifecycle - from feature engineering through production deployment and monitoring - and know where each stage tends to go wrong.
• You’re fluent in Python and SQL, and you’ve used PySpark or Spark to process data at scale.
• You’ve built agentic AI workflows - not just used off-the-shelf tools, but designed the automation yourself.
• You understand model validation principles and model risk governance well enough to assess bias, fairness, explainability, and privacy risk, not just accuracy.
• You can take a complex model apart, explain what’s wrong with it, and make that case clearly to both technical teams and senior stakeholders who aren’t.

Bonus points for

• An advanced degree (Master’s or PhD) in a quantitative field such as data science, statistics, mathematics, computer science, physics, or engineering.
• Experience in BNPL, credit cards, or other transaction-heavy payment products.
• You’ve mentored junior validators or led validation reviews.
• Exposure to inference on rejected transactions and how fraud risk and credit risk overlap.
• Familiarity with AI governance frameworks and emerging AI regulatory requirements.

Please include a CV in English. Concrete beats comprehensive - what you built, what it did, what it cost. Curious to learn more about Klarna and what it’s like to work here? Explore our career site!