Senior Data Scientist - Fraud Model Validation

Klarna, briefly At Klarna, we're building an everyday finance network, helping over 120 million consumers across 26 countries save time and money, and worry less about their finances. Working here means taking on problems most companies never get to solve, and being hands-on enough that the interesting part of the work lands with you, not someone else — you'll build with AI, not watch it happen. This is the stretch zone. Come find out what you're capable of.

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.

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.

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