Job-ID: b0c60dd1-4c3b-4e0d-8e69-81bc Location: MilanSalary: 72950 - 93142 EURType: Full timePosted: Contact: , briefly At Klarna, were 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 — youll build with AI, not watch it happen. This is the stretch zone. Come find out what youre capable of. About the role 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. Youll 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 youll do Youll 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.



Youll review transaction datasets exceeding 100 million records and feature pipelines with hundreds of features for representativeness, leakage risk, and bias. Youll evaluate drift detection, retraining strategies, and production monitoring practices to confirm they catch degradation before it costs the business. Youll assess CI/CD and deployment controls — Docker, Jenkins, and the AWS SageMaker, S3, Athena, and Lambda environments models run in. Youll evaluate model governance documentation, explainability approaches, and compliance with regulatory expectations on model risk, fairness, and data privacy. Youll validate emerging techniques as first-line teams adopt them — graph networks, behavioral biometrics, anomaly detection, and GenAI-based systems. Youll document validation outcomes and communicate model risks directly to first-line data scientists, ML engineers, and business stakeholders. Who you are Youve 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 elses implementation choices, not just build your own. Youve worked across the full ML lifecycle — from feature engineering through production deployment and monitoring — and know where each stage tends to go wrong. Youre fluent in Python and SQL,



and youve used PySpark or Spark to process data at scale. Youve 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 whats wrong with it, and make that case clearly to both technical teams and senior stakeholders who arent. Premio points for An advanced degree (Masters 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. Youve 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. Things you should know before applying 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 candidates qualifications, skills, and experience. This is a second-line, independent validation position — youll work closely with first-line fraud data science and ML engineering teams, without reporting into them. 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 its like to work here? Explore our career site!

📌 Senior Data Scientist - Fraud Model Validation (Milano)
🏢 Klarna
📍 Milano