Senior Data Scientist
Pubblicato il 01-10-2026 - MindMatch in Italia
Job-ID: b0c60dd1-4c3b-4e0d-8e69-81bc Location: MilanSalary: 72950
Possiede le competenze e l'esperienza giuste per questo ruolo? Continui a leggere per scoprirlo e invii la sua candidatura.
- 93142 EURType: Full timePosted: Contact: , 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.
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.
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 10