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# Senior/Lead Data Scientist -Fraud

- **Company:** Klarna
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Amazon S3, Artificial Neural Networks, Big Data, Biometrics, Continuous Integration, Information Engineering, Python (Programming Language), Machine Learning, Performance Tuning, Standard Sql, Software Engineering, Model-Driven Development, Scikit Learn, Information Technology, Low Latency, Xgboost, Machine Learning Operations, Docker, Jenkins
- **Published:** August 19, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2933552403-2

## About the Role

building real-time or near-real-time inference systems; batch pipelines alone are insufficient. Comfortable with large-scale datasets including hundreds of millions of transactions and high-dimensional feature spaces. Strong Python and SQL skills with hands-on experience in scikit-learn, LightGBM, Docker, Jenkins, and modern Python packaging. Self-motivated, fast-moving, and creative. You bring novel solutions where others reach for off-the-shelf tooling. Communicates precisely across technical and non-technical audiences including senior stakeholders. Degree in computer science, physics, applied mathematics, astrophysics, automatic control, mathematics, software engineering, electrical engineering, or a related quantitative field. Awesome to have Experience building end-to-end ML systems in early-stage startups or small greenfield teams. This is a strong positive signal. Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics,
beyond prototyping or fine-tuning. Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices. Experience mentoring or technically guiding other data scientists. Please include a CV in English. Curious to learn more about Klarna and what it's like to work here? Explore our career site!

## Description

deployment. A central part of this role is converting some of Klarna's existing rules-based fraud systems into sophisticated, model-driven architectures that operate at scale across hundreds of millions of transactions. You will build infrastructure from scratch, not maintain or extend existing frameworks. Working with engineers, analysts, and commercial stakeholders, you will translate ambiguous business problems into precise technical solutions and bring novel approaches such as graph networks, anomaly detection, and behavioural signals into production where they create real impact. Who you are End-to-end ML ownership across the full stack: data engineering, feature development, model design, training, low-latency production deployment, monitoring, and retraining. Strong instinct for when a model is ready for production and when it is not. Proven track record of building ML models and pipelines from scratch, not integrating or extending someone else's product or tooling. Experienced


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