Senior/Lead Data Scientist -Fraud

Overview

In this role you own the full ML lifecycle to build and productionize fraud-detection models at a scale that touches hundreds of millions of transactions. You will transform rules-based approaches into model-driven systems and create infrastructure from scratch, collaborating with engineers, analysts, and stakeholders. You’ll explore advanced techniques such as graph networks, anomaly detection, and behavioural signals to deliver measurable impact. This is a fast-paced, hands-on opportunity to shape Klarna’s fraud capabilities in a startup-like environment at scale.

Responsibilities

• Own end-to-end ML pipeline from data engineering, feature development, model design, training to low-latency production deployment
• Develop and productionize models for fraud detection at scale across hundreds of millions of transactions
• Convert existing rules-based systems into model-driven architectures
• Build infrastructure from scratch and collaborate with engineers, analysts, and stakeholders
• Experiment with advanced techniques (graph networks, anomaly detection, behavioural signals) and bring them into production
• Monitor, retrain, and maintain deployed models in production
• Translate ambiguous business problems into precise technical solutions

Key requirements

• End-to-end ML ownership across data engineering, feature development, modeling, deployment, monitoring, and retraining
• Proven track record of building ML models and pipelines from scratch
• Experience with real-time or near real-time inference systems
• Comfort with large-scale datasets and high-dimensional features
• Strong Python and SQL skills with hands-on experience in scikit-learn, LightGBM, Docker, Jenkins, and modern Python packaging
• Degree in a quantitative field (CS, physics, maths, etc.) or related
• Curiosity, grit, and ability to communicate with senior stakeholders

• Self-motivated
• Fast-moving
• Creative problem-solver
• Python
• SQL
• scikit-learn

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