Overview In this role you lead the productionization of consumer credit underwriting models, turning data-science experiments into reliable, scalable production pipelines. You own the ML infrastructure and deployment, working with a cross‑functional team across Stockholm, Milan and Warsaw to grow Klarna’s in‑house data science capability in credit risk and fraud. You’ll build and maintain the end-to-end ML pipeline, from feature computation to retraining and monitoring, delivering robust models in production. This is a hands-on engineering role focused on delivering impactful, scalable solutions in a fast‑paced fintech environment.
Responsabilità
Write production Python to train credit underwriting models (tree-based models)
Build and maintain ML pipeline infrastructure from feature computation through retraining and monitoring Deploy models into production using tools like AWS SageMaker and ensure ongoing operation Troubleshoot end-to-end pipeline issues and own the lifecycle of deployed models Scale Klarna's in-house data science capability as the credit risk and fraud teams grow
Requisiti fondamentali
Production Python for ML: training models, not just prototyping Experience taking ML models/pipelines from development to production and owning them Hands-on with tree-based models Deployment and operation of ML workloads on AWS or equivalent cloud Understanding full SDLC and applying it to ML code Collaboration with data scientists to build reliable pipelines English communication (spoken and written) Clear communication Cross-functional collaboration Ownership and accountability Python for ML (production) Tree-based models AWS SageMaker or equivalent cloud deployment
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