14 set - Bardi
Klarna
OverviewIn 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.
ResponsabilitàWrite 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 fondamentaliProduction 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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