Senior Machine Learning Engineer
5 giorni fa
Milano, Lombardia, Italia
Klarna
Tempo pieno
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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
* 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