14 set - Bardi
Klarna
OverviewIn this role you own end-to-end fraud ML solutions from raw data to production, shaping models that operate at scale across hundreds of millions of transactions.
You will transform rules-based fraud systems into model-driven architectures and build infrastructure from scratch.
You'll work with engineers, analysts, and stakeholders to translate business problems into precise technical solutions, exploring approaches like graph networks and anomaly detection.
This is a fast-moving environment that values ownership, impact, and building best-in-class systems.
ResponsabilitàOwn end-to-end ML pipeline from data engineering to production deployment and monitoring
Convert rules-based systems into scalable, model-driven architectures
Develop feature engineering, model design, training, and real-time/inference pipelines
Collaborate with engineers, analysts, and commercial stakeholders to translate business problems into technical solutions
Experiment with graph networks, anomaly detection, and behavioural signals in production
Build infrastructure from scratch and ensure low-latency, high-throughput deployment
Perform model evaluation, monitoring, and retraining in a production environment
Requisiti fondamentaliEnd-to-end ML ownership across data engineering, feature development, model design, training, deployment, monitoring, and retraining
Experience building ML models and pipelines from scratch
Experience with real-time or near-real-time inference systems and large-scale datasets
Strong Python and SQL skills; hands-on with scikit-learn, LightGBM, Docker, Jenkins, and modern Python packaging
Ability to communicate with technical and non-technical stakeholders
Degree in computer science, physics, applied mathematics, astrophysics, automatic control, mathematics, software engineering, electrical engineering, or a related quantitative field
Self-motivated
Fast-moving
Creative
Python
SQL
scikit-learn
16 set - Lissone
Adecco Italia
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