15 set - Varese
Klarna
Overview You will perform end-to-end validation of fraud detection ML models, covering data, features, models, deployment, and monitoring. You’ll develop challenger approaches and scrutinize methodologies used by first-line teams. You will build agentic AI tools to automate validation workflows and surface risks. Your work directly supports governance, regulatory expectations, and responsible deployment in a fast-paced payments environment.
Responsabilità
Validate end-to-end fraud ML models, including data integrity, features, deployment design, and monitoring Develop challenger models and critique first-line methodologies and implementations Build and deploy agentic AI tools to automate validation workflows and surface risks Assess model performance using fraud-specific metrics and business impact trade-offs Evaluate data representativeness, leakage risks, bias, and large-scale feature pipelines Review model governance, explainability, privacy, and regulatory compliance Assess CI/CD controls, deployment processes, and cloud environments Develop and maintain validation frameworks and monitoring tools Collaborate with data scientists, ML engineers,
product, and business stakeholders Document validation outcomes in line with governance standards and regulations Stay updated on fraud typologies, ML/AI techniques, and regulatory developments
Requisiti fondamentali
Advanced degree in a quantitative field (Master’s or PhD) 3+ years of hands-on fraud modeling experience Strong ML methods for fraud detection (tree-based models, anomaly detection, graph models) Deep ML lifecycle expertise from design to production monitoring Strong Python and SQL; PySpark/Spark Experience with agentic AI workflows Familiarity with cloud ML platforms (AWS SageMaker, Lambda, S3, Athena) and deployment Knowledge of model validation, governance, and regulatory expectations Experience assessing bias, fairness, and privacy risks Strong communication and ability to explain risks to senior stakeholders Ability to work independently while constructively challenging teams analytical thinking clear communication collaborative mindset Python SQL PySpark
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