Senior Data Scientist

12 ore fa

Varese, Lombardia, Italia Klarna Tempo pieno
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