Lead AI Validation
Pubblicato il 18-10-2026 - Generali Italia in Roma
Within the Group Data, Artificial Intelligence and Automation department, we are looking for a highly experienced Data Scientist / Data Engineer to lead AI Validation and Governance across both traditional Machine Learning and next-generation Generative and Agentic AI systems.
The Lead AI Validation & Governance (GenAI & ML) will play a pivotal role in defining, implementing, and scaling the Group's AI governance and model risk framework, ensuring that AI systems are robust, explainable, compliant, and fit for purpose in business‐critical contexts.
This role sits at the intersection of AI model validation, model risk management, and Responsible AI, providing independent oversight and technical leadership across the entire AI lifecycle—from design and development to deployment, monitoring, and periodic review.
In addition to its strategic and governance responsibilities, this role is expected to remain strongly hands‐on, actively contributing to the design, validation, and testing of AI models and systems.
The ideal candidate combines deep technical expertise with a pragmatic mindset, and is comfortable developing and reviewing model validation code; working directly with ML and GenAI pipelines; building prototypes, validation tools, and monitoring solutions; interacting with data, models, and APIs in a production‐oriented environment.
Key responsibilities
Perform independent validation and challenge of AI/ML and agentic/Generative AI systems across the full lifecycle, including model design, data quality, and performance (accuracy, robustness, explainability, fairness).
Align validation and governance practices with applicable regulations (e.g., EU AI Act), internal guidelines, and risk appetite, translating requirements into operational processes and controls.
Identify and assess key risks (bias, explainability, compliance), and define appropriate validation controls and mitigation actions.
Define and implement continuous monitoring and alerting mechanisms for AI s