◦ Functional Retrodocumentation for a large telco company: Developed a Generative AI-based Proof of Concept reducing the retrodocumentation process time by 66%, optimizing IT legacy systems transition.◦ AI-driven Data Quality Management Asset: Conceptualized and delivered a strategic asset demonstrating future AI and GenAI applications in data quality management.◦ Cybersecurity Initiative for GenAI with Google Mandiant: Led the development of a GenAI-enhanced banking app, which was security tested by Google Mandiant to identify new generation cybersecurity risks.

In charge of providing visibility on key topics triggering a successful deployment of the Track&Trace project and also to carry out a state-of-the-art end to end diagnostic of the vehicle tracking data quality. Track&Trace is an ambitious international supply chain project aiming at real-time tracking of vehicles on their logistic journey from the factory door to the dealership door. It aspires to provide customers with an Amazon like experience and is part of a broader perspective that focuses on making Stellantis number one in customer satisfaction by 2030.Key achievements:◦ Data quality analysis: Analyzed the data quality of the current vehicle tracking system and identified businessprocesses causing discrepancies.◦ Real time dashboards creation: Created real-time tracking quality monitoring dashboards for service providers toguide the operational management of Track&Trace deployment.◦ Data analysis: Analyzed the completeness and quality of end-of-delivery declarations in the context of overhauling thecustomer billing system.

This research project, in collaboration with the fluid mechanics (LMFA) and tribology (LTDS) laboratories of ́Ecole Centrale de Lyon, aimed to characterize milk foams and optimize their production, taking into account various quality criteria.