Freelancer profile translated to English.

Description

With 12 years of experience in Consulting and managing Data, AI, and Generative AI products, I leverage my skills to optimize processes and create business value. Thanks to my cross-functional positioning and my techno-functional profile, I support organizations in designing and implementing innovative solutions aligned with their strategic objectives.

My user-centric approach and rigorous methods allow me to guarantee quality deliverables, respecting your contractual, application, and human commitments. My commitment and adaptability to processes, environments, and teams enable me to ensure smooth collaboration with all stakeholders for tangible and measurable results.

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

AG2R LA MONDIALE - GIE AG2R
Data Partner | Product Manager Data | Data Mesh & Governance Expert | Data Culture & Value Creation
BANKING AND INSURANCE
March 2025 - Today (1 year and 5 months)
Paris, France
In a federated Data Mesh & governance context – Data Train SAFe, I supported Corporate functions (Finance, Compliance, HR, Asset Management, Actuarial) during the implementation of the strategic decision to platform the AG2R La Mondiale group.
Support for Data Owners & Data Stewards
• Strategic consulting to resolve business issues related to data
• Formalization of a data roadmap aligned with stakeholder needs
• Definition of a clear product vision to promote data adoption and generate business value
Product Manager Role within the Data Train
• Steering product vision and roadmap in collaboration with business and data teams
• Backlog management, breaking down Epics into Features, prioritization based on business impact
• Close coordination with Product Owners, Data Partners, Architects, RTE, and Business Owners
• Active participation in agile ceremonies: PI Planning, demonstrations, retrospectives
Data Product Quality & Governance
• Guarantee of compliance, documentation, and adherence to standards (Quality by Design)
• Support for teams in mastering their data ownership responsibilities
Data Culture & Innovation
• Raising awareness among business units about the strategic uses of data
• Technological and regulatory watch to identify new innovation levers through data

Results: construction of the customer knowledge base roadmap, creation of several data products in various business domains. Technical environment: Google Cloud Platform (GCP), BigQuery, Data Build Tool (DBT), Semarchy, Elasticsearch, Airflow, Jira, Confluence, Agile SAFe
Product Discovery Product Delivery Product roadmap KPIs and Metric Definition and Monitoring SAFe
Getaround SAS - Noé
Product Manager
TRANSPORTATION
August 2024 - September 2024 (1 month)
Paris, France
I held the role of PM for Getaround during the Noé bootcamp with the support of Lead PM, Head of Product, and CPO. The challenge was to increase the repeat rate based on car quality.
Here's what I implemented:
• Product Strategy: exploration of strategic issues and Getaround's positioning against the competition in France
• Product Discovery: interviews with about ten users, data analysis (SQL, Excel)
• Prioritization: use of Use Case, Cost & Impact, and RICE frameworks for problems/solutions, MoSCoW for feature implementation
• Prototyping, definition of metrics to test, and A/B testing with real users in an iterative process
• Delivery: writing specifications for the tech team (user stories, workflows, edge cases)
• Final Pitch: presentation to Getaround's Lead Product
Product management Product Discovery UX/UI Designer User Research Définition et suivi des indicateurs (KPI)
Make.org
Product Manager
DIGITAL AND IT
June 2024 - July 2025 (1 year and 1 month)
Paris, France

Make.org wants to improve the response format of its LLMs to enhance user experience. It is in this context that I am involved to find the appropriate response format for user needs.
• Product Discovery of a GenAI product
• Understanding of the indexing method and search engines based on vector embeddings
• Optimization of prompt engineering to improve the LLM's RAG (Retrieval-Augmented Generation)
• Testing and comparison of LLMs: OpenAI (ChatGPT), Mistral, Claude, Llama, Chatsonic, Perplexity, Gemini, Copilot
• Collaboration with a product designer
• Sharing insights and discussions with the product team
• Product recommendations
• COMEX observation
Results: understanding the specifics of each LLM, user ranking of response formats. Technical Environment: Notion, Figma, Jira, MixPanel, Told, Microsoft Office 365
Product Discovery Product Delivery Priorisation (RICE, MosCow, ...) User Research Développement produit