Freelancer profile translated to English.
Back to original languageAbout Arnaud
I help healthcare industry players (pharma, biotech, medtech) transform their data into levers for performance, quality, and compliance. My dual background as a Pharmacist and Chemical Engineer allows me to bridge the gap between field requirements (GxP, quality, operations) and the power of data & AI technologies.
âś… Need to leverage your data to improve your processes?
âś… An automation, visualization, or predictive modeling project?
âś… A complex problem to solve with artificial intelligence?
I design concrete, robust, and understandable solutions, in close collaboration with the business units.
From ideation to model industrialization, including data exploration, validation, and team support, I put data to work for your industrial challenges.
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🔧Key Skills
🔹Python, SQL, R, VBA – Development & Automation
🔹Machine Learning / Deep Learning – Scikit-learn, TensorFlow, NLP, Computer Vision
🔹Data Analysis & DataViz – Power BI, Tableau, Streamlit
🔹Industrial Statistics & Process Modeling – SPC, SAS, Matlab, Minitab
🔹Data Management & Governance
🔹Web scraping & Text processing (NLP)
🔹Validation of computer systems in a regulated environment
🔹Support & Pedagogy – Team Training & Change Management
French
Native or bilingual
English
Fluent
Japanese
Basic
German
Basic
Can work on-site
Orléans (up to 50km), Paris (up to 50km), Lyon (up to 50km), Rouen (up to 50km), Strasbourg (up to 50km)
Experience
- Johnson & JohnsonData Analyst Power BIPHARMACEUTICALS INDUSTRYJanuary 2025 - March 2025 (2 months)→ Mission Objective: Develop a Power BI dashboard consolidating internal (internal databases, SharePoint, CRM) and external sources to assess supplier quality risk and optimize supplier management strategy.→ Context: Cross-functional project, reporting to global.→ Solution Implemented:- Identify user needs and formalize wireframes.- Design a multi-dimensional analysis model.- Deploy the solution into production via Power BI Services, with data flow automation, automatic updates, and access security.→ Result Achieved: Provision of a visual and interactive tool facilitating supplier risk assessment at various levels (management, platforms, cross-functional teams). Improved strategic management, better anticipation of quality risks, and finer adjustment of supplier action plans.
- Merck GroupPython Developer - Process ModelingPHARMACEUTICALS INDUSTRYFebruary 2024 - April 2024 (2 months)Orléans, France→ Mission Objective: Model a dry granulation process based on production data.→ Context: Reporting to the production team.→ Solution Implemented:- Develop an interactive Python tool with a Dash interface for visualizing, exploring, and comparing production curves.- Create a hybrid model combining process expertise and historical data analysis.- Train a predictive model to anticipate quality risks based on production parameters.- Deploy the tool in the production environment for batch analysis.→ Result Achieved: Provision of a batch analysis support tool enabling rapid investigations, significant reduction in deviations, and overall improved process control.
- Ipsen PharmaProject Manager - Clinical Data ManagementPHARMACEUTICALS INDUSTRYFebruary 2023 - November 2024 (1 year and 8 months)Les Ulis, France→ Mission Objective: Implement a monitoring and management tool for clinical studies/data, ensuring regulatory compliance and adoption by business teams.→ Context: Reporting to the Standards & Systems department. Close collaboration with: Product Owner, Quality Assurance, Developers, IT, CROs.→ Solution Implemented:- Agile deployment of a clinical data monitoring solution (Python, SQL, Cloud, Tableau).- Definition and execution of the version validation strategy (risk analysis, IQ/OQ/PQ, UAT).- Strong involvement in user needs assessment, change management, and functional administration of data and the tool.→ Result Achieved: Go-live with smooth adoption of the tool, providing a global, near real-time view of the study portfolio. Improved traceability and quality of clinical data, and secured validation processes in a GxP regulated environment.
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Education
- Data ScientistOpenClassRoom - Centrale SupelecLa formation de Centrale Supélec me permet de maitriser les algorithmes d'apprentissage automatique (supervisés / non supervisés). C'est une formation, outre les cours théoriques, qui porte sur la réalisation de 9 projets professionnels de data scientist.
- Pharmacist - Chemical Engineer (ENSIC)Université de Lorraine2010Double diplôme de Pharmacien + Ingénieur chimiste à l'ENSIC, école spécialisée dans le génie des procédés.
Certifications
- Executive certificate in Lean Six Sigma Black BeltCentrale Supélec2015
- Lean Six Sigma Green BeltSIGMA PLUS2013