Hello! 🖐️ I am a Senior AI & Data Science Engineer with 5 years of experience. I excel in designing and implementing data-driven models tailored to client needs.

🔍 Areas of Passion:

• Advanced NLP techniques including:
  • LLMs 🤖
  • Qlora 🦄
  • LORA 🦜
  • RAG (retrieval-augmented generation) 🕸️
• 🤿 Currently, I’m immersed in exploring augmented data generation techniques for NLP tasks.
• 💬 Got queries about NLP, AI, or machine learning? Don't hesitate to ask! 🧠

• Large Language Models (LLMs) 🤖
• Data Analysis 📊
• Natural Language Processing 📝
• Dashboard Realization 📈
• Business Intelligence 📉
• Data Management & Transformation ⚙️
• Machine Learning 🦾
• Deep Learning 🧬
• Data Visualization 🖼

Whether you're looking to tell a compelling story with your data, develop a real-time dashboard with KPIs for monitoring your company's health, or explore natural language processing solutions 🗣, I can assist you in your next venture.

Over the years, I've successfully managed projects worth +€1M across 12+ countries 🌍 for major clients including the European Parliament, Kering, Atos, Renault Nissan Mitsubishi, Damart, and more. I wear multiple hats as a 🧑‍🔬 Data Scientist, 📊 Data Analyst, and 🧑‍💼 Project Manager with both functional and managerial expertise.

• Microsoft Certified: Power BI Data Analyst Associate (PL-300)
• Microsoft Certified: Data Analyst Associate (DA-100)
• Microsoft Certified: Azure Fundamental (AZ-900)

Location: Paris (October 2020)
Prize: €10,000

• Objective: Development of a prediction/recommendation platform based on AI.
• Description:
  • Prediction of the environmental impact of Kering's various activities throughout the supply chain.
  • Accurate evaluation of environmental impacts: resource depletion, biodiversity, greenhouse gases.
  • Decision support for designers, material researchers, and consumers in their choices to reduce the impact of luxury.
  • Automatic creation of predictive models from user-provided data or directly from Kering data and integrated native models.
  • Recommendation technique: Collaborative-based method, Content-based method, Hybrid method.
• Results: The team won 1st place for the best platform for predicting the environmental footprint of Kering's products.
• References:
  • [1] Kering organizes the first hackathon for sustainable luxury
  • [2] Kering Awards Core Project at Its First Sustainable Luxury Hackathon

Location: Paris (February 2019)

• Objective: Production of a Business Intelligence tool based on voice commands.
• Description:
  • Real-time monitoring of the actions of each sales agent on the different business units and real-time feedback.
  • Automation of the customer prospecting process by centralizing data.
  • Streamlining the Manager - Salesperson - Client relationship through an omnichannel approach based on high-value-added artificial intelligence.
• Results: This project allowed decision-makers to discover insights into their activity and monitor the actions of each salesperson in real-time.
• References:
  • [1] SKEMA News - ESIEA-SKEMA Digital Consulting Week
  • [2] Facebook Video
  • [3] 5ème édition de la Digital Consulting Week SKEMA x ESIEA
  • [4] Digital Consulting Week SKEMA ESIEA - LinkedIn

🎯 Objective: Automate order & credit return management for Thuasne with advanced NLP & LLMs.
📧 Created an email classification system using the Melusine tool.
🔍 Extracted order details like items, quantity using NLP & LLMs.
🛠️ Developed a prototype system handling email text & attachments.
🚀 Improved order processing efficiency & anomaly detection.
📊 Used explainability tools like LIMETextExplainer, ELI5NLP, SHAP, and AnchorsNLP.
💡 Optimized system performance with hyperparameter tuning.
🌐 Adapted the system to cater to different orthopedic domains.
📈 Result: Enhanced system efficiency & performance using advanced NLP techniques, LLMs, and Melusine tool.

📜 Documentary AI Fraud Demonstrator - Senior Data Scientist NLP
🎯 Objective: Develop a demonstrator for detecting anomalies, frauds, or inconsistencies in documents.
📝 Designed a demonstrator for document processing (insurance invoices).
🚫 Detected fraudulent patterns & document falsification.
🔎 Extracted key invoice fields & verified their consistency.
🕵️‍♂️ Identified potentially suspicious overbilling cases.
📈 Result: Significant improvement in fraud detection using AI, outperforming traditional OCR techniques.

🛍️ Part Forecasting: PFO – Technical Lead/ Technical Expert
🎯 Objective: Provide 18-month forecasts to suppliers, mitigating semiconductor crisis impact.
💻 Developed PFO architecture as a Streamlit web app hosted in Azure.
📊 Integrated data from Oracle Exadata Database.
☁️ Used Azure Data Factory for file transfer & processing tasks.
🚢 Containerized the PFO app using Docker & deployed to Azure Container Registry.
📈 Result: Enhanced inventory management & supplier collaboration, improving part prediction accuracy.

• Project Management 📅
  • Preparation 📝
  • Planning 🗓️
  • Management 📊
  • Evaluation 📈
  • Monitoring and Control of:
    • Resources 💰
    • Calendar 📆
    • Costs 💸
    • Scope 🎯
    • Risk 🚨
    • Quality 🌟
    • Requirements 📑
    • Value 💎
    • Satisfaction 😊
• Tools: TFS, MS Project, GANT, PERT