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) 🤖
• MLE | MLOPS 🚀
• 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

📊 Senior Data Scientist | LLM expert - TOTAL ENERGIE, Paris

Objective: SQL Chatbot for Database Management

• Led the development of an advanced SQL chatbot to enhance database querying and data visualization using NLP and LLMs.
• Architected the SQL chatbot leveraging LangChain and OpenAI's GPT-4, enabling intuitive data visualizations and command translations.
• Enhanced model efficiency & performance using LLMs.
• Designed a full-stack solution hosted on Azure SQL Database, integrating Azure Bot Services and Azure Language Understanding (LUIS) for a dynamic user interface.
• Optimized model performance with hyperparameter tuning.
• Adapted the model to cater to different building types.
• Result: Enhanced model efficiency & performance using advanced NLP techniques & LLMs.
• Technical Stack: Python, Azure, PostgreSQL, LangChain, Streamlit, Gitlab, AzureDevOps

🧑‍💼 IFACI – Expert LLM

Objective: AI GEN Assistant for Auditing Profession

• Role: AI GEN Assistant for Auditing Profession
• Developed a generative AI base and an assistant for field agents, enhancing natural language understanding capabilities using Spacy.
• Utilized a combination of Azure, LanceDB, RAG, Vector Store, HNSW, and Hybrid search technologies to optimize performance.
• Result: Improved efficiency and accuracy in the auditing process through the implementation of advanced AI techniques.
• Technical Stack: Python, Azure, Neo4J, AzureDevOps, LanceDB, Chroma, Milvus, MLFlow, HNSW, Hybrid search

🧑‍💼 GSF – Senior Data Scientist / MLE Architect

Objective: Predictive Maintenance for Cleaning Services

• Role: Workplace Accident Prediction + Explainability
• Spearheaded the implementation of CI/CD pipelines and developed a system for the evaluation of prediction explainability.
• Utilized Azure Machine Learning, Azure Datafactory, Azure Pipelines, Azure Devops, and integrated with Snowflake and Control-M for workflow management.
• Result: Improved workplace safety through accurate accident prediction and enhanced model explainability.
• Technical Stack: Python, Azure, MLFlow, Databricks, Pandas, Terraform, TensorFlow, Scikit-learn, PyTest, Docker, Azure Machine Learning, Azure Datafactory, Azure Pipelines, Azure Devops, Snowflake, Control-M

📧 Expert NLP LLM / Senior Data Scientist - THUASNE

Objective: Email Order System (1K orders/day)

• Created an email order management system and a multimodal model for information extraction employing BERT, Azure, ChatGPT, NLP, LLMs, and Melusine.
• Enhanced the system with Azure Document AI for advanced document processing.
• 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.
• Technical Stack: BERT, Azure, OpenAI, NLP, LLMs, Melusine, Azure Document AI, Scikitlearn,Docker

📨 Senior Data Scientist - ADELAIDE

Objective: Automatic Email Processing (10K emails/day)

• Developed an explainability module for email classification and automatic responses using open-source tools such as Melusine, LIMETextExplainer, ELI5NLP, SHAP, AnchorsNLP, and integrations with Hugging Face and RASA.
• Managed version control and continuous integration using Git and CI/CD practices.
• Result: Streamlined email processing and improved response accuracy through the implementation of advanced NLP techniques and explainability tools.
• Technical Stack: Melusine, LIMETextExplainer, CNN, ELI5NLP, SHAP, AnchorsNLP, Hugging Face, RASA, Git, CI/CD

🕵️‍♂️ Expert LLM - ACOSS/URSSAF/CNAF/CNAM

Objective: Documentary AI for Social Fraud Prevention

• Developed a demonstrator for multimodal processing of large data volumes using Transformers, LayoutLM, OCR, NLP, and Topic Modeling to detect fraud.
• Result: Enhanced fraud detection capabilities through the implementation of advanced AI techniques for multimodal data processing.
• Technical Stack: Transformers, OpenCV, PyTorch, CNN LayoutLM, OCR, NLP, Topic Modeling

🕵️‍♀️ Senior Data Scientist - Quantmetry, Paris

Objective: Documentary AI Fraud Demonstrator

• 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.
• Technical Stack: Python, Azure, OCR, OpenCV, PyTorch, CNN, NLP, Machine Learning, Scikitlearn, Docker

🚗 Senior Data Scientist - Stellantis, Paris

Objective: Part Forecasting: PFO – Technical Lead/ Technical Expert

• Provided 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.
• Technical Stack: Streamlit, MLFlow, Airflow, Terraform, PyTest, Databricks, Azure, Oracle Exadata Database, Azure Data Factory, Docker, Azure Container Registry

💸 Project Manager / Technical Expert - ATOS, Grenoble

Objective: Travel & Expense Dashboard Atos (€10M+/ year)

• Developed a KPI dashboard to monitor Atos' expenses in real-time with geolocation and carbon footprint of travel.
• Recovered +10% VAT + billable expense reports (+€1 million annual gain).
• Gained more than 3214 hours of work per year.
• Result: Automation of weekly reports, significant cost savings, and improved efficiency through real-time expense monitoring and analysis.
• Technical Stack: Pandas, Matplotlib, Numpy, Scikit-learn, Jupyter Notebook, Power BI

🗣️ Lead Data Scientist - ATOS, Grenoble

Objective: R&D – Expressive TTS System

• Collected and adapted a large corpus of interactive behaviors in English (LJ Speech) and French (MAILABS).
• Developed and trained an expressive TTS system based on the Tacotron2 model by NVIDIA.
• Implemented a methodology for evaluating the learning quality of the prototype based on the distribution of lengths (number of spectrogram frames) of the predicted clips compared to the originals.
• Prepared a scientific paper: "Linking Utterances via Punctuations for Improved End-to-End Speech Synthesis".
• Captured the variability of styles and emotional state and their syntheses to the user profile for better prediction of speech synthesis applied to the text-to-speech (TTS) system.
• Result: Improved robustness and accuracy of TTS e-spectrogram generation, control and generation of styles, verbal behaviors, and prosody based on the user.
• Technical Stack: Pytorch, Tensorflow, Python, LSTM, Transformers, Attention Mechanism

🚗 Data Scientist - Renault Nissan Mitsubishi, Paris

Objective: Industry Automobile – Home to Car Next Generation Alliance

• Realized prototypes and developed the first generation of voice assistants of the Renault Nissan Mitsubishi alliance.
• Integrated the Google Assistant with Nissan cars to receive information from the car and control it remotely from your phone or from a Google Home.
• Connected to the authentication servers of the RNM Alliance and complied with cybersecurity specifications.
• Deployed Alexa and Google Actions project environments fully configured and ready to use.
• Documented user journey to configure the service.
• Result: Launched these features with the Nissan Juke at the 2019 Frankfurt Motor Show.
• Technical Stack: Python, Azure, Tensorflow, Keras, Dialogflow, Luis, Reddit, Alexa Skill, Bot Framework

🏨 Data Scientist - ATOS (European Parliament), Grenoble

Objective: Service – SAMBOT an intelligent conversational agent for room reservation (+3000 users)

• Developed a multilingual chatbot for room reservation in natural language (text and voice).
• Implemented a recommendation system based on user habits, locations, and room occupancy.
• Paired with Outlook calendars & email systems (Skype).
• Documented functional, technical, and user journey aspects.
• Result: Realized room reservations in record time considering user habits.
  Technical Stack: Python, Azure, OCR, OpenCV, PyTorch, CNN, NLP, Machine Learning, Scikitlearn, Docker

• 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