Freelancer profile translated to English.
Back to original languageAbout Nidal
Looking for an expert in generative AI, Python, AI agents, LLMs, and AWS?
🎯 You've come to the right place.
I am a Data Scientist with over 6 years of experience.
I have worked in various sectors such as industry, energy, scientific research...
I have collaborated with companies such asVicat**, **EDF**, and **CNRS(the French National Centre for Scientific Research), distinguished by several Nobel Prizes and Fields Medals...
📜Certifications and Diplomas:
- Supervised Machine Learning: Regression and Classification (**Stanford University**)
- Machine learning in Python with scikit-learn (**Inria**)
- Microsoft Azure AI Engineer Associate (Microsoft)
- Building Agentic RAG with LlamaIndex (**DeepLearning.AI**)
- Serverless Agentic Workflows with Amazon Bedrock (**DeepLearning.AI**)
- Quality Mastery in Web Projects (**Opquast**)
💡I can assist you with:
- Creation and integration of conversational AI agents and LLMs
- Customization of models like GPT or Llama
- Feasibility study and specification of your needs
- Data collection, cleaning, and preprocessing
- Development and implementation of machine learning models and predictive algorithms
- Exploration and comparison of models for appropriate technological choices
- Python development of model training pipelines
- Deployment on a cloud provider such as AWS
- Proposal of metrics to evaluate and track the performance of deployed models
- Design of your AI solution architecture
🧠Technical Skills:
Transparency with my clients:
📝 Daily report sent by email at the end of the day on tasks performed.
Shall we discuss your project?
Contact me directly via the platform.
French
Native or bilingual
Can work on-site
Lyon (up to 50km), Paris (up to 50km), Marseille (up to 50km), Bordeaux (up to 50km), Toulouse (up to 50km)
Experience
- EDFData EngineerRAW MATERIALS INDUSTRYSeptember 2020 - June 2021 (9 months)Lyon, France
⚡ EDF: A key player in energy
EDF plays a central role in the energy sector, serving over 37 million customers. The company is distinguished by its 2,178 technological innovations. It plays a key role in energy transition and industrial automation.🌍 Project ContextTook over a critical application designed to automate the writing of complex network configurations (several hundred lines). This application, essential for configuring network infrastructures, was unusable after a year of unsuccessful development.My role was to debug, optimize, and finalize the application to meet the engineers' needs. I adopted an agile approach, prioritizing rapid deliveries to restore client confidence.💡 Key Contributions🔍 Project Recovery and Optimization:- Application analysis and debugging.
- Rewriting critical modules.
- Automated pipelines to generate complex configurations.
🤝 Collaboration with Engineers:- Regular discussions to understand their needs.
- Iterative testing and adjustments for an intuitive application.
- Agile approach to restore confidence.
- Rapid deliveries to meet client urgency.
🏆 Major Achievements:- Reduced time required for configuration writing.
- Delivery of an application adopted by 100% of the relevant teams.
- Elimination of redundant tasks, allowing engineers to focus on strategic missions.
⚙️ Technical Context:- PHP
- Symfony
- MySQL
- Workflow Automation
- Data Pipelines
- Agile
🔑 Transferable Skills:- Automation and process optimization to improve productivity.
- Interdisciplinary collaboration to meet user needs.
- Management of critical projects with an agile approach.
- Application of Data Engineering principles to structure and automate data.
- Groupe VicatData ScientistRAW MATERIALS INDUSTRYFebruary 2022 - Today (4 years and 4 months)Lyon, France
🏗️ Vicat: A pillar of construction materials
The Vicat Group, a key player in construction materials since 1853, operates in 12 countries. In 2024, it achieved a consolidated turnover of 2.916 billion euros. It stands out for its commitment to low-carbon cements.🌍 Context and MissionsAs a Data Scientist, I lead strategic Data Science and Machine Learning projects to improve product quality, optimize industrial processes, and enhance safety at production sites.💡 Key Contributions🤖 Gen AI Innovation:- Development of an internal conversational agent based on LLMs, deployed on AWS
- Automation of technical responses
- Facilitating access to internal data.
📈 Product and Sales Optimization:- Prediction of cement sales using time series models to adjust production and logistics. (Python and Scikit-Learn)
- Exploratory analysis and predictions to improve the quality of cements and manufacturing recipes. (Python, Scikit Time, Facebook Prophet)
👁️🗨️ Computer Vision and Automation:- Classification of concrete blocks and fire detection using computer vision, enhancing safety and quality control. (Python, Open-cv, PyTorch Facebook/detr-resnet-50, Deep Learning)
- Development of a fire detection model. (Python, open-cv, YOLO, CNN, DeepLearning)
💡 And other AI projects… For more details, contact me directly via Malt.🏆 Major Achievements:- Reduced loading times thanks to predictive models.
- Decreased cement quality variations through process optimization.
- Improvements in cement recipes and manufacturing processes.
⚙️ Technical Context:- Python
- LLM
- RAG
- AI Chatbot
- TensorFlow
- Scikit-learn
- OpenCV
- Snowflake
- AWS
- PyTorch
- Facebook models
- Scikit Time
- Agile/Scrum
- Pandas
- CNRS (Centre national de la recherche scientifique)Data EngineerRESEARCHDecember 2019 - September 2020 (9 months)Lyon, France
🔬 CNRS: World leader in scientific research
The CNRS is a world leader in scientific research. It brings together over 32,000 employees and 1,100 laboratories. Several of its works have been awarded Nobel Prizes and Fields Medals. The Lumière et Matière Institute (ILM) is affiliated with the University of Lyon 1. It excels in the fields of nanoscience and renewable energy.🌍 Project ContextI joined a team composed of a developer, the director of ILM, and the HR director. We worked on an HR application to manage researchers. The objective was to:- Centralize administrative data.
- Automate processes.
- Provide analytical tools to improve efficiency.
💡 Key Contributions🔧 Data Automation and Management:- Design of pipelines to automate the processing of researcher-related information.
- Creation and optimization of PostgreSQL databases.
📊 Visualization and Reporting:- Development of interactive dashboards to visualize flows, resources, and deadlines.
- Provision of actionable KPIs for decision-makers.
🤝 Interdisciplinary Collaboration:- Discussions with researchers to understand their data management and analysis needs.
- Exploration of projects combining development and data.
🏆 Major Achievements:- Reduction of administrative delays through automation.
- Adoption of the application by over 50 users, with improved traceability and reduced human error.
- Implementation of dashboards facilitating strategic decisions.
⚙️ Technical Context:- Python
- Django
- PostgreSQL
- Data Pipelines
- Interactive Dashboards
- HR Management
🔑 Transferable Skills:- Data automation and processing.
- Development of interactive visualizations for reporting.
- Collaboration with interdisciplinary teams.
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Education
- Data ScientistStanford UniversityStanford University, est reconnue pour son excellence dans les sciences informatiques et son rôle pionnier en intelligence artificielle depuis sa création en 1885. Située au cœur de la Silicon Valley, elle est un catalyseur d'innovation a travers des initiatives telles que le Stanford Artificial Intelligence Laboratory et la recherche de pointe en IA et technologies émergentes.
- Data ScientistMicrosoft AI SchoolL'École IA Microsoft est une institution dédiée à la formation de data scientists depuis 2018. Elle a formé 905 apprenants répartis en 50 promotions à travers la France, avec un taux de sorties positives de 98,45%.
Certifications
- Supervised Machine Learning: Regression and ClassificationStanford University
- Building Agentic RAG with LlamaIndexDeepLearning.AI