À propos de Amine
Freelance Data Science & IA | Machine Learning | RAG & Agents IA | Python
Data Scientist spécialisé en Machine Learning, IA Générative et systèmes de Recherches Augmentées (RAG), je vous accompagne dans la conception et l’optimisation de vos modèles intelligents. Fort d’une expertise en Python, NLP, Scikit-Learn, Scala, Hadoop et PySpark, j’ai travaillé sur des projets alliant modélisation prédictive, analyse de données et automatisation intelligente.
🔹 Compétences clés : Machine Learning (Supervised & Unsupervised Learning), NLP, RAG & Agents IA (LLMs), Bases de données SQL/NoSQL, Big Data Analytics.
🔹 Projets réalisés : Prédiction de la qualité de l’air avec IA, automatisation de workflows avec IA NoCode, algorithmes de recommandation et optimisation.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Arabe
Bilingue ou natif
Espagnol
Notions
Accepte de travailler sur site
Paris (jusqu’à 50 km), Lyon (jusqu’à 10 km), Nantes (jusqu’à 10 km), Marseille (jusqu’à 10 km)
Expériences
- ENGIE - Entreprises & CollectivitésData Scientist - Energy Storage (BESS)ENERGIEavril 2025 - Aujourd'hui (1 an et 2 mois)Paris, France
- Designed and implemented a multi-model intelligent mapping framework for BESS telemetry tags, combining RAG pipelines (LLMs), NLP (NER, similarity), statistical profiling, and DeepLearning approaches to generate high-confidence mappings by cross-validating model outputs.
- Developed a statistical sensor profiling model to characterize each tag's behavioral signature using the TSFresh library on large-scale time series data, enabling probabilistic mapping and anomaly detection across heterogeneous telemetry streams.
- Optimized the data ingestion architecture: replaced the AWS Firehose layer with a direct Kinesis → Databricksintegration, reducing daily costs from $300+ to $10–30 while ensuring full data capture across 10+ BESS sites.
- Led performance evaluation research across all implemented models (RAGs, NLP, statistical profiling, DeepLearning), designing a unified framework with retrieval/generation metrics and agent-based validation.
- Collaborated with GBU Renewables, Labor Elec, Databricks, and AWS teams to ensure alignment with operational use cases and define a scalable deployment strategy.
- G-ONData Scientist & IAENERGIEmai 2024 - octobre 2024 (5 mois)Issy-les-Moulineaux, France• Supervised Learning for Indoor Air Quality Prediction – Developed a machine learning model using CatBoost, XGBoost, and Artificial Neural Networks (ANNs) to predict CO₂ and VOC levels from time-series micro-sensor data, optimizing hyperparameters and evaluating performance with R² > 0.90.• Alternative to FileMaker: Designed and implemented a more efficient database system tailored for non-technical users, offering a simplified interface and enhanced functionalities compared to FileMaker.• Data Analytics and Consolidation: Built and optimized statistical databases (BDNB) using public energy audit data, performing advanced correlation analysis between IoT sensor data and energy consumption patterns to drive actionable insights.• Process Automation with NoCode and AI: Automated key workflows (API), including report generation and data processing, leveraging NoCode platforms and AI, improving productivity by 30%.• Cross-Functional Collaboration: Collaborated with cross-departmental teams, including data scientists and energy auditors, to align AI implementations with organizational goals.
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Formations
- Master MIAGE in Decision-Making ITDauphine - PSL University2025• Data Warehousing, IT Security, Machine Learning (Numpy, TensorFlow, Scikit-Learn, Pandas, Computer Vision, NLP, RAGs), Big Data (Spark, HDFS) • Data Optimization (DDG, Regularization, Stochastic Gradient), Decision Algorithms, Power BI, SAS
- Bachelor ’ s Degree in Mathematics, Computer Science, and FinanceDauphine - PSL University2023Mathematics (Algebra, Probability, Statistics), Programming (Java, Python, C, Unix), Economics