À propos de Harry
En tant qu'ancien chercheur passionné par la science et la technologie, j'aime travailler sur des projets data/IA innovants. Je trouve que ce type de projets pousse à penser en dehors des sentiers battus et à trouver des solutions uniques à des problèmes complexes. Bien que je sois particulièrement attiré par les projets dans le secteur industriel, y compris la production, l'énergie, le transport, ainsi que dans les domaines de la médecine et de l'aérospatiale, je suis ouvert à explorer d'autres domaines et secteurs. Je crois que l’utilisation des technologies d’IA à ces domaines a le potentiel de révolutionner la façon dont nous abordons ces industries et de les rendre plus efficaces, plus durables et plus efficientes.
Français
Bilingue ou natif
Accepte de travailler sur site
Nantes (jusqu’à 50 km), Paris (jusqu’à 10 km), Rennes (jusqu’à 10 km), Angers (jusqu’à 10 km), Tours (jusqu’à 10 km)
Expériences
- ZenikaConsultant/Trainer Data Science - Machine Learning - Deep Learning - MLOpsAGENCE & SSIIseptembre 2019 - mars 2023 (3 ans et 6 mois)Nantes, France► Assignment @ Cbp - Digital & Technologies Division:- Prediction of the complexity of a claim- Optimization of medical controls- Activity rate prediction⚗ Topics: machine learning, deep learning, MLOps, CI/CD, explainability, fairness⚒ Environment: Python, TensorFlow, AWS (EMR, SageMaker Pipelines, Forecast), Data Catalog (Zeenea)► Assignment @ industry sector: Development of an end-to-end solution for automatic segmentation and spatial positioning of multiple objects in a 3D scene.⚗ Topics: deep learning, MLOps, computer vision, 3D U-Net, vector calculus in Euclidean geometry, quaternion rotation, optimization algorithm⚒ Environment: Python, TensorFlow, Open3D, AzureML► Assignment @ SNCF : Data Scientist of the "Rock the Data" team at 574 in Nantes. Development and industrialisation of a time series forecasting tool for SNCF national call centres.⚗ Topics: time series, gradient boosting, deep learning, MLOps⚒ Environment : Python, Azure ML, Azure DevOps, TensorFlow, Pandas, NumPy, CatBoost► Assignment @ VadeSecure: Development of an email binary classification API (benign or malicious).⚗ Topics: NLP, machine learning, MLOps, CI/CD⚒ Environment : Python, Scikit-learn, NLTK, Docker, FastAPI, bash► Contributor and Trainer of "Python for Data Science" and "Deep Learning with Tensorflow 2" training.► Professor at CNAM Nantes, Master 1 course, "Manipulating structured data with Python" (Numpy, Pandas, Matplotlib).► POC Manitou Group, study of the implementation of Databricks (Spark Structured Streaming, spark.ml, MLFlow).► Continuous self-training in Machine Learning, Deep Learning.► Technology watch► Participation in Data Science competitions (Kaggle, Challenge Data)⚗ Workshop/Talk╸ "Introduction to TensorFlow" Shopee Code League 2020⚗ Certifications╸ "Professional Machine Learning Engineer" (GCP)╸ "TensorFlow Developer"╸ "Microsoft Certified: Azure Data Scientist Associate"
- FreelanceFreelance Data Scientist / Machine Learning Engineer with Cloud and MLOps expertiseavril 2023 - Aujourd'hui (3 ans et 2 mois)France
- CEA - Commissariat à l'énergie atomique et aux énergies alternativesResearcher in multiphysics modellingENERGIEnovembre 2015 - juin 2019 (3 ans et 7 mois)91400 Saclay, France► Studies in modelling and multiphysics simulation of welding and additive manufacturing (processes and thermo-metallo-mechanical effects).► Development of a Cast3M generic routine for transient thermo-mechanical element-finish simulation multi-pass welding and additive manufacturing.► Implementation of numerical tools in Python 3 in a welding simulation business software and coupling of scientific calculation codes.► Supervision of a PhD student in the field of welding and the lifespan of 316L(N) steel assemblies.► Scientific and technical support of a PhD student in the field of additive manufacturing of metallic materials.► Participation in European projects on the subject of welding and prediction of residual manufacturing stresses.► Consultation by external companies in the field of relaxation cracking of austenitic stainless steels (e.g. AISI 316L).► Computer management in the laboratory (i.e. purchasing, hardware, network, software installation).► Management of the laboratory's scanning electron microscope (SEM).⚗ Main Talks╸ "Intergranular Fracture in Austenitic Stainless Steels : A Micromechanical Model of Reheat Cracking Following a Local Approach to Fracture" 20th European Conference on Fracture (Trondheim - Norway, 2014)╸ "Intergranular Fracture in Austenitic Stainless Steels : A Micromechanical Model of Reheat Cracking Following a Local Approach to Fracture" Creep Fatigue Crack Development, Symposium HTMTC (Dubendörf - Swiss, 2014)
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Formations
- Diplôme de Docteur en Sciences et Génie des MatériauxEcole nationale supérieure des Mines de Paris2014Diplôme de Docteur en Sciences et Génie des Matériaux
- Degree of Mechanical Engineer, Mechanics, MaterialsInstitut supérieur de mécanique de Paris2011Degree of Mechanical Engineer, Mechanics, Materials