About 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.
French
Native or bilingual
Can work on-site
Nantes (up to 50km), Paris (up to 10km), Rennes (up to 10km), Angers (up to 10km), Tours (up to 10km)
Experience
- ZenikaConsultant/Trainer Data Science - Machine Learning - Deep Learning - MLOpsDIGITAL AND ITSeptember 2019 - March 2023 (3 years and 6 months)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 expertiseApril 2023 - Today (3 years and 2 months)France
- CEA - Commissariat à l'énergie atomique et aux énergies alternativesResearcher in multiphysics modellingENERGY AND UTILITIESNovember 2015 - June 2019 (3 years and 7 months)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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Education
- 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