À propos de Simon
I am a Machine Learning Engineer at the intersection of Deep Neural Networks and hard core engineering.
My technical day-to-day involves leveraging skills in {Python (tensorflow, matplotlib, numpy, scipy, file i/o), C++, distributed computing, research, linear algebra, calculus, and statistics} in order to deliver sustainable, extensible software for research and production.
This includes high-level work such as
- performing research on state-of-the-art methods (using tools such as Google Scholar, Mendeley, PapersWithCode etc.),
- inventing and investigating model architectures
- developing novel scientific applications
as well as the more lower-level activities involved in
- building end-to-end software frameworks that encompass data preprocessing, model training, evaluation, logging, and deployment
I am very curiosity-driven and action-oriented. I believe in the methodology of building and testing, measuring and comparing, learning, and improving.
Anglais
Bilingue ou natif
Anglais
Bilingue ou natif
Français
Bilingue ou natif
Français
Bilingue ou natif
Accepte de travailler sur site
Paris (jusqu’à 50 km), Toulouse (jusqu’à 50 km), Lyon (jusqu’à 60 km), Paris (jusqu’à 60 km), Lille (jusqu’à 50 km)
Expériences
- Noble.aiMachine Learning EngineerHIGH TECHmars 2019 - septembre 2020 (1 an et 6 mois)San Francisco, États-Unis- Research, invent, implement, and deploy deep-learning, cloud-based solutions for industry-science and device design.- Drive and develop software for experiment tracking, hyperparameter tuning, and distributed training.- Develop internal progress trackers and repeatable process documents- Break down high-level objectives into lower-level tasks and help Eng team see them through to completion using project and product management principles
- METEO FRANCEData ScientistSECTEUR PUBLIC & COLLECTIVITÉSmars 2018 - septembre 2018 (6 mois)Toulouse, FranceBuilt deep learning models for weather services project to:- Classify different types of snow from webcam images, got 96.2% accuracy.- Correct bad temperatures measurements from IoT data using recurrent neural network. Error has dropped from 2 to 0.4 degrees.- Predict realistic rain movement from radar sequences, using generative models (GAN).Organize a workshop for meteorologist specialists to help them understand deep learning concepts, and how to implement them using Tensorflow, Keras.
- DatakeoData AnalystEDUCATION & E-LEARNINGavril 2017 - octobre 2018 (1 an et 6 mois)Paris, France
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Formations
- Master in MathematicsUniversity Paul Sabatier Toulouse2017Probability and Statistics track, with a focus on the mathematics behind machine learning and deep learning, such as: - Stochastic processes - Asymptotic theory - Convex Analysis
- Master in EconomicsToulouse school of Economics2015A solid formation for students who want to pursue a PhD in Economics, with a high diversity of skills developed, such as: - Mathematics - Informatics - Finance, corporate organizations, etc..