David Huang
About David Huang
David Huang is a Machine Learning Engineer specializing in deploying NLP models to improve the accessibility of legal information. He has a background in Data Science and has worked in various roles in the field, currently at Doctrine since 2022.
Current Role at Doctrine
David Huang serves as a Machine Learning Engineer at Doctrine, a position he has held since 2022. In this role, he specializes in deploying Natural Language Processing (NLP) models aimed at improving the accessibility and comprehension of legal information. His work contributes to enhancing the understanding of complex legal texts for a broader audience.
Previous Experience in Data Science
Prior to his current position, David Huang worked as a Data Scientist at OCTO Technology from 2020 to 2021 in Paris, Île-de-France, France. His experience also includes a role as a Deeplearning Engineer and Consultant Data at ClaraVista in 2019 for six months, and a brief stint as a Consultant at JEAN-LOUP DURAND - LES COMBLES NANTAIS S.A.R.L. in 2018.
Educational Background
David Huang studied at Université de Tokyo, where he focused on Information Science and Technology, achieving the status of Research student in Data Science in 2018 for 11 months. He also holds a Master's degree in Engineering from IMT Atlantique, where he studied Génie Informatique pour l'Aide à la Décision from 2016 to 2019. His foundational education includes two years at Lycée Marcelin Berthelot, where he completed a preparatory program in Mathematics.
Recruitment Involvement at Doctrine
In addition to his engineering responsibilities, David Huang is actively involved in recruitment efforts at Doctrine. He encourages potential candidates to reach out directly for inquiries, facilitating the connection between the company and prospective talent.
Early Career Experience
David Huang began his career as a Stagiaire en Machine Learning at IAV GmbH in 2018, where he worked for two months in the Berlin region. This early experience laid the groundwork for his subsequent roles in data science and machine learning.