Jordan Thieyre
About Jordan Thieyre
Jordan Thieyre is a Machine Learning Engineer currently working at Inria in Paris. He has a background in data science and system administration, with previous roles at EY and various educational institutions.
Current Role at Inria
Jordan Thieyre works as a Machine Learning Engineer at Inria, a prominent research institution in France. He has held this position since 2021, contributing to various projects and initiatives within the organization. His role focuses on applying machine learning techniques to solve complex problems, leveraging his expertise in data analysis and algorithm development.
Previous Experience in Data Science
Before joining Inria, Jordan Thieyre worked at EY as a Data Scientist in the Forensic & Integrity Services division from 2019 to 2021. His responsibilities included analyzing data to uncover insights and support investigations. He also held a position at EY for five months in 2019, further developing his skills in data analysis and problem-solving.
Education and Expertise
Jordan Thieyre studied at Arts et Métiers ParisTech, where he earned a Diplôme d'ingénieur in Informatique from 2016 to 2019. He also completed two years of preparatory classes at PTSI-PT TURGOT from 2014 to 2016. His educational background is complemented by a Baccalauréat in Sciences Industrielles from Lycée Georges Cabanis, achieved from 2011 to 2014.
Involvement in Projects
Jordan Thieyre is involved in the REGALIA project, which focuses on the regulation of recommendation and pricing algorithms. This project highlights his engagement in significant research initiatives that address contemporary challenges in algorithmic regulation and ethics.
Technical Skills and Tools
Jordan Thieyre possesses a diverse skill set in data processing and analysis. He is proficient in web scraping using Selenium, natural language processing with NLTK, and image processing using OpenCV. Additionally, he utilizes tools such as Pandas, Numpy, and Tableau for data visualization, showcasing his versatility in handling various types of data.