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Researcher at FAIR
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Assessing your cultural and operational fit
Unity ML-Agents : Deep Reinforcement Learning Platform. Double Master Degree from Stanford University and Ecole Polytechnique. Focus on Machine Learning, Deep Learning, Reinforcement Learning, Applied Mathematics.
Stanford University
Master’s Degree, Management Science
January 1, 2015 – January 1, 2017
École Polytechnique
Master's Degree, Physics
January 1, 2011 – January 1, 2015
Meta
Researcher at FAIR
November 1, 2021 – Present
San Francisco Bay Area
Unity Technologies
Senior Machine Learning Software Engineer
May 1, 2019 – November 1, 2021
San Francisco Bay Area
Unity Technologies
Machine Learning Software Engineer
May 1, 2017 – May 1, 2019
San Francisco Bay Area
Stanford University
Head Teacher Assistant
September 1, 2016 – December 1, 2016
Stanford
Unity Technologies
Data Scientist Intern
June 1, 2016 – September 1, 2016
Région de la baie de San Francisco, États-Unis
Natural Resources Defense Council
Schneider Research Fellow
July 1, 2015 – September 1, 2015
Chicago, IL
Stanford University
Teacher Assistant in Physics
January 1, 2015 – June 1, 2016
Stanford Physics Department
Nara Institute of Science and Technology
Analysis Internship
April 1, 2014 – July 1, 2014
Japon
Cultural Fit Analysis
The candidate has a strong background in research and development within tech companies (Meta, Unity Technologies) and academia. The transition from Machine Learning Software Engineer/Researcher to a Data Analyst role suggests an interest in data-driven insights, which aligns with the target role. However, the primary focus of recent roles has been more on ML engineering and research rather than pure data analysis, which might require some adjustment. The project diversity is strong in ML/research but less explicit in diverse data analysis applications beyond the intern role.
Soft Skills & Operational Fit
The candidate's experience as a Head Teacher Assistant at Stanford demonstrates leadership, team management, and organizational skills. The descriptions of projects, while brief, indicate problem-solving and analytical capabilities. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.