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AI @ NVIDIA
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine learning engineer with expertise in computer vision, and recommendation systems.
Université Côte d'Azur
Doctor of Philosophy (PhD), Applied Mathematics
January 1, 2013 – January 1, 2016
ENS Cachan
Master of Science (M.S.), Master MVA (Mathématiques, Vision, Apprentissage)
January 1, 2011 – January 1, 2012
CentraleSupélec
Master of Science (M.S.), Mathématiques Appliquées
January 1, 2009 – January 1, 2012
Lycée du Parc
Equivalence L1 / L2, Mathématiques et Physique
January 1, 2007 – January 1, 2009
NVIDIA
Principal Applied Research Scientist
May 1, 2024 – Present
San Francisco Bay Area · Hybrid
YouTube
Senior Machine Learning Engineer
October 1, 2019 – May 1, 2024
San Francisco Bay Area
NVIDIA
Research Scientist for Deep Learning
January 1, 2018 – October 1, 2019
San Francisco Bay Area
Arterys
Machine learning scientist
July 1, 2016 – January 1, 2018
San Francisco
Massachusetts General Hospital
Visiting Scholar
January 1, 2013 – December 1, 2013
Région de Boston, États-Unis
INRIA
PhD candidate in the Asclepios team
January 1, 2013 – June 1, 2016
Sophia Antipolis
INRIA
Intern
May 1, 2012 – December 1, 2012
Sophia Antipolis
Technicolor
Project Intern
September 1, 2011 – March 1, 2012
Région de Paris, France
Georgia Institute of Technology
Visiting Scholar
February 1, 2011 – July 1, 2011
Cultural Fit Analysis
The candidate's background is heavily focused on advanced research and machine learning engineering within large tech companies and academic institutions. While this demonstrates a strong technical foundation, the project diversity and explicit alignment with a typical 'Data Analyst' role (which often involves business understanding, reporting, and dashboarding) are not clearly evident. The candidate's roles are more aligned with a Data Scientist or Machine Learning Engineer profile. The breadth of skills is deep in ML/AI but less explicit in core data analysis tools and methodologies.
Soft Skills & Operational Fit
The candidate's resume indicates a strong research and development background, suggesting analytical thinking, problem-solving, and independent work. However, specific soft skills like teamwork, communication, and leadership are not explicitly detailed in the provided descriptions. Operational fit for a Data Analyst role is moderate, as the experience leans heavily towards ML Engineering and Research rather than traditional data analysis, reporting, or business intelligence.