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Deep Learning Software Engineer at 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
Stanford University
Master of Science (M.S.), Computational Mathematics
January 1, 2014 – January 1, 2016
CentraleSupélec
Diplôme Ingénieur ECAM, Engineering
January 1, 2012 – January 1, 2015
Lycée Privé Sainte-Geneviève
Mathematics & Physics
January 1, 2010 – January 1, 2012
Lycée Français de Shanghai
Baccalauréat, Série scientifique
January 1, 2006 – January 1, 2010
NVIDIA
Deep Learning Software Engineer
June 1, 2016 – Present
Santa Clara
Stanford University
Teaching Assistant
September 1, 2015 – March 1, 2016
Nissan Motor Corporation
Engineering Intern
June 1, 2015 – August 1, 2015
Sunnyvale
BCD Semiconductor
Summer Industrial Engineering Intern
June 1, 2013 – July 1, 2013
Shanghai, China
Distributed Max-Flow in Spark
March 1, 2015 – June 1, 2015
Created a distributed Max-Flow algorithm and implemented it in Spark.
Towards new tactile hand prostheses with brain-machine-brain interface
September 1, 2012 – June 1, 2013
Yearlong five-person project for the Adaptive NeuroComputation Group (CNRS and Université Pierre & Marie Curie, Paris V) that focused on the analysis of various strategies for encoding hand prostheses tactile information in order to optimize both information rate and robustness.
Preventive error correction in Optical Character Recognition
October 1, 2011 – June 1, 2012
Yearlong sophomore research project: optical character recognition – application of the Viterbi and Forward-Backward inference algorithms in preventive error correction.
Cultural Fit Analysis
The candidate's background is heavily research and engineering-focused, with a strong emphasis on deep learning, computational mathematics, and algorithm implementation. While these skills are foundational for a Data Analyst role, the direct application of these skills to business intelligence, stakeholder communication, or specific data visualization/reporting tools is not explicitly detailed. The projects are diverse in their scientific application (neuroprostheses, OCR, distributed algorithms), indicating intellectual curiosity. However, the lack of explicit business-oriented data analysis projects or experience with common data analyst tools (e.g., SQL, Tableau, Power BI) suggests a potential gap in direct cultural fit for a typical Data Analyst role without further upskilling or a role specifically focused on advanced analytical model development.
Soft Skills & Operational Fit
The candidate's experience as a Teaching Assistant at Stanford suggests good communication and mentoring skills. Project descriptions indicate an ability to work in teams (five-person project) and independently on research-oriented tasks. The roles at NVIDIA and Nissan imply an operational fit for structured engineering environments, focusing on problem-solving and implementation. However, specific details on stress handling, work attitude, or team collaboration are not available from the provided data.