Yaoguang Zhai
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Applied Scientist on Optimization at Amazon
As a Ph.D. on Computer Science, my primary focus is on the development of machine learning based search and optimization algorithm and its scientific and engineering applications including molecular simulations, chip designs, and SMT solving. I also have extensive experience working with high-performance computing systems. Furthermore, I am a senior Mechanical Engineer with comprehensive expertise in both solid mechanics and aerodynamics. I'm known for my self-motivation, diligence, and easy-going nature, coupled with a strong willingness to take on responsibilities and consistently deliver results. Professional specialization: - Sample driven search and optimization for non-convex and black-box problems - Machine learning modelling and active learning algorithm - Industrial and scientific software development in C++, Rust, and Python - Solid mechanics & dynamics - Aerodynamics Proficient in: - Python, C/C++, Matlab, C#, FORTRAN, VBA, shell scripts - OpenMP, CUDA, AWS，high-performance computing clusters
UC San Diego
Doctor of Philosophy - PhD, Computer Science
January 1, 2018 – January 1, 2023
UC San Diego
Master's degree, Computational Science, Mathematics and Engineering
January 1, 2016 – December 1, 2018
KTH Royal Institute of Technology
Master's degree, Sustainable Energy Engineering
January 1, 2006 – January 1, 2008
Zhejiang University
Bachelor's degree, Engineering Mechanics
January 1, 2002 – January 1, 2006
Amazon
Applied Scientist
April 1, 2024 – Present
On-site
Amazon
Applied Scientist Intern
June 1, 2023 – December 1, 2023
Cupertino, California, United States · On-site
Amazon
Applied Scientist Intern
June 1, 2022 – September 1, 2022
Cupertino, California, United States · On-site
Lawrence Livermore National Laboratory
Data Scientist Intern
June 1, 2020 – September 1, 2020
Interpreta
Data Scientist Intern
June 1, 2019 – September 1, 2019
La Jolla · On-site
University of California, San Diego
Graduate Researcher at Computer Science and Engineering, UCSD
September 1, 2018 – March 1, 2024
La Jolla
Veritone, Inc.
Data Scientist Intern
June 1, 2018 – September 1, 2018
Greater Los Angeles Area · On-site
San Diego Super Computer Ctr
Graduate Research Assistance on Machine Learning and Parallel Computing
June 1, 2017 – June 1, 2019
Greater San Diego Area
Siemens Industrial Turbomachinery AB
Research And Development Engineer
December 1, 2008 – September 1, 2016
Finspång, Sweden · On-site
Cultural Fit Analysis
The candidate has a strong academic background and experience in research and development, including roles at Amazon and various research institutions. This indicates a fit for environments that value innovation, deep technical problem-solving, and continuous learning. The diverse project experience, from automated reasoning to protein design and health record analysis, shows adaptability and a broad interest in applying data science across different domains. However, the target role is 'Data Analyst', while the candidate's experience leans heavily towards 'Applied Scientist' and 'Data Scientist' with a focus on model development and optimization. This might indicate a potential overqualification or a mismatch in the expected day-to-day responsibilities, as a Data Analyst role often emphasizes reporting, dashboarding, and business insights more than advanced algorithm development. The lack of explicit project descriptions for the 'Data Analyst' target role makes it difficult to fully assess the alignment with typical data analyst responsibilities.
Soft Skills & Operational Fit
The candidate's resume highlights leadership in projects and innovation, suggesting strong problem-solving and initiative. The descriptions of enhancing efficiency and developing novel models indicate a results-oriented approach. However, without specific psychometric test results or interview data, a detailed assessment of soft skills like teamwork, stress handling, and communication clarity in a collaborative setting is not possible.