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Applied Scientist II at Amazon
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Major in machine learning and graph algorithms. Strongly interested in and highly skilled at applied mathematics, numerical analysis and numerical simulation. Fluent with Python, Scala, SQL, C++, Fortran, and LatTex. Have several years of experience with machine learning and graph algorithms applied to risk control in finance. GitHub address: https://github.com/primerLi
Louisiana State University
Doctor of Philosophy - PhD, Physics
January 1, 2011 – January 1, 2017
Nanjing University of Aeronautics and Astronautics
Bachelor's degree, Engineering Mechanics
January 1, 2005 – January 1, 2009
Amazon
Applied Scientist II
July 1, 2021 – Present
San Diego, California, United States
Suning R&D Center USA
Software engineer/Machine learning engineer
October 1, 2017 – July 1, 2021
Palo Alto
Department of Physics and Astronomy, LSU
Research assistant
August 1, 2011 – August 1, 2017
Baton Rouge, Louisiana, United States
Nanjing University of Aeronautics and Astronautics
graduate student
September 1, 2009 – June 1, 2011
Nanjing University of Aeronautics and Astronautics
Undergraduate
September 1, 2005 – June 1, 2009
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, transitioning into applied science and machine learning. While these roles involve software development, the direct alignment with a pure 'Backend Engineer' role, which often emphasizes specific architectural patterns, distributed systems, and API design, is not explicitly detailed in the resume. The lack of project descriptions makes it difficult to assess diversity of experience or specific contributions to team environments. The experience at Amazon as an Applied Scientist II could indicate exposure to large-scale backend systems, but the specific nature of the work is not provided.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. The candidate's academic and industry roles suggest a capacity for complex problem-solving and research, which are valuable for a backend engineering role.