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Principal Applied Scientist 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
- Extensive experience in computer vision, machine learning and pattern recognition, especially in the field of object tracking, detection and recognition. - Hands-on experience with a variety of computer vision and machine learning algorithms, including graphical model (MRF, CRF), SVM, regression model, kernel methods, EM, PCA, LDA, AdaBoost, particle filtering, KLT tracker, SIFT/LBP/HOG features, Hough transform, normalized cuts, camera calibration, structure from motion etc. - Proficient in C++/C, MATLAB.
University of Maryland
Doctor of Philosophy (Ph.D.), Computer Vision, Machine Learning
N/A – Present
Beijing Institute of Technology
Bachelor's Degree, Electrical Engineering
N/A – Present
Ryerson University
Master's Degree, Computer Vision, Multimedia Signal Processing
N/A – Present
Amazon
Principal Applied Scientist
January 1, 2023 – Present
Palo Alto, California, United States
Amazon
Applied Scientist
January 1, 2017 – January 1, 2023
Palo Alto, California, United States
A9.com
Software Development Engineer/ Machine Learning Scientist
January 1, 2013 – January 1, 2017
NVIDIA
Internship
October 1, 2011 – January 1, 2012
Cultural Fit Analysis
The candidate's extensive background is heavily skewed towards Computer Vision, Machine Learning, and Software Development, primarily in research and applied science roles. While these skills are valuable, the target role is 'Data Analyst'. This represents a significant pivot, and the resume does not explicitly detail experience in traditional data analysis tasks, business intelligence, or specific data visualization/reporting tools commonly associated with a Data Analyst role. This lack of direct alignment with the target role's typical responsibilities suggests a potential cultural fit challenge for a pure Data Analyst position, though the analytical foundation is strong.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's resume highlights significant experience in research and applied science roles, suggesting strong problem-solving and analytical capabilities. However, there is no direct information on communication, teamwork, or leadership styles.