Xiaowen Zhang
Data AnalystAI Engineer Tech Lead
LinkedIn
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer Tech Lead
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Problem solving oriented AI Engineering Tech Lead.
Columbia University
Master’s Degree, Electrical Engineering
January 1, 2015 – January 1, 2017
UCLA
Exchange Program, Statistics
January 1, 2014 – January 1, 2014
Tongji University
Bachelor’s Degree, Electronics Science and Technology
January 1, 2011 – January 1, 2015
Staff Software Engineer - AI
January 1, 2022 – January 1, 2026
Senior Software Engineer - AI
March 1, 2020 – December 1, 2021
Senior Software Engineer, AI
July 1, 2019 – February 1, 2020
Software Engineer, AI
January 1, 2018 – July 1, 2019
京东商城
Research Engineer
March 1, 2017 – November 1, 2017
Santa Clara, California
Columbia University in the City of New York
Course Assistant
September 1, 2016 – December 1, 2016
New York
Algorithms: Design and Analysis
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in AI/ML and software engineering, primarily within large tech companies like LinkedIn. While the target role is 'Data Analyst', the candidate's experience is heavily skewed towards AI/ML engineering. This suggests a potential mismatch in the day-to-day responsibilities and focus, as a Data Analyst role typically emphasizes data extraction, transformation, visualization, and statistical analysis rather than AI model development and deployment. The 'Exchange Program, Statistics' at UCLA is a positive indicator, but the core experience is not directly aligned with a typical Data Analyst profile.
Soft Skills & Operational Fit
The candidate's resume indicates a progression to Tech Lead, suggesting strong leadership, problem-solving, and potentially communication skills within a technical context. However, without specific project descriptions or interview data, it's difficult to assess operational fit beyond technical leadership.