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Software Engineer at Google
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Wisconsin-Madison
Doctor of Philosophy (PhD), Statistics
January 1, 2010 – January 1, 2015
University of Science and Technology of China
Master's degree, Mathematics
January 1, 2005 – January 1, 2008
University of Science and Technology of China
Bachelor's degree, Mathematics
January 1, 2001 – January 1, 2005
Software Engineer
September 1, 2017 – Present
Mountain View, California
Groupon
Machine Learning Engineer
September 1, 2015 – Present
University of Wisconsin-Madison
Research Assistant
September 1, 2014 – June 1, 2015
Greater Madison Area
University of Wisconsin-Madison
Lecturer
September 1, 2013 – May 1, 2014
Greater Madison Area
University of Wisconsin-Madison
Teaching Assistant
January 1, 2013 – May 1, 2013
Greater Madison Area
University of Wisconsin-Madison
Project Assistant
July 1, 2012 – December 1, 2012
Greater Madison Area
University of Wisconsin-Madison
Teaching Assistant
January 1, 2011 – May 1, 2012
Greater Madison Area
r-deploy-git
February 1, 2014 – Present
Automatically preprocess and deploy R package to github.
MaXact
June 1, 2009 – Present
Exact MAX3 or MAX2 test for one-locus genetic association analysis and trend test for dominant, recessive and additive models. Core functions implemented in C++. Significantly faster than simulation methods.
Cultural Fit Analysis
The candidate's diverse background, including academic research, teaching, and industry roles at prominent tech companies (Google, Groupon), suggests adaptability and a broad perspective. The personal projects indicate initiative and a passion for data-driven solutions. The transition from academic research to industry roles aligns with a growth mindset. However, without specific details on team collaboration or project contributions in industry, it's difficult to fully assess cultural fit beyond a general alignment with analytical and problem-solving environments.
Soft Skills & Operational Fit
The candidate's background as a lecturer and teaching assistant suggests strong communication and pedagogical skills, which are beneficial for explaining complex data insights. The research assistant role implies strong problem-solving and independent work capabilities. However, specific details on collaboration and project management within industry roles are not provided.