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Engineering Director - Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Passionate about Machine Learning and Engineering Leadership. Computer Science PhD focused in text mining (applied ML) from University of Illinois at Urbana-Champaign. My publication record is here: https://scholar.google.com/citations?user=mTp7FswAAAAJ&hl=en.
University of Illinois Urbana-Champaign
Doctor of Philosophy (PhD), Computer Science
January 1, 2006 – January 1, 2011
University of Illinois Urbana-Champaign
Master of Science (MS), Computer Science
January 1, 2006 – January 1, 2008
Fudan University
Bachelor’s Degree, Software Engineering
January 1, 2002 – January 1, 2006
University College Dublin
Bachelor’s Degree, Computer Science
January 1, 2002 – January 1, 2006
Engineering Director - Machine Learning
May 1, 2018 – Present
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June 1, 2010 – August 1, 2010
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May 1, 2009 – August 1, 2009
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May 1, 2008 – August 1, 2008
Cultural Fit Analysis
The candidate has a consistent career trajectory within large, high-growth tech companies (Twitter, Facebook), demonstrating adaptability to fast-paced environments. The progression from individual contributor to engineering director shows strong leadership potential and a commitment to career development. The academic background and research internships suggest a strong foundation in problem-solving and innovation. While the target role is 'Big Data Engineer', the candidate's experience is heavily skewed towards Machine Learning Engineering, which often involves significant big data components. The lack of explicit project diversity outside of ML/Ads/Notifications could be a minor area for exploration regarding broader big data applications.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership, team growth, and strategic technical vision, indicating strong operational fit for senior roles. The focus on advancing ML tech stacks and driving business metrics suggests a results-oriented approach. However, without specific psychometric test results, a detailed assessment of stress handling or team collaboration is not possible.