David Cheng
Sr. Manager, Automation & Analytics
Los Gatos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDavid Cheng is a senior manager specializing in automation and analytics who leverages 15+ years of experience to design AI-driven ecosystems that turn data into operational leverage. Based in Los Gatos, he has led cross-functional analytics and marketing automation teams at companies from Mandiant to Google and now MariaDB, bridging strategy, operations, and engineering. His background combines hands-on data and marketing operations—spanning roles at Cisco, Oracle, and startups—with recent work at Google focused on strategic operational improvements. A practical ML contributor, he implemented and benchmarked a BERT SQuAD model within the widely used tensorflow/models repo, demonstrating attention to performance and evaluation metrics. Known for translating complex metrics into actionable workflows, he excels at scaling analytics platforms that improve acquisition, retention, and business decisioning. David pairs technical credibility with operator instincts, frequently surfacing non-obvious performance insights that guide product and go-to-market choices.
7 years of coding experience
7 years of employment as a software developer
MIS Business, MIS Business at San José State University
Github Skills (9)
machine-learning10
benchmark10
nlp10
benchmarking10
tensorflow10
natural-language-processing10
bert10
python9
gpu9
Programming languages (3)
Github contributions (5)
tensorflow/models
Jun 2019 - Apr 2020
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:25 commits, 10 PRs, 2 pushes in 10 months
Contributions summary:David implemented and benchmarked a BERT model for the SQuAD task, creating a new benchmark file. They added a new benchmark to measure the model's performance with different GPU configurations. The user also introduced a new class to measure accuracy, and added steps to evaluate the model, incorporating evaluation metrics into the training summary. This work showcases a focus on performance evaluation and accuracy testing within the context of a BERT model.
deep-learningtensorflow
davidmochen/models
May 2019 - Feb 2020
Models and examples built with TensorFlow
Contributions:134 pushes, 59 branches in 8 months
tensorflow
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