Yang Song
Applied Scientist at Yelp
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolYang Song is an applied scientist with nine years of experience building and deploying data-driven ML and experimentation systems, currently working on real-time ads bidding and auction optimization at Yelp in San Francisco. He has progressed from statistician and research roles to lead data scientist, driving causal inference, variance reduction, sequential A/B testing, and production ML for marketplace and local services. Yang combines strong statistical foundations (Stanford MS in Statistics) with hands-on engineering—Spark pipelines, Elasticsearch ranking, and productionized matching models—to close the gap between experimentation and revenue impact. He has a demonstrated knack for fixing subtle measurement problems (e.g., cohort pollution, proportion metric dependencies) and applying Bayesian and imputation methods for observational studies. Beyond ads, his background spans NLP for product flows, waitlist time prediction, and practical tooling for large-scale experimentation, and he maintains a technical blog highlighting his applied work.
9 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree Mathematics and Statistics, Bachelor’s Degree Mathematics and Statistics at Purdue UniversityMaster’s Degree Statistics, Master’s Degree Statistics at Stanford University
English, ChineseProgramming languages (1)
Github contributions (5)
songyang0716/Statistical_Testing
Oct 2019 - Sep 2020
Contributions:51 commits, 55 pushes, 1 branch in 10 months
songyang0716/Topic_Modeling
Oct 2019 - Jul 2020
Contributions:26 commits, 37 pushes, 1 branch in 9 months
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