Kevin Yang
Data Scientist
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKevin Yang is a Data Scientist with a decade of experience building product-focused ML and analytics across startups and major tech platforms from Instagram and Slack to Palo Alto’s Glean. He pairs hands-on modeling—evidenced by contributions to the popular H2O open-source ML platform and expertise in clustering and diagnostics—with product instincts honed as a first PM hire and founder. At Slack he led data science for the Developer Platform, helping third-party integrations scale, and earlier roles spanned recommender systems, experimentation, and feature engineering. Kevin has entrepreneurial chops (co-founded a YC company and launched Relay to streamline customer channels) that complement his rigorous Princeton training in Operations Research and Financial Engineering. He’s comfortable moving from notebooks and model debugging to shaping product strategy and developer-facing data tooling. Colleagues would describe him as a pragmatic scientist who elevates product decisions with scalable, production-minded analytics.
10 years of coding experience
9 years of employment as a software developer
Summer 16, Summer 16 at Y CombinatorBachelor of Science in Engineering (BSE) Operations Research and Financial Engineering, Bachelor of Science in Engineering (BSE) Operations Research and Financial Engineering at Princeton University
Github Skills (7)
jupyter-notebook10
machine-learning10
h2o10
python10
data-science10
k-means-clustering10
automl9
Programming languages (1)
Github contributions (4)
h2oai/h2o-3
Oct 2015 - Nov 2015
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Data Scientist
Data ScientistContributions:49 commits, 73 pushes, 8 branches in 1 month
Contributions summary:Kevin made a commit demonstrating work in a Jupyter Notebook (`.ipynb`) related to clustering and diagnostics using the H2O machine learning library. The commit modifies a notebook to allow for proper execution, which involved addressing errors and adapting to the syntax. The user's contribution involves the application of machine learning models, specifically K-means clustering, within the context of the H2O platform.
xgboostgampythonk-meansautoencoders
kyanga2/analystgg
Sep 2015 - Oct 2015
Contributions:27 pushes, 2 branches in 1 month
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