Hannah Tillman
Technical Writer at REPAY - Realtime Electronic Payments
Madison, Wisconsin, United States
Summary
🤩
Rockstar🎓
Top SchoolHannah Tillman is a technical writer with six years of experience specializing in documentation for open-source AI and text extraction/classification projects. She spent five years focused on improving clarity and usability for platforms like H2O-3, authoring examples in R and Python and maintaining docs across Sphinx, MkDocs, and Docusaurus. Based in Madison, Wisconsin, she translates complex machine learning concepts into practical tutorials, release notes, and user-focused examples that lower the barrier to entry for data practitioners. Her background in English and nonprofit coordination sharpened her stakeholder communication and project organization skills, helping her move quickly from people-oriented roles into technical documentation. Notably, her contributions to the widely used H2O-3 project emphasize actionable examples and parameter clarity, improving developer onboarding. She currently brings that blend of editorial rigor and technical fluency to realtime payments documentation at REPAY.
6 years of coding experience
Bachelor of Arts - BA English Language and Literature General Psychology, Bachelor of Arts - BA English Language and Literature General Psychology at University of Detroit Mercy
Github Skills (4)
documentation10
r5
python5
machine-learning3
Programming languages (2)
Github contributions (5)
h2oai/h2o-3
Jul 2019 - Jan 2023
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:
Technical Writer
Technical WriterContributions:171 reviews, 987 commits, 549 PRs in 3 years 6 months
Contributions summary:Hannah appears to be a technical writer primarily focused on updating and enhancing the documentation for the H2O-3 platform. Their commits involve modifying examples, adding new examples, and clarifying descriptions for various algorithms and their parameters within the H2O-3 documentation. The commits showcase a focus on clarity and user understanding of the platform, indicating a commitment to improving the documentation's quality.
xgboostgampythonk-meansautoencoders
benjamintanweihao/h2o-3
Apr 2022 - Apr 2022
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.
Contributions:1 push in 1 day
xgboostgamk-meansadditive-modelselastic-net
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