Michelle Tanco
Head Of Product at H2O.ai
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolMichelle Tanco is a product leader with 10 years of experience marrying deep technical chops and go-to-market savvy as Head of Product at H2O.ai, where she directs the AI Cloud and Generative AI portfolio for enterprise customers. She started in data science and solutions engineering, moving through product management to lead cross-functional teams that ship SDKs, an AI App Store, and platform services while working closely with C-suite stakeholders. Hands-on engineering roots show in her open-source contributions to core H2O projects like h2o-3 and Sparkling Water, where she has driven backend features, DevOps improvements, and release management. Known for translating complex ML capabilities into compelling demos, training programs, and winning proofs-of-concept, she balances technical detail with customer impact. Based in Seattle with dual degrees in Mathematics and Computer Science from Ursinus College, she brings a rare combination of product strategy, executive alignment, and low-level platform experience.
10 years of coding experience
10 years of employment as a software developer
Auburn MountainviewBachelor's Degree Mathematics (3.85) and Computer Science (3.9), Bachelor's Degree Mathematics (3.85) and Computer Science (3.9) at Ursinus College
English
Github Skills (15)
release-management10
spark10
h2o10
integrate10
hadoop10
integrations10
java9
devops9
scala9
machine-learning9
javas9
big-data9
pyspark8
r8
build-automation8
Programming languages (7)
Github contributions (5)
h2oai/sparkling-water
Jan 2018 - Apr 2020
Sparkling Water provides H2O functionality inside Spark cluster
Role in this project:
Back-end Developer & Release Manager
Back-end Developer & Release ManagerContributions:560 commits, 48 PRs, 757 pushes in 2 years 3 months
Contributions summary:Michelle's commits primarily revolve around releasing new versions of RSparkling, a library providing H2O functionality within a Spark cluster. Their contributions involved updating the `README.rst` file to reflect the latest Sparkling Water, H2O, and Spark versions, ensuring compatibility. These releases included updates to installation instructions, specifically regarding the installation of H2O and the necessary dependencies. The user also adjusted documentation regarding the download location of specific jar files to align with the current release structure.
sparkh2omachine-learningintegrationpysparkling
h2oai/h2o-3
Nov 2015 - May 2020
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:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:539 commits, 1002 pushes, 1 branch in 4 years 7 months
Contributions summary:Michelle's contributions focus on integrating new features for the H2O Driver class. They were responsible for adding functionality for clouding with the filesystem and introducing the concept of a Clouding Manager. Further, the user worked on incorporating external dependencies, like Hive JDBC Driver, and implementing testing, and enabling the allow_insecure_xgboost flag within the H2O driver. The user's work also covers adding configuration options such as a Hive JDBC Url, and principal information to assist with Kerberos authentication.
automldeep-learningelastic-netgbmgradient-boosting
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