Rachel House
Lead Data Scientist
Nashville-Davidson, Tennessee, United States
Summary
🤩
Rockstar🎓
Top SchoolRachel House is a Lead Data Scientist with 18 years of cross-disciplinary experience spanning data engineering, ML engineering, data science, software, and systems engineering, now focused on delivering data-enabled solutions at Cat Financial. She combines hands-on technical leadership with a talent for translating complex ideas for stakeholders and the C-suite, and is particularly skilled at turning concepts into production-grade systems and tooling. Rachel has driven foundational ML infrastructure at hardware-centric startups and built production AI pipelines and labeled-data frameworks at S&P Global, demonstrating strength across both cloud platforms and embedded-sensor domains. As Senior Developer Advocate for the widely used Great Expectations project, she produced tutorials, docs, and workshops that improved data-quality adoption and user experience for GX Cloud and Airflow integrations. She is passionate about technical education and data literacy, creating internal training programs and widely consumed e-learning content that scale knowledge across organizations. Known for a craft-first mindset, she pairs rigorous architecture and clarity of presentation with a relentless focus on continuous improvement.
9 years of coding experience
15 years of employment as a software developer
Bachelor's Degree, Computer Engineering, Bachelor's Degree, Computer Engineering at University of Virginia
Github Skills (9)
docusaurus10
documentation10
technical-writing10
data-quality9
airflow8
exploratory-data-analysis7
data-engineering7
cloud-computing6
data-science6
Programming languages (4)
Github contributions (5)
great-expectations/great_expectations
Aug 2023 - Jan 2025
Always know what to expect from your data.
Role in this project:
Technical Writer
Technical WriterContributions:107 reviews, 34 PRs, 148 pushes in 1 year 4 months
Contributions summary:Rachel primarily focused on updating and adding documentation for the Great Expectations project. They added documentation for GX Cloud, including architecture, deployment patterns, and managing credentials. The user also updated the documentation for Try GX and added tutorial pages to the Learn section regarding data quality use cases and Airflow integration. These contributions indicate a strong focus on improving the user experience and providing comprehensive guidance.
pythondatadata-integritydatacleanerpipeline-testing
rachhouse/vogon
Aug 2020 - Aug 2021
Corral poetry and jupyterlab into docker for containerized python library development.
Contributions:14 commits, 7 PRs, 17 pushes in 1 year
python-librarypythondocker-imagejupyter-notebookinto-docker
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