Charlie Henry is a full-stack data scientist with experience leading teams and delivering end-to-end data solutions at the City of Austin Transportation Department, he specializes in Python, SQL, cloud orchestration, and Power BI.
Leads a cross-functional team (data analyst, intern, product manager) to develop data-driven tools for decision-making.
Engineered Python data pipelines to support interactive dashboards and open data APIs.
Developed data products that track $1 billion in spending and $10 million in revenue
Automated daily data publishing pipelines using Python and Prefect for the city’s open data portal
Created high-frequency updating dashboards using Power BI and Python
Developed high-impact safety models using large-scale transportation datasets
Designed and tested traffic simulation models for complex roadway networks
Masters of Science
Bachelor of Science
Developed an end-to-end ML pipeline that forecasts airport security wait times and posts daily updates to Bluesky
Automated data ingestion from TSA checkpoint reports (PDF format) using Python ETL scripts
Trained and deployed ARIMA-based supervised learning models for short-term forecasting
Integrated the pipeline with Bluesky’s API to publish real-time predictions and travel tips for major U.S. airports
Tools: Python, Scikit-learn, Postgres, Google Cloud Functions, Bluesky API
Built a Bluesky bot that monitors and posts real-time road condition updates for Austin using open sensor data
Implemented a Python script to query roadway grip sensors every 5 minutes via the city’s open data API
Open-sourced and containerized the application with Docker, and deployed it on Render for continuous operation
Designed the solution to be modular and open-source for easy adaptation to other cities with similar datasets
Tools: Python, Docker, Render, Bluesky API, City of Austin Open Data Portal