
Elizabeth focuses on delivering business value by enabling machine learning use cases through optimized ML pipelines, model development, and strategic guidance on tooling and best practices for deployment. Her background spans advanced analytics, data science, data engineering, reporting, and data visualization across industries including aerospace, healthcare, federal, entertainment, logistics, and manufacturing.
Previously, she served as a technical lead in machine learning at Lovelytics, where she played a key role in optimizing ML processes for clients within the Databricks ecosystem. Prior to that, she contributed to client success at Accenture by developing prototype analytics products, building robust data pipelines, designing insightful Tableau dashboards, and creating customized machine learning models.
In her free time, Elizabeth enjoys exploring Richmond, VA’s vibrant music and art scene, hiking in the outdoors, and traveling to gain new perspectives.
BSBA, Supply Chain Management, University of Tennessee, Knoxville
MIDS, Data Science, University of California, Berkeley