Megan Thomas is an Associate Manager in Data Science with eight years of hands-on experience turning large, messy datasets into actionable predictive models, automated reporting, and executive-ready dashboards. She has driven measurable business impact across education technology, retail, insurance, and software services—delivering wins like a 20% YOY conversion lift at DoorDash and streamlining marketing analytics and GA4 migration at LegalZoom. Comfortable at the intersection of statistics and engineering, Megan builds reproducible models for retention, cross-sell, fraud detection, and contract valuation while making complex results broadly accessible through intuitive visualizations. Her background includes near-complete PhD training in statistics from UC Berkeley and repeated success operationalizing analytics so they become everyday decision tools for sales, marketing, and operations. Notably, she combines deep experimental design and A/B testing expertise with pragmatic automation that reduces reporting time and uncovers high-value growth opportunities.
8 years of coding experience
9 years of employment as a software developer
PhD (All but dissertation) Statistics, PhD (All but dissertation) Statistics at University of California, Berkeley
Bachelor of Science (BS) Statistics, Bachelor of Science (BS) Statistics at University of Pittsburgh
Contributions:30 PRs, 40 pushes, 9 branches in 2 months
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