Andy Eschbacher is a Staff Data Scientist in Washington, D.C. with 11 years of experience building production-grade spatial data science platforms and ML systems. He leads cross-functional engineering teams to design cloud-native geospatial pipelines on GCP, achieving multi-fold performance and cost improvements while introducing ML monitoring and human-in-the-loop validation. His work spans applied research, NLP and transformer-based entity resolution, graph algorithms for service-area optimization, and Kubernetes-powered productionization. Formerly a lecturer in urban spatial analytics and an educator earlier in his career, he brings a strong pedagogy to technical leadership and product strategy. Notably, he transformed manual geospatial processes into automated solutions with 6x efficiency gains and cut database resource use by 90% in Python-based systems.
11 years of coding experience
8 years of employment as a software developer
MPhys Physics, MPhys Physics at The University of Edinburgh
MA Physics, MA Physics at The University of Texas at Austin
Contributions:1 release, 2 PRs, 9 pushes in 5 years 1 month
prototypeterritorypartitioning
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