I am leading the development of data science products in the healthcare space to drive quality/safety improvements, streamline billing processes, and improve compliance in key areas. Our products primarily offer improvements throughout the perioperative process, which is uniquely challenging for all involved providers.

I currently am working on the intelligence behind Alchemy: a support-as-a-service platform build to empower partner teams to reduce their support costs. In my time working on Alchemy, the platform has grown from powering a single application to over 18 production applications. I've developed onboarding processes for new applications, techniques for managing custom NLP/ML requirements per application, and statistical techniques to refine our data-driven approach to growth. I've also evangelized our work at internal conferences to make strategic partnerships and identify new potential customers.In the past, I've also worked directly on our ML deployment scheme. My work led to a 90% reduction in hosting costs, a 75% reduction in average inference times, and significantly fewer outages/regressions. I designed the config-based approach that has allowed us source control and manage expansion to new workloads.I've led several informal projects: one highlight was to create a contextual-embedding based historical case search. This project led to reduce ticket time to resolve. A second project involved creating an intelligent feedback solicitation system to inject human intelligence into our ML platform. This project has led to better model outcomes and an improved AI/HI paradigm.

Predicted undergraduate dropout risk: published “Predicting Student Dropout in Higher Education”, presented at 2016 ICML Workshop “#Data4Good”

I worked with the customer DNA team and got a chance to experience industry standards for data management practice. The projects completed involved working with a variety of complex data to derive business intelligence.