Focus: leveraging cloud technologies for the development of data processing and machine learning pipelines.

Denis specializes in designing and deploying full-stack, cloud-native data science solutions to intractable technical problems across domains. At the North Carolina Institute for Climate Studies, Denis works on petabyte scale multi-cloud data transfer pipelines, creating pipelines to furnish analytics-ready environmental data, and building production machine learning platforms applying MLOps principles to automate model development and deployment. Previous work has involved expanding the capabilities of scientific instrumentation using machine learning and optimization, building prediction platforms that materials science engineers could use to leverage machine learning for better product design, scaling IOT ingest and analytics pipelines, implementing workforce development plans for data science teams, and strategic positioning planning for bioinformatics companies.