Manorathan Murugesan is a Staff Data Scientist with 11 years of cross-industry experience applying machine learning and analytics to healthcare, clinical trials, and advertising domains. Based in Chicago, he has led cost-saving pipeline migrations and deployed scalable predictive models that measurably improved trial operations and drug development efficiency at AbbVie and prior roles. His background spans building real-time personalization systems, ETL and OLAP solutions, and image-recognition pipelines for public-health research, demonstrating comfort from cloud infrastructure to deep learning. Manorathan combines academic training from the University of Chicago with hands-on HPC and cloud optimization experience that cut processing costs by up to 80% in previous projects. He also contributes to applied open-source tooling and research workflows, reflecting a pragmatic approach to reproducible, production-ready data science. Known for bridging rigorous experiment design with operational impact, he excels at turning complex, messy datasets into actionable business outcomes.
11 years of coding experience
12 years of employment as a software developer
BE Electronics and Communication engineering, BE Electronics and Communication engineering at College of Engineering, Guindy
Master of Science (M.S.) Analytics, Master of Science (M.S.) Analytics at University of Chicago
This package puts together commonly used preprocessing, variable construction & risk adjustment algorithms for medicaid data. The package currently supports MAX files. Support for TAF files is currently being added.
Contributions:176 commits, 3 PRs, 223 pushes in 1 year 7 months
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