Summary
Tokunbo Hiamang is a multidisciplinary data science leader with 11+ years of experience building scalable AI and analytics programs across healthcare, media, and adtech. He has led enterprise segmentation, experimentation, and predictive modeling efforts that translate complex data (from EPIC records to geospatial footprints and genomic profiles) into business-ready tools and executive dashboards. At Memorial Sloan Kettering he increased retention and acquisition through ML-driven segmentation and built an AWS NLP self-service analytics platform, and at Deloitte he shipped high-impact POCs and semantic platforms that accelerated drug submissions and saved millions. His recent work at Gilead focused on global HCP classification and model governance to operationalize omnichannel targeting across regions. Known as a pragmatic translator between executives and engineers, he combines commercial pharma strategy roots with deep statistical rigor to drive measurable outcomes. He’s equally comfortable designing cohort algorithms and teaching linear algebra, a blend that helps him both mentor teams and operationalize cutting-edge analytics.
11 years of coding experience
8 years of employment as a software developer
Associate's degree Mathematics, Associate's degree Mathematics at Brookdale Community College
Master of Science (M.S.) Biostatistics, Master of Science (M.S.) Biostatistics at Rutgers University
English, Yoruba, Latin