Bryson Bonham leads AI enablement and data science in regulated environments. The work pairs scientific AI depth with production discipline: formulation and small-molecule ML, indication discovery, agentic clinical systems, and the evaluation infrastructure required to trust outputs after launch.
He has owned AI platforms and portfolios across life sciences and healthcare, from strategy through global deployment. The through-line is consistent: design the use case, measure whether it works, govern it for the environment, then deploy.
Leads three AI teams building evaluation and agentic systems for clinical voice AI and life sciences use cases. Established LLM-as-a-judge workflows, real-time monitoring, multimodal signal extraction, and async prompt insertion on the evaluations team, then extended that foundation to agentic orchestration and first production deployments in regulated healthcare.
Led three to four AI product teams spanning biomedical literature extraction, clinical trial acceleration, indication discovery, and formulation and small-molecule AI. Owned platform strategy, technical direction, and delivery, including hiring that doubled team size across data science, engineering, and scientific subject matter experts.
Delivered indication discovery from proof of concept to production with CatBoost, TabPFN, and agentic prioritization for clinical decision-grade therapeutic ranking. Built formulation AI from zero to one with UniMol, ChemProp, and AIMNet2, improving R&D cycle time by roughly twenty percent. Stood up a reusable Scientific AI platform for pipelines, MLOps, LLM evaluation, and governance, giving executives visibility into impact and ROI.
Led teams of fifteen to twenty on production AI in client environments, contributing to greater than fifty percent improvement and approximately four hundred million dollars in NPV on a life sciences portfolio. Drove adoption through interpretable models, uncertainty-aware validation, and responsible AI governance, including the first global operational rollout in the portfolio. Built real-world evidence patient embeddings with knowledge graphs to estimate therapeutic efficacy and prioritize novel therapeutics.
Led an AI transformation at a financial services institution that generated more than one hundred million dollars in incremental revenue through segmentation and channel optimization. Redesigned outreach for programs reaching five to ten million members at a major healthcare payer and led AI workstreams across more than ten healthcare and life sciences clients.
Deployed multimodal models for patient readmission risk at a large healthcare payer and built enterprise analytics dashboards for real-time performance visibility. As National Learning Director, trained more than one hundred data scientists in production ML across supervised, unsupervised, and reinforcement learning.
MS Analytics, NC State Institute for Advanced Analytics (2018). BS Biochemistry, Virginia Tech (2014). Graduate coursework in bioinformatics, Johns Hopkins University. AI/ML in Medicine, Cambridge Centre for AI in Medicine (CCAIM).