James Campbell is a founding partner at Move Analytical, where he leads the design of software and AI systems for metabolomics workflows emphasizing reproducibility, interpretability, and quantitative rigor. His work focuses on the interface between measurement systems and decision-making, with particular attention to how uncertainty, calibration, and model structure influence downstream conclusions.
He is one of the co-creators of Great Expectations, an open-source data quality framework that formalized expectations-based validation as a practical paradigm for data systems. Earlier in his career, he worked in cybersecurity and political modeling at the Central Intelligence Agency developing analytical approaches for reasoning under uncertainty in operational contexts.
Across domains, his work has centered on building systems in which humans and computational models collaborate effectively—where workflows are transparent, assumptions are inspectable, and results can be both trusted and challenged. He studied Mathematics and Philosophy at Yale and International Security at Georgetown.