Summary
Daniel Lumian is a founder and applied researcher with 10 years of experience translating complex problems into clear, reproducible data workflows across industry and government settings. He builds and evaluates analytical prototypes—prioritizing clarity, uncertainty communication, and decision rationale before chasing scale—drawing on expertise in Python, predictive modeling, computer vision, and NLP. His work spans FDA surveillance, federal data-program assessment, and privacy-conscious ML for financial datasets, with a knack for automating pipelines and onboarding through documentation-first design. Trained as a PhD behavioral scientist, he combines experimental rigor from fMRI and emotion-regulation research with practical engineering to bridge abstraction levels and make assumptions explicit. Based in Denver, he specializes in solutions that reduce friction for teams while surfacing methodological tradeoffs that inform durable, maintainable systems.
10 years of coding experience
6 years of employment as a software developer
Data Science Immersive, Data Science Immersive at Galvanize
Doctor of Philosophy - PhD, Psychology, Doctor of Philosophy - PhD, Psychology at University of Denver
University of California, Los Angeles