Summary
Daniel Kick is a genome editing data scientist who applies deep learning, statistical genetics, and crop growth modeling to predict plant traits from genomic and environmental data, with nine years of experience spanning academia and government labs. He led an NIFA-funded effort to build "Environmentally Aware" genomic-selection tools and helped forge a multi-institution collaboration while producing stakeholder-facing presentations and technical trainings. Previously a neuroscientist, he brings a rare multimodal background—published work on single-cell classification and experience modeling physiological time series—that informs his computational approach to complex biological data. Now at Bayer, he combines practical breeding impact with rigorous model development and a track record of mentoring and centralizing reproducible documentation for team adoption.
9 years of coding experience
1 year of employment as a software developer
Central High School
Bachelor's of Science, Biology, General, Senior, Bachelor's of Science, Biology, General, Senior at Truman State University
University of Missouri