Summary
Matthew Dzievit is a data-driven soybean discovery breeder with over a decade of industry experience combining quantitative genetics, software development, and visualization to accelerate crop product development. At Corteva he builds pipelines and web apps that turn large-scale genotypic data into actionable insights—managing multiple Shiny apps and engineering tools that improved marker performance and quantified germplasm contributions. His PhD work produced novel genomic prediction and imputation approaches for massive SNP datasets, and he has first-author publications on trait dissection and population diversity. Matthew excels at bridging wet-lab breeding objectives with scalable analytics workflows, often independently designing end-to-end solutions that replace legacy tools. Recognized for operational excellence and creative scientific invention, he brings both practical breeding domain expertise and hands-on software craftsmanship to cross-functional teams. Located in the Des Moines area, he combines global collaboration experience with a knack for turning complex genetic data into intuitive decision support.
9 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Plant Breeding and Genetics, Doctor of Philosophy - PhD, Plant Breeding and Genetics at Iowa State University
BA, Biochemistry, Cell, and Molecular Biology, BA, Biochemistry, Cell, and Molecular Biology at Drake University, College of Arts and Sciences