Summary
Scott Simpkins is a data scientist with a decade of experience translating wet-lab problems into computational solutions, currently applying machine learning and network analysis at Octant in the San Francisco Bay Area. His work spans LC-MS method development, high-throughput chemical-genetic screens in yeast, and predictive modeling of synthetic lethal interactions from genome-wide CRISPR screens to nominate cancer drug combinations. Trained as a PhD in Bioinformatics and Computational Biology, he blends statistics, data mining, and cheminformatics to support collaborators and to build interactive visual tools that make complex experimental data actionable. Scott has led international multi-disciplinary projects and developed pipelines that moved from discovery to experimental validation, reflecting both deep domain knowledge and practical engineering. He is motivated, independent, and a quick learner who prefers solving biological questions by integrating diverse computational approaches rather than one-size-fits-all methods.
10 years of coding experience
University of Minnesota Twin Cities
Bachelor of Arts - BA, Biochemistry and Molecular Biology, Bachelor of Arts - BA, Biochemistry and Molecular Biology at Gustavus Adolphus College