Summary
Tan Zhou is a Senior Data Scientist with a decade of experience applying machine learning, Bayesian methods, and geospatial analytics to real-world environmental and supply-chain problems. Currently at Grainger and previously supporting Bayer Crop Science through Colaberry, he builds automated pipelines, processes large Sentinel-2 and LiDAR datasets at scale (Domino & AWS S3), and delivers uncertainty-aware models for soil and bio-workflow prediction. His academic grounding—PhD in Ecosystem Science & Management from Texas A&M—underpins practical expertise in waveform LiDAR processing, Bayesian decomposition, and remote sensing R/Python packages. He has a track record of turning noisy, unstructured environmental data into operational recommendations like soil survey recommender systems and seed prescription fields. Colleagues benefit from his blend of research rigor and production-focused engineering, including tools and R packages for ICESat-2 and waveform lidar. Based in Missouri, he brings a rare combination of field experience, high-performance computing, and production ML for environmental and supply-chain applications.
10 years of coding experience
7 years of employment as a software developer
Master's degree, Environmental Science, Master's degree, Environmental Science at Beijing Normal University
Bachelor's degree, Environmental Science, Bachelor's degree, Environmental Science at Tianjin University of Science & Technology
Doctor of Philosophy - PhD, Ecosystem Science & Management, Doctor of Philosophy - PhD, Ecosystem Science & Management at Texas A&M University