Summary
Andrew Joros is a research computing engineer with 11+ years building AI/ML, cloud, and data-engineering solutions for environmental science, translating complex simulations and sensor networks into production-ready analytical pipelines. At the Desert Research Institute he has delivered end-to-end systems—from converting OpenFOAM CFD outputs for ML-driven snow transport models to real-time hydrologic dashboards and statewide groundwater analyses—supporting federal, state, and academic partners. He combines meteorological domain expertise with hands-on Python and AWS engineering to operationalize genomics, wildfire smoke dispersion, and climate monitoring workflows. Notably, he applies unsupervised ML (e.g., Self-Organizing Maps) to climate event classification and has a track record of integrating large geospatial and in-field sensor data into scalable cloud services. Based in Reno, he blends rigor from an atmospheric science MS with practical product-minded tooling that accelerates scientific decision-making.
11 years of coding experience
14 years of employment as a software developer
Bachelors of Science, Meteorology, Bachelors of Science, Meteorology at San José State University
Masters of Science, Atmospheric Science, Masters of Science, Atmospheric Science at University of Nevada, Reno
English, Spanish