Summary
Kyle Dawson is a Lead Data Scientist with nine years of experience building end-to-end machine learning products and decision-grade analytics at the intersection of climate tech, geospatial intelligence, and digital agriculture. He combines a PhD in Atmospheric Science and a NASA postdoc background in LiDAR and aerosol remote sensing with hands-on engineering—deploying models and dashboards on Azure/AWS and productionizing UAV and satellite-derived pipelines. Kyle has led QA/QC forecasting models and interactive wind-processing apps that directly reduced uncertainty in methane surveys, demonstrating a rare ability to translate research-grade sensing into operational tools. His technical fluency spans Python, scikit-learn, GDAL, Plotly, and cloud data platforms, paired with Bayesian uncertainty quantification for robust predictive modeling. Colleagues rely on him to bridge stakeholders and complex science through clear documentation and customer-facing analytics. Based in Houston, he’s actively seeking opportunities to apply geospatial ML and emissions analytics to climate-focused product challenges.
9 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Atmospheric Science (Aerosol Physics and Chemistry), Doctor of Philosophy (Ph.D.), Atmospheric Science (Aerosol Physics and Chemistry) at North Carolina State University
Bachelor of Science, Atmospheric Science, Bachelor of Science, Atmospheric Science at University of Illinois Urbana-Champaign
English