Jonathan Weyn
Data Scientist
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolJonathan Weyn is a data scientist at Microsoft who applies machine learning to advance medium-range to sub-seasonal weather forecasting, building on a PhD in Atmospheric Sciences from the University of Washington. With eight years of experience spanning graduate research and operational ML work, he specializes in deep convolutional neural networks for ensemble forecasts and studies of predictability and error growth. His background in physics and hands-on research—from drought-driven vegetation regime shifts to severe-weather predictability—gives him a strong quantitative foundation and a pragmatic sense for real-world forecasting challenges. Based in Redmond, he blends academic rigor with production-focused ML development, translating atmospheric science insights into scalable forecasting solutions. A lifelong weather enthusiast, he brings both domain passion and proven technical depth to improving how we predict the atmosphere.
8 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Atmospheric Sciences and Meteorology, 3.87/4.0, Doctor of Philosophy (Ph.D.), Atmospheric Sciences and Meteorology, 3.87/4.0 at University of WashingtonBachelor's Degree, Physics, 3.95/4.0, Bachelor's Degree, Physics, 3.95/4.0 at The University of Texas at AustinMcNeil High School
English, French
Github Skills (8)
deep-learning8
machine-learning8
benchmark7
data-driven4
forecast3
forecasting3
ecology1
hydrology1
Programming languages (2)
Github contributions (5)
jweyn/DLWP
Oct 2018 - Aug 2019
Deep Learning Weather Prediction
Contributions:82 commits, 94 pushes, 3 branches in 9 months
deep-learning
jweyn/ensemble-net
Feb 2018 - Nov 2018
Tools for deep learning and ensemble forecasting
Contributions:114 commits, 2 PRs, 79 pushes in 9 months
deep-learning
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