Grey Nearing
Research Scientist at Google
Zurich, Zurich, Switzerland
Summary
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Senior🎓
Top SchoolGrey Nearing is a research scientist based in Zurich with a decade of experience applying machine learning and AI to water and climate challenges, currently driving Google's FloodHub. With a PhD in Hydrology and prior roles at UC Davis, University of Alabama, and NASA Goddard, he bridges academic rigor and production-scale ML. He contributed key Transformer architecture work and evaluation metrics to the well-regarded neuralhydrology project, improving hydrologic forecasting performance and usability. Grey combines deep domain expertise in hydrology with hands-on ML engineering—designing positional encodings, schedulers, and robust training code—to move research models toward operational impact. He is selective about external engagements due to high demand, reflecting a focus on large-scale, high-impact projects.
10 years of coding experience
8 years of employment as a software developer
The University of ArizonaBachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Purdue University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
neural-network10
model-building10
transformer-models10
pytorch10
machine-learning10
python10
modeling10
model-driven10
model-driven-development10
data-analysis9
metric9
evaluation9
time-series9
Programming languages (5)
Github contributions (5)
neuralhydrology/neuralhydrology
Jan 2021 - Jun 2022
Python library to train neural networks with a strong focus on hydrological applications.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 5 commits, 2 PRs in 1 year 4 months
Contributions summary:Grey primarily focused on developing and implementing a Transformer model for hydrological applications, contributing significant code for the model's architecture and functionality. They added features like positional encoding, and the ability to concatenate or sum it to inputs. They also integrated a rate scheduler and documentation, demonstrating a commitment to model performance and usability. Furthermore, the user integrated a new metric for evaluation and performed bug fixes.
python-librarypythonhydrologicalneural-networksmachine-learning
grey-nearing/triple_collocation
Dec 2016 - Jul 2019
Contributions:4 commits, 3 pushes, 1 branch in 2 years 7 months
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