Grant Buster
Data Scientist
Boulder, Colorado, United States
Summary
👤
Senior🎓
Top SchoolGrant Buster is a data scientist in Boulder with 11 years of experience applying engineering rigor to clean energy and resilience challenges. At the National Laboratory of the Rockies he has led development of cornerstone tools like the NSRDB, reV, and sup3r, combining climate science, energy-systems modeling, and generative ML to improve renewable integration and extreme-weather planning. Earlier work at NuScale honed his simulation-based risk analysis and automation skills, producing production-ready Python and MATLAB tools used in a U.S. SMR design certification effort. He holds an M.S. from UC Berkeley where he built and automated x‑ray tomography experiments and software—an unusual experimental-to-software background for an energy modeler. Colleagues rely on him to bridge deep technical implementation with policy-relevant energy planning.
10 years of coding experience
7 years of employment as a software developer
Master's Degree Nuclear Engineering, Master's Degree Nuclear Engineering at University of California, Berkeley
English
Stackoverflow
41reputation
483reached
2answers
0questions
Github Skills (65)
prediction-model10
high-resolution10
abstract-syntax-tree10
energy10
synthetic10
perl10
power-systems10
generative10
deep-learning10
rex10
refactoring10
modelica10
photovoltaic10
sarif10
hpc-applications10
Programming languages (5)
Github contributions (5)
NREL/elm
Sep 2023 - Feb 2025
ELM is a collection of utilities to apply Large Language Models (LLMs) to energy research.
Contributions:51 reviews, 27 PRs, 99 pushes in 1 year 5 months
energy-dataenergy-policylarge-language-modelsllmrenewable-energy
NREL/NRWAL
Dec 2020 - Nov 2022
The National Renewable Energy Laboratory Wind Analysis Libray (NRWAL)
Contributions:1 release, 24 reviews, 137 commits in 1 year 11 months
nationallaboratoryphotovoltaicwindanalysis
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