Ehsanย Gharib-nezhad
Senior Data Scientist
San Francisco Bay Area United States
Summary
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Top SchoolEhsan Gharib-nezhad is a Senior Data Scientist and LLM specialist based in the San Francisco Bay Area with nine years of experience building production-grade ML and AI systems across aerospace and retail. He led ML teams at NASA, reducing analysis time from months to minutes with 1D-CNN regressors, authored a popular TelescopeML PyPI package and fine-tuned LLaMA/Microsoft/phi-1 models for astronomy applications. At ClearDemand he now architects advanced LLM solutions for retail, while also advising product teams as an AI Business Fellow at Perplexity. His background combines deep domain science (PhD-level astrophysics and atmospheric modeling) with hands-on engineeringโdeploying models on AWS/SageMaker, creating reproducible Python tooling, and publishing ML research. Notably, he translated insights from 1,000+ arXiv papers into improved LLM precision and maintains a track record of turning complex scientific datasets into usable, production-ready models.
9 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy - PhD Physical Chemistry [Astrophysics], Doctor of Philosophy - PhD Physical Chemistry [Astrophysics] at Arizona State UniversityData Science Immersive program, Data Science Immersive program at General AssemblyMaster of Science - MS Physical Chemistry, Master of Science - MS Physical Chemistry at University of Tehran
English, Persian
Stackoverflow
1reputation
0reached
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0questions
Github Skills (17)
convolutional-neural-networks10
planetary-science10
planetary10
planets10
transmission10
telescope10
planet10
astronomy9
climate-model9
python8
astrophysics8
atmospheric-modelling8
journal7
machine-learning7
open-access6
Programming languages (2)
Github contributions (5)
EhsanGharibNezhad/DNA_Sequencing_Using_Machine_Learning_Algorithms
Feb 2022 - Feb 2022
Contributions:33 commits in 5 days
EhsanGharibNezhad/TelescopeML
Nov 2023 - Oct 2024
Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra
Contributions:2 reviews, 100 PRs, 204 pushes in 11 months
astronomyatmospheric-modellingconvolutional-neural-networksmachine-learningtelescope
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