Moyo Ajayi
AI Engineer at Plante Moran
Denver, Colorado, United States
Summary
👤
Senior🎓
Top SchoolMoyo Ajayi is an AI Engineer in Denver with nine years of experience translating environmental science and geospatial research into production-ready machine learning systems. With a PhD focus bridging Earth & Environmental Sciences and Environmental Engineering, he applies scientific rigor to build maintainable, business-focused ML code and MLOps pipelines. His background includes delivering climate and geochemistry-informed predictive models for clients at ERM and leading applied ML at Bridgestone Americas, plus academic work modeling methane emissions at scale. Comfortable across R and Python, GPU clusters, Azure deployments, and experiment tracking, he blends domain expertise in greenhouse gas science with practical engineering. Colleagues rely on him to turn noisy environmental data into robust features and reproducible models that inform operational decisions. Notably, he pairs field-derived knowledge of natural systems with production ML experience, making him effective at both research-driven discovery and enterprise delivery.
9 years of coding experience
6 years of employment as a software developer
High School, High School at East Anchorage High SchoolBachelor’s Degree, Environmental Biology, Bachelor’s Degree, Environmental Biology at Columbia University in the City of New YorkDoctor of Philosophy (Ph.D.), Environmental Engineering/Earth Science, Doctor of Philosophy (Ph.D.), Environmental Engineering/Earth Science at Vanderbilt University
English, Spanish
Github Skills (61)
pytorch9
python9
machine-learning9
natural-language-processing9
nlp9
data-visualization8
speech-recognition8
transformer8
data-analysis8
deep-learning8
ipython-magic8
bert8
tensorflow8
ui-components8
r-package7
Programming languages (4)
Github contributions (5)
907Resident/Ajayi_Moyo_Minimalist_DS_Resume
Dec 2020 - Jan 2025
Minimalist style DS resume
Contributions:4 PRs, 24 pushes, 14 branches in 4 years 1 month
907Resident/caldera-gases
Feb 2021 - May 2023
Field research conducted between 2017 and 2019 acquired hundreds of samples of diffuse CH4 and CO2 gas emissions. This repository contains the code used for analysis of the data. The public repository for the data will be shared when available.
Contributions:8 PRs, 44 pushes, 15 branches in 2 years 3 months
public-repositoryquantileemissionsgascarbon-emissions
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