Tyler Kirby
Co-founder, Chief Data Scientist
Missouri, United States
Summary
👤
Senior🎓
Top SchoolTyler Kirby is a co-founder and Chief Data Scientist driving practical AI solutions across startups and enterprises. Based in Missouri, he combines a Classics background with advanced machine learning, leading computer vision pipelines, Bayesian modeling, and NLP initiatives across industries from logistics to collectibles. Since 2023 he has led Signal Mine as Co-founder and Chief Data Scientist, while previously building and guiding data science teams at ALGRTHM and Handled, deploying high-impact models and rapid product experimentation. An active open-source contributor, he has helped advance the Classical Language Toolkit (CLTK)—improving Latin syllabification, tests, and language resources—demonstrating breadth beyond traditional CS domains. His career path blends research, production-grade software, and leadership, underpinned by an MS in Computer Science from Washington University in St. Louis and a Classics bachelor's from New College of Florida, reflecting a rare mix of humanities and engineering.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Washington University in St. LouisBachelor's Degree, Classics, Bachelor's Degree, Classics at New College of Florida
German, Latin, Greek
Stackoverflow
263reputation
22kreached
16answers
0questions
Github Skills (15)
linguistics10
nlp10
python10
historical10
testing10
github8
dom-manipulation6
mongoose6
algorithm6
nodejs6
mongojs6
javascript6
mongodb6
firebase6
database6
Programming languages (3)
Github contributions (5)
cltk/cltk
Dec 2017 - Aug 2019
The Classical Language Toolkit
Role in this project:
Back-end Developer
Back-end DeveloperContributions:34 commits, 26 PRs, 2 pushes in 1 year 8 months
Contributions summary:Tyler primarily focused on improving the Latin syllabifier within the Classical Language Toolkit. Their contributions involved fixing issues related to macrons and the "ui" diphthong, enhancing the accuracy of syllable parsing. They also added unit tests to validate these improvements, ensuring the robustness of the syllabification process. Additionally, the user worked on integrating stop words and Marathi corpus documentation.
nlplingpythonlatinnltk
TylerKirby/latin-vocab-project
Sep 2019 - Jan 2023
Contributions:2 PRs, 7 pushes, 2 branches in 3 years 4 months
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