Peadar Coyle
Founder at Cuhullin Ltd
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolPeadar Coyle is a founder and CTO with 14 years of experience building AI-native, production-grade data and audio platforms from London. He leads AudioStack’s API-first, low-code text-to-audio product while also advising clients as Principal Data Scientist at Cuhullin, translating probabilistic models and data engineering into measurable business value across fintech, media and adtech. A core contributor to the PyMC3 ecosystem, he combines deep Bayesian expertise with practical MLOps—having accelerated a marketing-attribution model’s time-to-value by 100x and authored examples like Bayesian logistic regression and a GARCH time-series demo. Comfortable across Python, AWS, Terraform and streaming pipelines, he mentors engineers, teaches data science, and shapes AI policy through industry and government steering groups. He’s equally interested in the culture and craft of engineering, publishing interviews with data science teams at Amazon, Spotify and Etsy that reveal how top teams ship real-world ML.
14 years of coding experience
4 years of employment as a software developer
Bachelor of Science modules Mathematics, Bachelor of Science modules Mathematics at The Open UniversityMaster's Degree Mathematics, Master's Degree Mathematics at University of LuxembourgA-Level A Levels in Physics Mathematics History and Biology, A-Level A Levels in Physics Mathematics History and Biology at Abbey Christian Brothers SchoolBachelor of Science (BSc) Physics and Philosophy, Bachelor of Science (BSc) Physics and Philosophy at University of Bristol
English
Stackoverflow
2,231reputation
341kreached
14answers
17questions
Github Skills (19)
bayesian-statistics10
probabilistic-programming10
python10
multiple-selection10
feature-selection10
pymc10
bayesian10
statistical-models10
variable-selection10
machine-learning9
time-series9
mcmc9
data-analysis9
ads6
audio6
Programming languages (16)
Github contributions (5)
pymc-devs/pymc
Dec 2015 - Dec 2018
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist
Data ScientistContributions:2 releases, 75 commits, 176 PRs in 3 years
Contributions summary:Peadar's commits primarily involve enhancements to the "Bayesian Logistic Regression" example, including improvements to the model and documentation. Their work spans exploratory data analysis, model selection using DIC and WAIC, and contributions to the example's author attribution. The user also created a new GARCH example for time series modeling. Further contributions include working on the Posterior Predictive Checks and the ARMA models with Oil data.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
springcoil/probabilisticprogrammingprimer
Aug 2018 - Feb 2021
Contributions:44 commits, 10 PRs, 33 pushes in 2 years 6 months
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