Nathaniel Forde

Staff Data Scientist

Dublin, Ireland
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Summary

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Nathaniel Forde is a Staff Data Scientist based in Dublin with 11 years of experience applying causal inference, experimentation and statistical modelling to product and business problems. He has progressed from analytics and software roles into senior data science leadership at Personio after driving R&D work at CarTrawler and leading insights teams at Paddy Power Betfair. A contributor to the PyMC open-source project, Nathaniel blends rigorous probabilistic modelling with practical experimentation design to turn ambiguous questions into measurable outcomes. His academic background in mathematical logic and philosophy underpins a formal, principled approach to inference and problem framing that few data scientists bring to product analytics. Colleagues rely on him for clarity in complex causal questions and for shipping statistically sound experiments that scale across organisations.
code11 years of coding experience
job9 years of employment as a software developer
bookMaster's degree (Research) Philosophy and Logic, Master's degree (Research) Philosophy and Logic at Trinity College Dublin
bookMaster of Science (MSc) Mathematical Logic, Master of Science (MSc) Mathematical Logic at University of Amsterdam
bookBachelor of Arts (B.A.) Liberal Arts and Sciences/Liberal Studies, Bachelor of Arts (B.A.) Liberal Arts and Sciences/Liberal Studies at University College Dublin
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Github Skills (24)

bayesian-statistics10
variational-inference10
probabilistic-programming10
python10
data-science10
marketing10
causal-inference10
mcmc10
pymc10
regression-models10
bayesian10
jupyter-notebook10
bayesian-inference10
statistics8
computational-science7

Programming languages (5)

TeXJavaScriptHTMLJupyter NotebookPython

Github contributions (5)

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NathanielF/pymc-examples

Oct 2022 - Jun 2026

Examples of PyMC models, including a library of Jupyter notebooks.
Contributions:218 pushes, 22 branches in 3 years 8 months
pythonpymcjupyter-notebooknotebooksjupyter
NathanielF/CausalPy

Jun 2023 - Apr 2026

A Python package for causal inference in quasi-experimental settings
Contributions:131 pushes, 7 branches in 2 years 9 months
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