Nathaniel Forde
Staff Data Scientist
Dublin, Ireland
Summary
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Rockstar🎓
Top SchoolNathaniel 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.
11 years of coding experience
9 years of employment as a software developer
Master's degree (Research) Philosophy and Logic, Master's degree (Research) Philosophy and Logic at Trinity College DublinMaster of Science (MSc) Mathematical Logic, Master of Science (MSc) Mathematical Logic at University of AmsterdamBachelor 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
Github Skills (40)
bayesian-statistics10
causal10
variational-inference10
probabilistic-programming10
python10
jupyter10
experimental-design10
causality10
causal-inference10
statistical-inference10
model-building10
mcmc10
pymc10
regression-models10
bayesian10
Programming languages (3)
Github contributions (5)
NathanielF/pymc-examples
Oct 2022 - Mar 2025
Examples of PyMC models, including a library of Jupyter notebooks.
Contributions:206 pushes, 19 branches in 2 years 5 months
pythonpymcjupyter-notebooknotebooksjupyter
NathanielF/CausalPy
Jun 2023 - Jun 2024
A Python package for causal inference in quasi-experimental settings
Contributions:78 pushes, 4 branches in 11 months
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