Tom Kealy
Senior Data Scientist
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolTom Kealy is a senior data scientist in Berlin, currently applying rigorous analytics at HelloFresh. He brings a decade of experience across academia and industry, holding a PhD in Electrical and Electronics Engineering from Bristol and a Physics with Theoretical Physics degree from Imperial College London. His expertise spans statistical signal processing, high-dimensional inference, Python, Matlab, data analysis, and automation, with a strong emphasis on NLP and text analytics. An active open-source contributor, he develops PyMC educational resources and notebooks to teach Bayesian inference using PyMC, ArviZ, and Pandas. At the University of Bristol, he built an automated text-analysis system for the UK Hydrographic Office, including a web portal for tagging and a data-extraction engine that automated NtM updates, saving costs. Based in Berlin, he is open to opportunities in Germany or the USA, combining meticulous attention to detail with autonomous and collaborative problem-solving in complex analytics projects.
9 years of coding experience
5 years of employment as a software developer
Physics with Theoretical Physics, Physics, 1st, Physics with Theoretical Physics, Physics, 1st at Imperial College LondonDoctor of Philosophy (PhD), Electrical and Electronics Engineerins, Doctor of Philosophy (PhD), Electrical and Electronics Engineerins at Bristol
Stackoverflow
2,579reputation
212kreached
18answers
96questions
Github Skills (17)
bayesian-statistics10
python10
data-science10
pandas10
arviz10
pymc10
bayesian10
pymc310
jupyter-notebook10
bayesian-inference10
data-analysis10
recursion6
group-by6
dataframe6
matlab6
Programming languages (6)
Github contributions (5)
pymc-devs/pymc-resources
Mar 2022 - Jul 2022
PyMC educational resources
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 7 commits, 7 PRs in 3 months
Contributions summary:Tom contributed to educational resources related to Bayesian inference and statistics within the PyMC ecosystem. Their commits include adding and updating notebooks containing code, data, and explanations. The changes involve the implementation of Bayesian methods, data analysis, and the use of libraries like PyMC, Arviz, and Pandas to demonstrate key concepts and techniques. The changes are primarily focused on enhancing the educational material by including new chapters and code updates.
data-analysispythondata-sciencepymcbayesian-inference
TomKealy/scikit-causal
Apr 2020 - Feb 2024
Scikit-learn inspired estimators for causal machine learning
Contributions:2 PRs, 3 pushes, 5 branches in 3 years 10 months
causal-machine-learningcausalmachine-learningscikit-learnestimators
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