Tom Kealy
Senior Data Scientist
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolTom Kealy is a Senior Data Scientist with a decade of experience blending academic rigor and commercial impact, currently applying statistical signal processing and high-dimensional inference at HelloFresh from his base in Berlin. He holds a PhD in Electrical and Electronics Engineering from Bristol and a first-class degree in Theoretical Physics from Imperial, bringing deep expertise in Bayesian methods, Python, Matlab and automation. Tom has a strong track record in research-to-production projects—building NLP pipelines, web portals and text-extraction systems that delivered measurable cost savings for the UK Hydrographic Office—and he contributes educational Bayesian resources to the prominent PyMC community. Comfortable working autonomously or in teams, he combines meticulous attention to detail with a practical focus on scalable analytics, and is open to international roles in Germany or the USA.
10 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 (16)
bayesian-statistics10
pandas10
arviz10
jupyter-notebook10
pymc10
bayesian10
python10
bayesian-inference10
data-science10
data-analysis10
recursion6
matlab6
groupby6
exponentiation6
dataframe6
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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