Alexandre Andorra
Host Creator
Miami, Florida, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexandre Andorra is a Senior Applied Scientist based in Miami who specializes in Bayesian modeling and probabilistic programming, applying these techniques to sports analytics, electoral forecasting and business problems. He co-founded PyMC Labs, scaling it from zero to $1M profit in two years while also creating IntuitiveBayes courses and hosting the Learning Bayesian Statistics podcast with 12k+ monthly listeners. A core developer and active contributor to PyMC and ArviZ, he has improved user-facing tooling (plotting, compare/loo warnings) and routinely validates example notebooks to raise UX and reproducibility. With an unconventional blend of a History BSc and advanced training in management and public policy, he excels at turning complex probabilistic models into actionable, bottom-line impact.
7 years of coding experience
3 years of employment as a software developer
Master of Science - MSc Management, Master of Science - MSc Management at HEC Paris
French, English, German, Spanish
Github Skills (20)
bayesian-statistics10
probabilistic-programming10
python10
statistics10
exploratory-data-analysis10
statistic10
numpy10
mcmc10
arviz10
pymc10
bayesian10
statistical-modeling10
pymc310
jupyter-notebook10
statistical-modelling10
Programming languages (5)
Github contributions (5)
pymc-devs/pymc-resources
Jun 2019 - Jul 2020
PyMC educational resources
Role in this project:
Data Scientist
Data ScientistContributions:20 reviews, 15 commits, 37 PRs in 1 year 1 month
Contributions summary:Alexandre contributed answers and solutions to the end-of-chapter practice problems, focusing on Bayesian inference and statistical modeling within the PyMC3 framework. Their work included implementing and analyzing various statistical models, demonstrating a solid understanding of Bayesian methods. The user's contributions were primarily centered on applying Bayesian techniques to a range of problems, including binomial and Poisson regressions. The user also performed checks and updated plots within the notebooks, showing engagement with the educational material.
data-analysispythondata-sciencepymcbayesian-inference
pymc-devs/pymc
Jan 2020 - Oct 2022
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:Data Scientist
Data ScientistContributions:1 release, 323 reviews, 31 commits in 2 years 9 months
Contributions summary:Alexandre made substantial contributions to the PyMC repository, focusing on example notebooks and code related to Bayesian modeling and probabilistic programming. Their commits updated and improved several notebooks, including those for the radon example, posterior predictive checks, and the Mauna Loa example. These updates involved code cleanup, typo fixes, modifications to sampling defaults, and improvements to the overall presentation and functionality of the examples, demonstrating a focus on model validation and practical application.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
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