Hannah Frick
Senior Software Engineer at Posit PBC
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolHannah Frick is a Senior Software Engineer based in London with 11 years of experience bridging data science and software engineering. Currently at Posit, she focuses on building robust tooling for data workflows and has hands-on experience contributing to the popular tidymodels ecosystem—adding features like step_select() and refining preprocessing steps in recipes. Her background spans applied research roles at UCL and Universität Innsbruck through industry data science positions at Mango Solutions, giving her a strong foundation in statistical modeling and reproducible analysis. She holds a PhD and combines rigorous academic training with pragmatic engineering, often improving test coverage and reliability in data pipelines. Known for thoughtful contributions to open-source ML tooling, she brings both deep domain knowledge and a collaborator’s mindset to cross-functional teams.
10 years of coding experience
10 years of employment as a software developer
Ludwig Maximilian University of MunichDoctor of Philosophy (PhD), Doctor of Philosophy (PhD) at Leopold-Franzens Universität InnsbruckThe University of Glasgow
English, German, French
Stackoverflow
166reputation
736reached
8answers
0questions
Github Skills (14)
data-preprocessing10
machine-learning10
feature-engineering10
r10
testing9
tidyverse9
dplyr9
tidymodels6
parsnip6
logistic-regression6
glmnet6
gam6
mgcv6
glm6
Programming languages (8)
Github contributions (5)
tidymodels/recipes
Feb 2021 - Dec 2022
Pipeable steps for feature engineering and data preprocessing to prepare for modeling
Role in this project:
Data Scientist
Data ScientistContributions:71 reviews, 74 commits, 28 PRs in 1 year 10 months
Contributions summary:Hannah contributed to the `recipes` repository, which focuses on data preprocessing and feature engineering for machine learning models. The commits involved modifying and adding tests for the `step_rm()` function, demonstrating a focus on refining existing preprocessing steps. The user also added the `step_select()` function. These contributions are directly related to enhancing the functionality of the data preprocessing pipeline.
pythondata-preprocessingmachine-learningengineeringprepare
tidymodels/poissonreg
Mar 2021 - Jan 2023
parsnip wrappers for Poisson regression
Contributions:1 release, 2 reviews, 66 commits in 1 year 10 months
regressionparsnippoissonwrapperspoisson-regression
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