Soledad Galli
Data Scientist Instructor
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolSoledad Galli is a data scientist and machine learning engineer with 11+ years of experience building production-grade ML systems across finance and insurance, from credit-risk and fraud models to claims processing. She founded Train in Data, authored two hands-on Python books on feature engineering and feature selection, and built Feature-engine — an open-source library with 300k+ monthly downloads and 2k+ stars that few practitioner-focused libraries achieve. A seasoned instructor and course author, she has trained 70k+ students and translates research-grade rigor into practical pipelines that move experiments out of notebooks and into decisions. Her background includes academic research with high-impact publications and grant-winning proposals, giving her a rare blend of statistical rigor and product-focused engineering. She contributes to popular ML libraries (imbalanced-learn, BorutaPy, Eli5, tsfresh) and emphasizes reproducible, well-documented tooling—evident from extensive documentation and course repos on hyperparameter optimization and imbalanced data. Based in Berlin, she combines mentorship and team leadership with hands-on coding and open-source stewardship.
11 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (PhD), Cell/Cellular and Molecular Biology, Sobresaliente (highest distinction), Doctor of Philosophy (PhD), Cell/Cellular and Molecular Biology, Sobresaliente (highest distinction) at Universidad de Buenos Aires
English, Spanish, German
Stackoverflow
887reputation
66kreached
28answers
14questions
Github Skills (30)
python10
data-science10
scikit10
machine-learning10
feature-engineering10
hyperopt10
mask-rcnn10
hyperparameter-optimization10
scikit-learn10
faster-rcnn10
jupyter-notebook10
documentation10
optuna10
data-analysis10
unit-testing9
Programming languages (4)
Github contributions (5)
solegalli/machine-learning-imbalanced-data
Nov 2020 - Nov 2022
Code repository for the online course Machine Learning with Imbalanced Data
Role in this project:
Data Scientist
Data ScientistContributions:51 commits, 9 PRs, 54 pushes in 2 years
Contributions summary:Soledad focused on fixing a bug within the `return_minority_perc` function and adding new content to section 3 and 9. Section 3 focuses on metrics and the bug fix demonstrates an understanding of how the code base will work. Section 9 focused on Probability and Calibration Notebooks. The overall repository focuses on Machine Learning and is very related to data science.
pythondata-scienceimbalanced-datamachine-learning-coursemachine-learning
feature-engine/feature_engine
Mar 2020 - Jan 2023
Feature engineering package with sklearn like functionality
Role in this project:Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:6 releases, 777 reviews, 154 commits in 2 years 10 months
Contributions summary:Soledad's commits indicate significant involvement in feature engineering tasks, including code updates, bug fixes, and improvements to existing functionalities within the feature_engine library. They updated and improved existing code for various features, discretizers and imputers, setup and configured continuous integration workflows. Their work involved modifying setup layout, updating requirements, adding and adjusting ci config, and incorporating style checks, suggesting a focus on code quality, model preparation, and overall project maintenance.
pythonfeature-extractiondata-sciencesklearnmachine-learning
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