Veronika Maurerová
Search & Service Manager at L'Oréal
Prague, Prague, Czechia
Summary
🤩
Rockstar🎓
Top SchoolVeronika Maurerová is a pragmatic Search & Service Manager and experienced trade marketing leader with 11 years of cross-functional experience in retail activation, team leadership and data-driven category management at L'Oréal in Prague. She combines strong commercial instincts and meticulous project execution—managing budgets, suppliers and SAP workflows—with a fast-learning technical bent evidenced by contributions to the popular H2O-3 open-source ML platform where she implemented XGBoost feature interaction constraints and multinomial AUC functionality. Comfortable at the intersection of marketing, operations and analytics, Veronika excels at turning market insights into measurable in-store results and automated scoring improvements. Her background in finance (MSc) and hands-on production coordination gives her a rare mix of analytical rigor and executional discipline.
11 years of coding experience
2 years of employment as a software developer
Master's degree, Finance, Master's degree, Finance at Vysoká škola ekonomická v PrazeGymnázium Na Vítězné Pláni
Czech, English, German, Slovak
Github Skills (9)
machine-learning10
xgboost10
h2o10
api10
python10
apidoc10
early-stopping9
r9
javadoc8
Programming languages (5)
Github contributions (5)
h2oai/h2o-3
Nov 2018 - May 2022
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:482 reviews, 377 commits, 269 PRs in 3 years 6 months
Contributions summary:Veronika primarily focused on the H2O-3 Python client, contributing significantly to the implementation of feature interaction constraints within XGBoost. They fixed bugs and implemented the correct scoring function. Also, the user worked on implementing multinomial AUC/AUCPR functionality by creating a Java backend, propagating it to Python and R, and adding early stopping and tests. In addition to this, they also worked on addressing various bugs related to H2O-3 and contributed to the documentation by improving early stopping explanations.
xgboostgampythonk-meansautoencoders
maurever/thesis
Aug 2017 - Sep 2018
Contributions:2 pushes, 1 branch in 1 year 1 month
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