Saulius Lukauskas
Data Science Manager
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolSaulius Lukauskas is a Data Science Manager in Berlin with 13 years of experience bridging research-grade machine learning and production software engineering. He holds a PhD from Imperial College London and spent 5+ years embedding ML into biological research, including postdoctoral work linked to high-impact publications. Saulius has transitioned into product-focused data science leadership at companies like buynomics and GetYourGuide, driving teams that turn complex models into business value. He contributes to notable open-source visualization projects (e.g., performance and dendrogram fixes for seaborn) and has deep practical experience improving plotting and labeling algorithms. Known for pairing rigorous scientific thinking with pragmatic engineering, he stays actively engaged with deep learning advances to unlock new scientific and product insights.
13 years of coding experience
8 years of employment as a software developer
Bachelor of Science (BSc) with Honours in Artificial Intelligence, Artificial Intelligence, Bachelor of Science (BSc) with Honours in Artificial Intelligence, Artificial Intelligence at The University of EdinburghDoctor of Philosophy (PhD), Biomathematics, Bioinformatics, and Computational Biology, Doctor of Philosophy (PhD), Biomathematics, Bioinformatics, and Computational Biology at Imperial College London
Github Skills (14)
data-visualizations10
pandas10
data-visualization10
data-visualisation10
visualization10
ggplot10
visualizations10
python10
r10
matplotlib10
data-science9
numpy9
cran9
plaintext9
Programming languages (10)
Github contributions (5)
mwaskom/seaborn
Aug 2015 - Mar 2018
Statistical data visualization in Python
Role in this project:
Data Scientist
Data ScientistContributions:20 commits, 7 PRs, 17 comments in 2 years 7 months
Contributions summary:Saulius focused on improving the performance and functionality of the `seaborn` library, specifically concerning the visualization of matrix-like data. They optimized the heatmap and dendrogram plotting functions by utilizing more efficient algorithms, such as `LineCollection` for dendrogram drawing, and by making adjustments to how tick labels are handled. Furthermore, they addressed testing issues and ensured consistent dendrogram sizing, showcasing an emphasis on both performance and visual correctness.
data-analysispythonstatisticsdata-sciencestatistical
slowkow/ggrepel
Jan 2017 - Jan 2017
:round_pushpin: Repel overlapping text labels away from each other in your ggplot2 figures.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:7 commits, 1 PR, 5 comments in 3 days
Contributions summary:Saulius focused on enhancing the `ggrepel` package, specifically by introducing and refining functionality related to the placement of text labels in plots. They implemented initial features to control label placement using x and y limits, then refactored the code for clarity by renaming variables. The user's work also involved adapting these changes to related geom layers. The updates refined the core features of the package.
pythoncranggplot2rstatsdata-visualisation
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