Alexandros Metsai

Senior Machine Learning Scientist at Ringier AG

Zurich, Zurich, Switzerland
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Summary

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Senior
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Alexandros Metsai is a Senior Machine Learning Scientist based in Zurich with 10 years of experience building research-driven, production-ready ML systems that serve millions of users. He began in deep learning research with peer-reviewed publications and transitioned to product-focused engineering, deploying models used by news professionals and human rights teams. At Ringier he shapes scalable algorithms and the cloud infrastructure that operationalizes them, blending algorithmic rigor with practical software engineering. His background spans recommender systems, anomaly detection, video summarization and multimedia forensics, reflecting a strong applied research-to-production arc. An active contributor to open-source documentation—improving usability in the widely used librosa audio library—he brings an uncommon attention to developer experience alongside model performance.
code10 years of coding experience
job5 years of employment as a software developer
bookMaster of Engineering - MEng, Computer Engineering, Master of Engineering - MEng, Computer Engineering at Aristotle University of Thessaloniki (AUTH)
languagesGreek, Albanian, English
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1,907reputation
133kreached
92answers
1question
Badges
python
top-5%
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Github Skills (18)

python10
matplotlib10
audio-analysis10
librosa10
documentation10
signal-processing7
digital-signal-processing7
music7
neural-network6
deep-learning6
tensorflow6
scikit-learn6
tqdm6
dataframe6
numpy6

Programming languages (7)

C++CSSShellJavaScriptJupyter NotebookCythonPython

Github contributions (5)

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librosa/librosa

May 2019 - May 2019

Python library for audio and music analysis
Role in this project:
userTechnical Writer
Contributions:38 commits, 3 PRs, 8 comments in 1 day
Contributions summary:Alexandros's contributions primarily focused on adding plot examples to the documentation within the librosa library. These examples demonstrate the usage of various librosa functions, covering a wide range of audio analysis techniques. By incorporating `plt.show()` in numerous example code blocks across different modules, the user enhanced the documentation by enabling immediate visualization and understanding of the functions' outputs. This directly improves the usability and educational value of the library's documentation.
python-librarydtwpythonlibrosaaudio
Contributions:1 release, 11 pushes, 1 branch in 3 years 2 months
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Alexandros Metsai - Senior Machine Learning Scientist at Ringier AG