Lukasz Mentel
Principal Data Scientist
Oslo, Norway
Summary
🤩
Rockstar🎓
Top SchoolLukasz Mentel is a Principal Data Scientist based in Oslo with 13 years of experience building ML-driven products and leading high-performing data teams across fintech, industrial SaaS, and startups. He combines hands-on full-stack data science—modeling, systems and UX—with strategic leadership from founding a SaaS startup to heading data at Vipps/Vipps MobilePay and Storebrand. His background as a PhD computational chemist informs a rigorous, research-led approach to practical ML problems like time series and predictive maintenance. An active contributor to the sktime time-series framework, he has improved cross-version robustness and test infrastructure, showing attention to production quality and open-source ecosystems. Colleagues know him for recruiting and mentoring top talent and for translating complex industrial data into machine-learning-ready products. He brings a rare mix of academic depth, startup grit and enterprise delivery experience that accelerates data initiatives from prototype to user-facing impact.
13 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Chemistry, Doctor of Philosophy - PhD, Computational Chemistry at Vrije Universiteit Amsterdam (VU Amsterdam)Master of Science (M.Sc.), Chemistry, Master of Science (M.Sc.), Chemistry at Nicolaus Copernicus University
Stackoverflow
21reputation
456reached
3answers
1question
Github Skills (14)
scikit10
pandas10
machine-learning10
time-series10
sktime10
python10
scikit-learn10
data-science9
pytest9
deserialization6
htmx6
django-rest-framework6
matplotlib6
django6
Programming languages (13)
Github contributions (5)
sktime/sktime
Nov 2021 - Dec 2022
A unified framework for machine learning with time series
Role in this project:
Data Scientist
Data ScientistContributions:5 releases, 225 reviews, 111 commits in 1 year 1 month
Contributions summary:Lukasz's contributions primarily involve debugging and refactoring code related to time series analysis and machine learning functionalities within the `sktime` framework. They fixed compatibility issues across different pandas versions in the `_diff_transform` and `test_wilcoxon` functions, improving the library's robustness. The user also refactored legacy test configurations and made enhancements to the testing framework. Furthermore, the user addressed a failure in a `FeatureUnion` test and corrected method names, which suggests involvement in maintaining and improving the project's core components.
machine-learningtime-seriesscikit-learntime-series-classificationtime-series-regression
lmmentel/awesome-time-series
Oct 2021 - Jan 2023
Resources for working with time series and sequence data
Contributions:16 reviews, 107 commits, 20 PRs in 1 year 3 months
time-seriespythonmachine-learningtime-series-analysisanomaly-detection
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