Michal Kosinski
Data & Analytics Director
Krakow, Lesser Poland Voivodeship
Summary
🤩
Rockstar🎓
Top SchoolMichal Kosinski is a pragmatic data and analytics leader with 15+ years of experience combining hands-on data science, software engineering and team leadership across companies from Motorola and Zendesk to startups like Tidio and Unit8. He has repeatedly turned complex data problems into production-grade ML and analytics products—shipping churn and conversion models, large-scale time-series tooling (contributions to the popular Darts library), and automated prediction pipelines integrated with business actions. At Tidio he scaled a data org, introduced A/B testing practices and data democratization, while at Zendesk and Unit8 he built terabyte-scale pipelines, experimented with causality and explainability, and adapted forecasting tools to datasets of 100M+ samples. Fluent in both strategic roadmap-setting and hands-on implementation, he excels at forming teams, setting data strategy, and promoting data thinking across organizations. An applied mathematician by training and a former Six Sigma Green Belt, he blends rigorous statistical thinking with practical engineering to deliver measurable business impact.
11 years of coding experience
22 years of employment as a software developer
Master Degree Applied Mathematics, Master Degree Applied Mathematics at Uniwersytet Marii Curie-Skłodowskiej w Lublinie
English, Polish
Stackoverflow
428reputation
34kreached
19answers
0questions
Github Skills (19)
pytorch10
python10
machine-learning10
time-series10
forecasting10
forecast10
data-science9
anomaly-detection9
unit-testing9
deep-learning8
numpy8
csv6
data-analysis6
scikit-learn6
data-mining6
Programming languages (3)
Github contributions (5)
unit8co/darts
Apr 2021 - Sep 2021
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:77 reviews, 49 commits, 28 PRs in 5 months
Contributions summary:Michal primarily focused on enhancing the `darts` library's time series forecasting and anomaly detection capabilities. They implemented batch prediction for PyTorch models, including updates to core functions and adding unit tests for improved functionality. Furthermore, the user added a new utility for splitting the dataset. The contributions demonstrate expertise in integrating and optimizing PyTorch models for time series analysis.
forecastingpython-libraryanomalypythontime-series-analysis
mkos/colaberas
Feb 2018 - Nov 2020
Contributions:2 PRs, 23 pushes, 3 branches in 2 years 9 months
pythonboilerplatedeep-learninggoogle-colabnets
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.