Markus Löning
Machine Learning Engineer at Snap Inc.
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolMarkus Löning is a Machine Learning Engineer with nine years’ experience, a PhD from UCL and visiting research ties to The Alan Turing Institute, now working at Snap in Paris. He builds production-ready ML systems that bridge research and operations—previously delivering real‑time, high-resolution commodity forward curves at Shell using Python, C#, Kubernetes, AzureML and MLflow. Co-creator and active contributor to sktime, he focuses on making time-series ML both accessible and practically useful, improving documentation and example usability for wider adoption. Trained in Philosophy, Politics & Economics, he combines rigorous technical research with a broad systems and product perspective, often prioritizing user experience in tooling and deployment.
9 years of coding experience
1 year of employment as a software developer
Erasmus Exchange, Erasmus Exchange at Universidad Complutense de MadridVisiting Student, Visiting Student at The Alan Turing InstituteUniversity College LondonInternational Baccalaureate, International Baccalaureate at Helene Lange GymnasiumM.A. Philosophy & Economics, M.A. Philosophy & Economics at University of BayreuthBachelor of Arts (Hons.) Philosophy Politics & Economics, Bachelor of Arts (Hons.) Philosophy Politics & Economics at Lancaster University
Spanish, English, German
Stackoverflow
835reputation
101kreached
22answers
1question
Github Skills (13)
time-series10
documentation10
data-analysis10
jupyter-notebook9
machine-learning9
scikit-learn6
pandas6
seaborn6
python6
matplotlib6
rpy26
classification6
data-science6
Programming languages (12)
Github contributions (5)
sktime/sktime
Nov 2018 - Dec 2021
A unified framework for machine learning with time series
Role in this project:
Data Scientist & Machine Learning Engineer
Data Scientist & Machine Learning EngineerContributions:16 releases, 1060 reviews, 911 commits in 3 years 1 month
Contributions summary:Markus updated example notebooks, adding titles and web links to improve documentation and navigation. The user focused on enhancing the presentation and accessibility of the shapelet transform example, indicating a focus on user experience and the practical application of time series analysis techniques within the sktime framework. Their contributions mainly centered around improving the presentation and accessibility of the information.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
mloning/dotfiles
Jan 2022 - Mar 2025
Contributions:16 PRs, 127 pushes, 12 branches in 3 years 2 months
dotfilesconfiguration-filesvimzsh
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.