Artem Sobolev
Software Development Engineer at Workday
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolArtem Sobolev is a Software Development Engineer with 11 years of experience and 6+ years focused on frontend engineering for FinTech, banking, and hospitality, now based in Vancouver. He builds high-performance, user-centric React and TypeScript applications (Next.js, Material UI) and has a track record of modernizing stacks—migrating CRA to Vite, enabling SSR, and creating reusable UI libraries with Module Federation. Artem blends frontend craftsmanship with DevOps sensibilities, implementing CI/CD, IaC workflows, monitoring (Sentry, Smartlook) and automated deployments to drive faster releases and measurable business impact. He’s contributed machine-learning fixes to scikit-learn around SVMs, reflecting strong analytical rigor beyond typical frontend work. A pragmatic mentor and documentation advocate, he accelerates teams through reusable components, automated pipelines, and clear onboarding materials.
11 years of coding experience
7 years of employment as a software developer
PBD, Tourism Destination Development, PBD, Tourism Destination Development at Thompson Rivers UniversityMaster's degree, Finance & Banking, Master's degree, Finance & Banking at State University of Management (SUM)
English, Russian
Stackoverflow
6,019reputation
593kreached
142answers
0questions
scikit-learn
top-1%
algorithm
top-5%
machine-learning
top-1%
python
top-5%
pandas
top-5%
classification
top-5%
Github Skills (18)
python10
data-science10
scikit10
machine-learning10
svm10
scikit-learn10
testing9
javascript9
pandas9
algorithm9
classification9
statistics7
documentation7
neural-network6
lazy-evaluation6
Programming languages (6)
Github contributions (5)
scikit-learn/scikit-learn
Mar 2015 - Mar 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:12 commits, 7 PRs, 73 comments in 10 days
Contributions summary:Artem primarily focused on improving and refining the machine learning aspects of the scikit-learn library. Their contributions involved fixing sign inconsistencies in the SVM implementation, ensuring the decision function behaved as expected, and correcting documentation. They also updated the tutorial to add a description of SVR. The user's work demonstrated expertise in SVM algorithms and the related mathematics, and ensured the code was accurate.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
bayesgroup/deepbayes
Nov 2016 - Mar 2020
Contributions:136 commits, 124 pushes in 3 years 5 months
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.