Jim Holmström
Software Developer at Stockholm Rust
Stockholm County, Sweden
Summary
👤
Senior🎓
Top SchoolJim Holmström is a software developer and data scientist with 14 years of experience building production-ready systems and ML tooling across startups and enterprise teams. He has hands-on expertise in machine learning engineering, QA and test automation—contributing to high-profile open-source projects like scikit-learn by improving metric tests and performance. Jim has built low-latency, resilient bot-protection services and SDKs at Imperva, led time-series disaggregation work at Watty, and most recently worked at NextML before joining Eir Insurance. Beyond engineering he co-founded and helps run Stockholm AI and a large community group, and now chairs a 120-unit tenant association—underscoring an uncommon mix of technical depth and practical organizational leadership.
14 years of coding experience
12 years of employment as a software developer
Separate Courses Electrical and Electronics Engineering, Separate Courses Electrical and Electronics Engineering at University of GävleBachelor's degree Master's degree Master of engineering's degree Physics Computer Science, Bachelor's degree Master's degree Master of engineering's degree Physics Computer Science at KTH Royal Institute of TechnologyExtra courses Electronic and Computer Software Engineering, Extra courses Electronic and Computer Software Engineering at Polhemskolan Gymnasium
English, Swedish, German
Stackoverflow
1reputation
224reached
0answers
1question
Github Skills (10)
scikit-learn10
testing10
python10
scikit10
data-science9
data-analysis9
machine-learning9
statistics9
numpy8
filemaker6
Programming languages (9)
Github contributions (5)
scikit-learn/scikit-learn
Apr 2013 - May 2013
scikit-learn: machine learning in Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:10 commits in 12 days
Contributions summary:Jim significantly contributed to the quality assurance of the scikit-learn library by extending and improving existing test cases. They focused on adding more comprehensive testing for the `confusion_matrix` and `auc` metrics, incorporating non-integer labels and addressing related bugs. Furthermore, the user enhanced code style and efficiency by refactoring code and optimizing performance.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
Jim-Holmstroem/ArchLinux
Sep 2017 - Oct 2022
Contributions:56 pushes, 1 branch in 5 years 2 months
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