Niclas Jern
SVP, Engineering & Data Services
Turku, Mainland Finland
Summary
👤
Senior🎓
Top SchoolNiclas Jern is an experienced engineering leader with 14+ years building distributed data systems, embedded firmware and mobile products, currently serving as SVP of Engineering & Data Services at Walkbase in Turku. He combines hands-on architecture and implementation skills—specialising in high-throughput event processing—with a leadership style he describes as the "Chief Unblocker," focusing on enabling teams to deliver. A co-founder of Walkbase, he has guided the company from research-stage indoor positioning and sensor hardware to a global analytics platform integrated across the STRATACACHE family. Niclas has shipped cross-platform mobile apps and taught MSc-level mobile development, and his open-source contributions to Go machine-learning code demonstrate attention to code quality and performance. He brings a practical research background (MSc in Computer Engineering) and a knack for translating embedded and sensor innovations into scalable cloud data products.
14 years of coding experience
9 years of employment as a software developer
Master’s Degree, Computer Engineering, Master’s Degree, Computer Engineering at Åbo AkademiInternational Baccalaureate Diploma Programme, International Baccalaureate Diploma Programme at Vasa Övningsskola
Swedish, Finnish, English, German
Github Skills (5)
code-optimization10
machine-learning10
refactoring10
go10
algorithms8
Programming languages (6)
Github contributions (5)
sjwhitworth/golearn
Jul 2014 - Jul 2014
Machine Learning for Go
Role in this project:
Back-end Developer
Back-end DeveloperContributions:23 commits, 3 comments, 3 issues in 2 days
Contributions summary:Niclas primarily focused on refactoring and optimizing the existing Go code base. Their contributions include simplifying range statements, streamlining conditional logic, and replacing verbose increment operations. They also made improvements to several key files, including utilities, bagging, and linear models, suggesting involvement in core logic refinement and performance improvements. This indicates a focus on code quality and efficiency within the machine learning project.
gomachine-learning
gophergala/watchtower
Jan 2015 - Jan 2015
Contributions:35 commits, 17 pushes in 1 day
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