Jordan Silke
DevOps Engineer at PlanHub
Pembroke, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolJordan Silke is a DevOps engineer with four years of hands-on experience building cloud-native tooling and automation, currently driving infrastructure at PlanHub after progressive roles in cloud development and data analytics at BlueCat. With an academic foundation in genomics and bioinformatics (MSc) and a data science diploma, Jordan blends scientific rigor with practical software and DevOps practices to solve complex operational problems. He contributes to major open-source projects like scikit-learn, improving example notebooks and cleaning up deprecations—evidence of both machine learning literacy and attention to developer experience. Known for deliberately breaking things to learn how they fail, he pairs curiosity-driven experimentation with a commitment to fixing and hardening systems. Based in Pembroke, Ontario, he brings a multidisciplinary perspective that bridges research, analytics, and production engineering.
4 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Biopharmaceutical Science (Genomics), Bachelor of Science - BS, Biopharmaceutical Science (Genomics) at University of OttawaDiploma, Data Science, Diploma, Data Science at Lighthouse Labs
Stackoverflow
1reputation
0reached
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Github Skills (10)
scikit10
scikit-learn10
machine-learning10
python10
data-science10
data-analysis10
jupyter-notebook9
statistics8
matplotlib8
documentation7
Programming languages (4)
Github contributions (5)
scikit-learn/scikit-learn
Mar 2022 - Apr 2022
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:34 reviews, 5 commits, 5 PRs in 1 month
Contributions summary:Jordan contributed to the scikit-learn repository by primarily modifying and updating example notebooks. Their work involved converting existing Python code examples into notebook-style formats, enhancing the documentation and presentation of machine learning concepts. Additionally, the user addressed warnings, fixed typos, and improved code formatting in several example files, demonstrating an understanding of the library and its use cases. They also contributed to a deprecation warning cleanup, showcasing awareness of the project's API changes.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
jsilke/spoonderful
Feb 2022 - Feb 2022
Contributions:36 commits, 14 pushes in 12 days
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