Sajaysurya Ganesh
Machine Learning Engineer at Scale AI
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolSajaysurya Ganesh is a Machine Learning Engineer with nine years of experience building ML systems in early-stage startups and research settings, currently at Scale AI after five years leading ML at ZOE. He combines a strong academic foundation from UCL in Computational Statistics and Machine Learning with hands-on product delivery across health and applied research domains. Notable for contributing to sktime—implementing and testing baseline estimators and pipeline integration—he demonstrates attention to foundational, well-tested components that improve wider time-series tooling. His background spans design engineering to research internships at The Alan Turing Institute, giving him a pragmatic systems perspective alongside statistical rigor. Based in London, he excels at bridging prototype research and production ML, with a track record of shipping reliable, test-covered models and pipelines. Colleagues describe him as a technically curious leader who prioritises robustness and reproducibility in ML workflows.
9 years of coding experience
9 years of employment as a software developer
School, School at Bharathi Vidya BhavanBachelor of Engineering (B.E.) Production Engineering, Bachelor of Engineering (B.E.) Production Engineering at PSG College of TechnologyUniversity College London
English, Tamil
Github Skills (10)
data-analysis10
testing10
scikit10
machine-learning10
time-series10
pytest10
python10
scikit-learn10
gridsearchcv9
pipeline9
Programming languages (7)
Github contributions (5)
sktime/sktime
Jan 2019 - Mar 2019
A unified framework for machine learning with time series
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:1 review, 96 commits, 4 pushes in 2 months
Contributions summary:Sajaysurya implemented and tested a dummy classifier and a dummy regressor within the sktime framework, demonstrating a focus on foundational machine learning components. They added tests to ensure comprehensive coverage of these new estimators, validating their functionality and compatibility. Furthermore, the user included tests for pipeline integration, indicating an understanding of how these new components fit within the broader sktime ecosystem and its functionalities.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
sajaysurya/dotfiles
Feb 2018 - Feb 2022
Contributions:173 pushes, 15 branches in 4 years 1 month
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