Joel Nothman
Staff Machine Learning Engineer at Canva
Sydney, New South Wales, Australia
Summary
🤩
Rockstar🎓
Top SchoolJoel Nothman is a Staff Machine Learning Engineer with 18 years of experience building ML-driven products, leading teams, and shipping pragmatic AI features at scale—currently designing generative and design-assist systems at Canva. A former core contributor to scikit-learn and active maintainer across NumPy, SciPy, IPython and NLTK, he brings deep NLP and scientific-Python expertise together with strong API and documentation craftsmanship. He has led research-to-production engineering in academia, running consultancy teams that translate complex research questions into reliable software and reproducible analysis. Polyglot and fast-moving in Python (and Cython), Java and TypeScript, he balances product-minded pragmatism with a rigorous engineering culture of tests, reviews and clear APIs. Known for empathic teaching and cross-discipline communication, he often bridges researchers, product teams and industry partners to unlock new data-driven insights. Outside engineering he’s an amateur tenor and single father, a detail that underscores his capacity for disciplined creativity and care.
18 years of coding experience
8 years of employment as a software developer
The University of Sydney
English, Hebrew
Stackoverflow
3,593reputation
1.4mreached
75answers
6questions
selenium
top-5%
nlp
top-5%
scikit-learn
top-5%
python
top-5%
numpy
top-5%
selenium-webdriver
top-5%
Github Skills (49)
algorithm10
algorithms10
scipy10
restructuredtext10
python10
data-science10
sphinx10
scikit10
testing10
pandas10
nltk10
sparse-matrix10
machine-learning10
ipython10
data-structure10
Programming languages (21)
Github contributions (5)
numpy/numpydoc
Nov 2016 - Mar 2022
Numpy's Sphinx extensions
Role in this project:
Technical Writer
Technical WriterContributions:87 commits, 76 PRs, 39 pushes in 5 years 4 months
Contributions summary:Joel primarily focused on improving the documentation of the `numpydoc` project. Their contributions included adding a guide to the documentation format, documenting the use of the plot directive, and clarifying aspects of the See Also section. The user also made edits to the README file and corrected links, suggesting a focus on enhancing the project's overall documentation and user experience.
pythonnumpysphinx-extensionextensionssphinx
scikit-learn/scikit-learn
Mar 2013 - Apr 2022
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:9 releases, 337 reviews, 978 commits in 9 years 3 months
Contributions summary:Joel's commits primarily focus on improving and extending the scikit-learn library's capabilities, with an emphasis on machine learning metrics and model evaluation. The contributions include fixing bugs related to the performance and behavior of classification metrics. They also contributed enhancements to a variety of machine learning functions. Furthermore, they worked on ensuring that the library supported sparse data and had a consistent API.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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