Jacques Kvam
San Francisco, California, United States
Summary
🤩
RockstarJacques Kvam is a San Francisco–based data scientist and engineer with 15 years of experience focused on machine learning tooling and practical ML systems. A Kaggle Master and prize winner, he builds developer-facing tools (author of Bowtie) and contributes to scientific Python projects such as scikit-learn, where he improved gradient boosting usability and documentation. He blends formal training across computer science, signal processing, wireless communication, and embedded systems with continuous self-study via MOOCs in ML, databases, algorithms, and convex optimization. Jacques is comfortable across the stack—from JupyterLab UX improvements (vim keybindings and cell navigation) to core algorithmic tweaks—reflecting a rare mix of research-minded rigor and pragmatic engineering. Colleagues value his knack for making complex ML workflows more usable and his tendency to surface small but impactful developer ergonomics improvements.
15 years of coding experience
Stackoverflow
2,976reputation
340kreached
78answers
5questions
python
top-5%
Github Skills (24)
javascript10
python10
data-science10
scikit10
machine-learning10
jupyterlab10
gradient-boosting10
vim10
jupyterlab-extension10
scikit-learn10
codemirror10
statistics9
front-end-development9
typescripts8
typescript-types8
Programming languages (16)
Github contributions (5)
jwkvam/jupyterlab-vim
Nov 2017 - Jul 2019
:neckbeard: Vim notebook cell bindings for JupyterLab
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:12 releases, 56 commits, 41 PRs in 1 year 8 months
Contributions summary:Jacques primarily focused on enhancing the user experience within a JupyterLab environment by implementing Vim-like keybindings. Their contributions included setting up Vim keymaps, modifying the editor's behavior, and adding shortcuts for common actions like cell manipulation and mode switching. Furthermore, the user integrated code to allow for cell navigation and execution, enhancing the overall usability of the JupyterLab interface. The user actively worked on improving the interaction with the editor to allow running code and keeping the user in command mode.
jupyterlab-vimjupyterlab-extensionvimnotebookjupyterlab
scikit-learn/scikit-learn
Oct 2012 - Apr 2014
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:7 commits, 1 PR, 2 comments in 1 year 6 months
Contributions summary:Jacques made several contributions related to the `scikit-learn` library, which is focused on machine learning. Their work primarily involved modifying and enhancing the `gradient_boosting` module. This included fixing keyword arguments, improving output formatting, and adding verbose output options for more detailed feedback during algorithm execution. They also updated the documentation, suggesting a focus on improving the usability and clarity of the library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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