M Pacer
Senior Machine Learning Scientist at Netflix
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolM Pacer is a Senior Machine Learning Scientist at Netflix with 13 years of experience melding research-grade causal inference and human explanation work with production ML systems. After earning a PhD in Cognitive Science from UC Berkeley, M helped lead Netflix’s Data Science Platform and notebook ecosystem, progressing from core Jupyter developer to senior roles shaping notebook-first data workflows. A Project Jupyter Steering Council member and prolific open-source contributor, M has improved foundational tools like IPython, nbformat, nbconvert, papermill and JupyterLab—work that quietly improves reproducibility for thousands of researchers. Their background bridges cognitive-science rigor and pragmatic engineering: they design models and systems that make causal reasoning and explainability usable at scale. Based in Berkeley, M pairs deep academic expertise with hands-on contributions to community tooling, including enhancements that span front-end UX and backend validation for the Jupyter ecosystem.
13 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Cognitive Science, Doctor of Philosophy (PhD), Cognitive Science at University of California, BerkeleyBachelor of Science (B.S.), Psychology, Bachelor of Science (B.S.), Psychology at Yale University
English, German, Italian
Github Skills (45)
json10
markdown10
notebook10
javascript10
restructuredtext10
pytest10
python10
jupyter10
validators10
testing10
ipython10
ui-design10
jupyterlab10
bibtex10
latex10
Programming languages (20)
Github contributions (5)
jupyter/nbconvert
Sep 2015 - Apr 2019
Jupyter Notebook Conversion
Role in this project:
Backend Developer & QA Engineer
Backend Developer & QA EngineerContributions:395 commits, 168 PRs, 73 pushes in 3 years 7 months
Contributions summary:M primarily contributed to the quality assurance and testing of the project, with modifications to testing procedures and code examples. They added tests for specific compilation methods and functionality related to PDF conversion, focusing on areas like strikethrough usage. The user also addressed various issues, including fixing JSON errors, commenting on the code example, and correcting syntax. Furthermore, they enhanced the test suite's robustness by improving test coverage and addressing potential areas of failure.
jupyter-notebook
scipy-conference/scipy_proceedings
Jun 2015 - Jul 2018
Tools used to generate the SciPy conference proceedings
Role in this project:Back-end Developer
Back-end DeveloperContributions:322 commits, 127 PRs, 66 pushes in 3 years 1 month
Contributions summary:M primarily focused on enhancing the functionality of the SciPy conference proceedings generation tools. Their contributions involved modifying the `publisher` module to support the integration of BibTeX files for bibliography management within reStructuredText files, ensuring accurate citation rendering. They also addressed errors and added features related to PDF generation. Furthermore, the user made changes to ensure compatibility with Python 3 and improved error handling within the configuration files.
scipy
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