Zachary Barry
Senior Machine Learning Engineer at Genentech USA
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolZachary Barry is a Senior Machine Learning Engineer and bioengineer with nine years of experience building ML/AI pipelines that turn microscopy images into actionable drug-discovery readouts. He has led production-grade solutions for single-cell phenotyping, mechanism-of-action elucidation, and AI-driven cell detection and segmentation at organizations including Novartis, Spring Discovery, and Genentech. A PhD-trained live-cell imaging specialist, he combines deep learning (notably transformer-based image embeddings) with practical image-processing to deliver robust, scalable analysis across fluorescence, brightfield, and in vivo imaging. Zachary also contributes to open-source data-cleaning tooling (pyjanitor), reflecting a hands-on approach to reproducible data engineering. Based in Cambridge, MA, he advises biotech teams on translating complex microscopy datasets into interpretable compound activity profiles.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Biological Engineering, Doctor of Philosophy (Ph.D.) Biological Engineering at Massachusetts Institute of TechnologyBachelor of Science (B.S.) Biomedical Engineering, Bachelor of Science (B.S.) Biomedical Engineering at Georgia Institute of Technology
Github Skills (12)
unit-testing10
pandas10
data-cleaning10
pytest10
unit-test10
python10
data-engineering10
dataframe9
data-model9
data-set9
user-data9
dataframes9
Programming languages (6)
Github contributions (5)
pyjanitor-devs/pyjanitor
Nov 2018 - Jan 2022
Clean APIs for data cleaning. Python implementation of R package Janitor
Role in this project:
Data Engineer
Data EngineerContributions:45 commits, 28 PRs, 41 pushes in 3 years 2 months
Contributions summary:Zachary primarily contributed to the `pyjanitor` library by implementing and improving DataFrame manipulation functionalities. Their work involved adding new methods such as `reorder_columns` and `collapse_levels`, along with associated unit tests. Furthermore, the user refactored existing functions, enhanced existing functionality by implementing the ability to deconcatenate tuples/lists/collections, and fixed issues related to categorical data types within the `concatenate_columns` method.
cleaningr-packagepythondatadata-science
lcbb/DeepScope
Apr 2017 - Aug 2017
Contributions:30 PRs, 106 pushes, 32 branches in 4 months
microscopy-imagesmoviesdeep-learningimage-segmentationcomputer-vision
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