Ethan Weber is a Senior Data Scientist with a decade of experience building production-grade ML systems and data products, currently at Plaid after leading AI efforts on Messenger at Meta. He combines research-level AI expertise—an academic background including EECS at MIT and PhD work at Berkeley—with hands-on engineering: refactoring full-stack NeRF tooling, containerizing projects, and shipping scalable pipelines in Airflow and PySpark. His career spans startups to large tech, delivering impact in recommendation, causal and probabilistic modeling, and automated feature engineering for high-stakes decisions like insurance and social behavior inference. Ethan is comfortable moving models from research to deployment, optimizing both MLOps and model performance, and contributing to open-source projects that improve developer workflows. Based in New York, he brings a product-minded, metrics-driven approach and a knack for reorganizing messy codebases into reproducible, package-oriented systems. An uncommon thread across his roles is merging deep academic grounding with pragmatic DevOps and full-stack contributions that accelerate team delivery.
10 years of coding experience
8 years of employment as a software developer
NYC Data Science Academy
Bachelor of Science - BS Economics, Bachelor of Science - BS Economics at Tulane University
Contributions:4 releases, 141 reviews, 230 commits in 10 months
Contributions summary:Ethan restructured the project to use packages and refactored the code to improve its structure. They introduced new files, including a Dockerfile and a setup file, which implies setting up and managing development environments and dependencies. Furthermore, they updated existing codebase which may involve working on both backend and frontend as well as DevOps.
Contributions:16 commits, 44 pushes, 1 branch in 8 months
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