Andrew Montanez
Head Of Engineering at DataCebo
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Montanez is Head of Engineering at DataCebo with 10 years of experience building ML-driven backend systems and synthetic data tooling from San Francisco. He combines hands-on Python development and backend/devops expertise with research roots at MIT, where he worked on the Synthetic Data Vault and copula modeling. An active open-source contributor to SDV projects (CTGAN, Copulas, SDV), he balances model development with package and build health—managing dependency upgrades, tests, and versioning to keep libraries production-ready. Known for turning research-grade algorithms into reliable, well-tested pipelines, he leads teams that deliver scalable synthetic-data solutions for real-world privacy and feature-generation problems.
10 years of coding experience
7 years of employment as a software developer
Massachusetts Institute of Technology
Github Skills (31)
data-generation10
constraints10
pytorch10
python10
package-management10
pandas10
machine-learning10
generative-adversarial-network10
constraint10
ci-cd10
synthetic-data10
generative-adversarial-networks10
devops10
data-science9
testing9
Programming languages (2)
Github contributions (5)
sdv-dev/SDV
Jul 2018 - Jan 2023
Synthetic data generation for tabular data
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:29 releases, 1066 reviews, 224 commits in 4 years 7 months
Contributions summary:Andrew primarily contributed to the codebase by implementing and refining constraints for synthetic data generation. Their work involved fixing issues related to handling lambda functions and functions returned from other functions within the constraint framework. They also addressed and resolved bugs related to duplicate IDs when utilizing reject-sampling, improving the functionality and robustness of the synthetic data generation process. These changes focused on core components of the synthetic data generation process using Python and potentially related frameworks.
relational-datasetssynthetictime-seriessynthetic-datamulti-table
sdv-dev/CTGAN
Jul 2021 - Jan 2023
Conditional GAN for generating synthetic tabular data.
Role in this project:ML Engineer
ML EngineerContributions:12 releases, 91 reviews, 20 commits in 1 year 6 months
Contributions summary:Andrew primarily worked on the CTGAN project, a conditional GAN for generating synthetic tabular data. Their commits involved updating the `rdt` dependency version, renaming and modifying tests related to synthesizers, and bumping the version of the library, indicating direct involvement in the model's core functionality and maintenance. They also fixed warnings and addressed package maintenance, which are important aspects of maintaining the project's health and stability.
pytorchdeep-learningconditionaltabular-datagenerative-adversarial-network
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