Andrew Montanez
Head Of Engineering at DataCebo
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Montanez is a Head of Engineering based in San Francisco with a decade of experience building production-grade software and machine learning systems. He rose through hands-on engineering roles—including research at MIT where he contributed to the Synthetic Data Vault—and now leads engineering at DataCebo, blending product delivery with technical strategy. His open-source contributions to prominent synthetic data projects (SDV, Copulas, CTGAN) show deep expertise in generative modeling, data constraints, and maintaining ML libraries in Python across versions and builds. He’s equally comfortable fixing subtle bugs (e.g., reject-sampling duplicate IDs, lambda handling in constraints) and improving CI/build tooling for library distribution. Colleagues rely on him to bridge research-grade models and robust backend engineering, turning experimental ML into maintainable services. Outside the obvious, he brings a maker’s background in embedded and app development, reflecting a practical curiosity that informs his systems-level decisions.
10 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng Electrical Engineering and Computer Science, Master of Engineering - MEng Electrical Engineering and Computer Science at Massachusetts Institute of Technology
Github Skills (27)
data-generation10
pytorch10
python10
package-management10
pandas10
machine-learning10
generative-adversarial-network10
cicd10
constraint10
synthetic-data10
devops10
data-science9
testing9
datatable9
tabular9
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
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.