Fabio Vera - Senior Data Scientist

Fabio Vera

Senior Data Scientist

New York, New York, United States

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Summary

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Rockstar

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Top School

Fabio Vera is a Senior Data Scientist with nine years of experience building production-ready ML and causal inference tools, currently advancing coding agents and maintaining Microsoft’s EconML package. He has deep hands-on experience implementing and hardening orthogonal machine learning models—having fixed input handling, resolved circular imports, and refactored code to align with scikit-learn conventions for the high-impact ALICE/EconML project. Prior work spans NLP for clinical decision support at Cohere Health and financial ML solutions at Spinnaker Analytics, reflecting a balance of research rigor and applied engineering. Based in New York, he pairs a Master’s in Computer Science from UIUC with strong open-source collaboration, quietly specializing in making complex causal methods robust and production-ready.

9 years of coding experience

6 years of employment as a software developer

Bachelor's degree Computer Science Mathematics, Bachelor's degree Computer Science Mathematics at Tufts University

Master's degree Computer Science, Master's degree Computer Science at University of Illinois Urbana-Champaign

Spanish

Stackoverflow

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Github Skills (8)

scikit-learn10

machine-learning10

econometrics10

python10

causal-inference10

scikit10

refactor9

refactoring9

Programming languages (3)

HTMLJupyter NotebookPython

Github contributions (5)

py-why/EconML

Apr 2022 - Jan 2023

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Role in this project:

Back-end Developer

Contributions:1 release, 117 reviews, 23 commits in 9 months

Contributions summary:Fabio primarily contributes to the codebase by addressing issues related to input processing and resolving circular import problems. They enhanced the `StatsModelsLinearRegression` model by improving its input handling mechanisms, making the model more robust. Additionally, they resolved a circular import issue within the `econml` package to ensure the smooth functioning of the causal forest DML functionality. Furthermore, the user refactored the codebase to align with sklearn refactor.

decision-makingfairness-mlpythonoutcomeproblems

fverac01/ProcessingVisualizer

Apr 2018 - Dec 2018

Contributions:8 pushes, 1 branch in 8 months

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