Gaurav Sheni
Staff Software Architect at DataCebo
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolGaurav Sheni is a Staff Software Architect with 11 years of experience building machine learning products and platforms, currently leading architecture for enterprise synthetic data at DataCebo. Previously the first engineering hire at Feature Labs, he helped create and scale both enterprise and open-source tools for automated feature engineering before the team's acquisition by Alteryx. At Alteryx he led teams building Alteryx CoPilot (LLM-powered) and a no-code AutoML product, blending product leadership with hands-on ML engineering. A committed open-source contributor, he is a core maintainer of Featuretools and has contributed meaningful primitives and fixes across popular AutoML projects like EvalML. He combines backend engineering, data science, and developer tooling expertise, with a practical focus on data typing via projects like Woodwork. Based in New York, he pairs entrepreneurial startup experience with enterprise delivery and a strong belief in open-source ecosystems.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at Wake Forest University
Github Skills (19)
python10
data-science10
testing10
pandas10
machine-learning10
feature-engineering10
numpy10
automl10
data-analysis10
scikit8
documentation8
scikit-learn8
variable-selection8
feature-selection8
multiple-selection8
Programming languages (11)
Github contributions (5)
alteryx/featuretools
Sep 2017 - Jan 2023
An open source python library for automated feature engineering
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:6 releases, 653 reviews, 205 commits in 5 years 5 months
Contributions summary:Gaurav contributed to the featuretools repository by implementing and fixing code related to the automated feature engineering library. They worked on improvements for the flight and demo data, resolved issues in variable types and tests, and added several new aggregation primitives, including `NumTrue`, `TimeSinceFirst`, and other aggregation methods. Their contributions included debugging code, adding new features, and updating existing functionality within the library.
python-librarypythondata-scienceautomated-machine-learningmachine-learning
alteryx/evalml
Apr 2020 - Jan 2023
EvalML is an AutoML library written in python.
Role in this project:Data Scientist
Data ScientistContributions:132 reviews, 39 commits, 97 PRs in 2 years 10 months
Contributions summary:Gaurav contributed significantly to the EvalML library, primarily focusing on improving and extending its machine learning capabilities. They added new metrics, such as Balanced Accuracy, and updated the documentation for existing functionalities, including the AutoClassificationSearch class. Additionally, the user fixed bugs, updated demo dataset links, and contributed to the testing and CI/CD processes, including the implementation of workflows related to dependency checks. The user's contributions directly improved the library's functionality and user experience.
pythondata-sciencemodel-selectionoptimizationmachine-learning
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