Tamar Grey
Software Engineer at Benchling
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolTamar Grey is a software engineer with five years of experience building data-driven products and developer tools, currently contributing at Benchling after a multi-year engineering role at Alteryx. She has strong Python and full-stack chops, having implemented production features like video APIs and offline mode for sports analytics at Second Spectrum and shipped AutoML and feature-engineering improvements in prominent open-source projects featuretools and EvalML. Tamar's open-source work highlights a practical focus on data preprocessing, feature selection, and performance optimization—skills that bridge research-grade data science and production reliability. She holds a BS in Computer Science and Molecular Biology from MIT, and early research work led to a published GWAS analysis, reflecting a knack for turning complex data into reproducible insights. Based in Cambridge, MA, she brings a mix of applied ML tooling, backend systems design, and a user-centered approach to shipping impactful software.
5 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Computer Science and Molecular Biology, Bachelor of Science - BS Computer Science and Molecular Biology at Massachusetts Institute of Technology
Stackoverflow
101reputation
2kreached
5answers
0questions
Github Skills (19)
python10
data-science10
scikit10
pandas10
machine-learning10
feature-engineering10
automl10
scikit-learn10
data-analysis9
hyperparameter-tuning8
variable-selection8
testing8
feature-selection8
multiple-selection8
time-series7
Programming languages (4)
Github contributions (5)
alteryx/featuretools
Jul 2020 - Nov 2022
An open source python library for automated feature engineering
Role in this project:
Data Scientist
Data ScientistContributions:10 releases, 415 reviews, 101 commits in 2 years 3 months
Contributions summary:Tamar focused on improving the `featuretools` library, primarily by addressing issues related to feature engineering and data type handling. Their contributions included fixing boolean data type mismatches, ensuring consistent feature ordering, and improving test cases. They implemented feature selection functionalities like removing highly null and correlated features, demonstrating a focus on optimizing feature engineering pipelines. The user also added a feature selection guide.
python-librarypythondata-scienceautomated-machine-learningmachine-learning
alteryx/evalml
Oct 2022 - Jan 2023
EvalML is an AutoML library written in python.
Role in this project:Data Scientist
Data ScientistContributions:259 reviews, 26 commits, 43 PRs in 3 months
Contributions summary:Tamar primarily contributed to the development and enhancement of the EvalML library, focusing on AutoML functionality. Their work included implementing the OrdinalEncoder component, integrating it into the AutoML search process and time series featurization. They also worked on performance optimization by implementing a partial dependence fast mode and fixing related bugs, particularly when using DFS Transformers. These changes demonstrate a focus on improving data preprocessing, model interpretability, and AutoML capabilities within the project.
pythondata-sciencemodel-selectionoptimizationmachine-learning
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