Scott Votaw

Bothell, Washington, United States

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Summary

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Rockstar

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Top School

Scott Votaw is a cloud service architect specializing in AI/ML platforms that operate over large-scale data, bringing four years of hands-on experience building performant, production-ready systems. Based in Bothell, WA, he contributes to prominent open-source projects like Microsoft LightGBM and SynapseML, where his backend work improved streaming APIs, memory efficiency, and LightGBM integration for distributed and multiclass training. He combines a strong systems mindset—optimizing C APIs, dataset streaming, and concurrency—with applied ML engineering to make models more scalable and reliable in real-world pipelines. With a background in environmental management and ecology from Duke and a BS in wildlife and fisheries and psychology from Texas A&M, he blends domain curiosity with technical rigor, often approaching ML infrastructure problems with an interdisciplinary perspective.

4 years of coding experience

Masters of Environmental Management, Natural Resource Ecology, Masters of Environmental Management, Natural Resource Ecology at Duke University

BS, Psychology, Wildlife and Fisheries, BS, Psychology, Wildlife and Fisheries at Texas A&M University

Github Skills (21)

apache-spark10

c-language10

data-science10

data-mining10

big-data10

machine-learning10

distributed-systems10

gradient-boosting10

ml10

scala10

mle10

lightgbm10

cprogramming-language10

apidoc9

pyspark9

Programming languages (2)

C++Scala

Github contributions (5)

microsoft/SynapseML

Mar 2022 - Jan 2023

Simple and Distributed Machine Learning

Role in this project:

Back-end Developer & ML Engineer

Contributions:98 reviews, 23 commits, 50 PRs in 9 months

Contributions summary:Scott primarily focused on improving the LightGBM integration within the SynapseML project. Their contributions include fixing issues related to class loading in IntelliJ, refactoring parameter systems, fixing model saving, and addressing multiclass training with initial scores. The code changes reveal involvement in the LightGBM booster, dataset aggregation, and parameter handling, indicating significant work in the backend machine learning aspects of the project.

fairness-mlpythoncaffe2model-deploymentcognitive-services

microsoft/LightGBM

Jul 2022 - Nov 2022

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Role in this project:

Back-end Developer & ML Engineer

Contributions:29 reviews, 6 commits, 9 PRs in 4 months

Contributions summary:Scott primarily focused on enhancing the LightGBM library's core functionality, specifically improving its streaming APIs to reduce client-side memory usage and handle distributed data more effectively. Their work involved modifying the C API, dataset loading, and internal data structures to support true streaming and concurrency. They implemented and tested features for streaming datasets, including dense and sparse data formats, which improved performance and efficiency. These changes are instrumental in handling larger datasets and optimizing the machine learning framework's performance.

kagglepythondata-mininglightgbmmicrosoft

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