Dana Benson
Software Development Engineer at Amazon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolDana Benson is a software development engineer with six years at Amazon building Alexa-related systems and a decade-plus background delivering production web and mobile services for companies like Starbucks and Avant. She combines backend API design, cloud-native ML tooling, and MLOps expertise—contributing to prominent AWS open-source projects such as the SageMaker examples and Python SDK where she improved experiment analytics, hyperparameter tuning integration, and lineage support. Dana has led greenfield projects, international rollouts (Starbucks Mobile Order & Pay), and cross-site teams while maintaining a strong focus on clean documentation and reproducible examples. Based in Seattle, she brings a pragmatic engineering style that blends TDD, CI practices, and data-focused tooling to help bridge research workflows and production systems.
6 years of coding experience
6 years of employment as a software developer
Computer Science, Computer Science, Computer Science, Computer Science at Pacific University
Stackoverflow
964reputation
481kreached
23answers
5questions
Github Skills (20)
python10
pandas10
machine-learning10
sagemaker10
amazon-sagemaker10
mlops10
aws10
jupyter-notebook10
pytorch9
tensorflow9
workflow-management9
experiment9
deep-learning8
data-science7
wadl6
Programming languages (2)
Github contributions (5)
aws/sagemaker-python-sdk
Sep 2020 - Jan 2023
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:131 reviews, 18 commits, 25 PRs in 2 years 4 months
Contributions summary:Dana primarily contributed to the development and maintenance of the SageMaker Python SDK, focusing on the experimentation and analytics features. They implemented functionality to reshape dataframes for experiment analytics and added features such as listing artifacts by type. Moreover, the user addressed style feedback and added new support for SageMaker lineage, workflow, and pipelines. Their work demonstrates expertise in extending the SDK's capabilities for machine learning workflows on AWS SageMaker.
amazon-sagemakermachine-learning-modelsawsmxnettensorflow
aws/amazon-sagemaker-examples
Apr 2020 - Apr 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:MLOps Engineer
MLOps EngineerContributions:26 reviews, 8 commits, 16 PRs in 2 years
Contributions summary:Dana primarily contributed to the example Jupyter notebooks within the repository, focusing on integrating hyperparameter tuning jobs with experiment management. They updated an existing notebook to reflect the latest SDK version, demonstrating a focus on maintaining and enhancing example code. Their work involved modifying the 'associate-hyperparameter-tuning-job' notebook to simplify and improve functionality, updating dependencies, and improving logging within the notebooks. Furthermore, they fixed inconsistencies in the documentation, and added and updated the documentation.
amazon-sagemakerjupyter-notebookmachine-learning-modelssagemakeraws
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.