Hannah Davis
Brand And Product Marketing Associate
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolHannah Davis is a brand and product marketing associate in New York with 11 years of experience blending data-driven analysis and creative storytelling within the beauty and retail sectors. Currently at Glossier after roles at Supergoop!, she specializes in translating customer insights into compelling product narratives and cross-functional programs that prioritize community. Her background includes hands-on retail marketing and influencer-facing work, giving her an uncommon mix of grassroots content sensibility and corporate strategy. She also contributes to open-source machine learning projects on GitHub, bringing practical full-stack fixes to ml5.js examples and training scripts—evidence of a technical curiosity that informs her approach to product data and experimentation. A practiced communicator and former assistant speech coach, she excels at building internal alignment and clear customer-facing copy. Fluent at adapting in fast-paced environments, she focuses on measurable outcomes to guide future brand decisions.
10 years of coding experience
Bachelor's degree Political Science and Government, Bachelor's degree Political Science and Government at Grinnell College
Spanish, English
Github Skills (14)
javascript10
p5js10
image-classification10
machine-learning9
lstm9
json9
deep-learning8
data-handling8
css8
html8
neural-network8
python8
github7
computer-vision6
Programming languages (2)
Github contributions (5)
ml5js/ml5-examples
Mar 2018 - May 2018
A collection of ml5.js examples
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:34 commits, 5 PRs, 5 comments in 2 months
Contributions summary:Hannah contributed to an example project focused on image classification. Their work included implementing a multiple image classification example, likely involving modifications to the front-end (sketch.js) and data handling (make_json/main.py). They also worked on cleaning up the multiple image example and saving results to a predictions file, indicating a focus on both the user interface and data processing aspects of the project. Further, the user made adjustments to naming conventions.
ml5machine-learningattribution
ml5js/ml5-library
Mar 2018 - Apr 2018
Friendly machine learning for the web! 🤖
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:32 commits, 3 PRs, 22 comments in 1 month
Contributions summary:Hannah's contributions primarily involved adding and modifying the "datasets page" within the application's user interface. The user also fixed a bug within the training script (train.py) relating to model file naming, preventing issues in model saving and retrieval. Additionally, the user updated the training script model name in several commits. These changes suggest a focus on both frontend and backend aspects of the project.
imagenetdeep-learninglstmjavascriptp5xjs
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