Neal Humphrey
Staff Software Engineer at Fandom
Washington, District of Columbia, United States
Summary
🤩
Rockstar🎓
Top SchoolNeal Humphrey is a Staff Software Engineer with 11 years of experience building data-driven systems that move the needle on ad revenue and personalization for large publishers. He has deep back-end expertise in Python, Node, AWS (EMR, Redshift, ECS, CDK) and production ML pipelines, having led ad-targeting, ETL and topic-tagging efforts at The Washington Post and now rebuilding Fandom’s ad engine. Neal blends hands-on engineering with team leadership—mentoring engineers, running architecture reviews, and teaching infrastructure-as-code—while also contributing to open-source ML visualization work like the Yellowbrick ConfusionMatrix. He pairs a systems-first approach to scaling millions-of-user services with practical product instincts from his startup and civic-tech project experience, making him equally comfortable refactoring legacy platforms or shipping new analytics features.
11 years of coding experience
14 years of employment as a software developer
University of WollongongBachelor of Science (BS) Mechanical Engineering, Bachelor of Science (BS) Mechanical Engineering at North Carolina State UniversityPontificia Universidad Católica de ValparaísoJohn T. Hoggard High SchoolKick
English, Spanish
Stackoverflow
3,322reputation
366kreached
16answers
1question
orm
top-1%
sqlalchemy
top-1%
python
top-5%
Github Skills (21)
visualization10
python10
matplotlib10
scikit10
psycopg10
machine-learning10
orm10
sqlalchemy10
scikit-learn10
visualizations10
multiple-selection9
feature-selection9
variable-selection9
amazon-ec26
css6
Programming languages (9)
Github contributions (5)
DistrictDataLabs/yellowbrick
Mar 2017 - Mar 2018
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 10 PRs, 7 pushes in 1 year
Contributions summary:Neal primarily contributed to the development of the ConfusionMatrix visualizer. Their work involved implementing the heatmap visualization with percentage or raw count representation, zooming capabilities, and dynamic text color adjustments. The user also contributed to the user testing example demonstrating usability issues of the visualization and provided a partial bug fix for the classification report. This involved significant work in visualization and machine learning model evaluation.
pythonvisual-analysisvisualizermodel-selectionmachine-learning
focusconsulting/housing-insights
Sep 2016 - Nov 2019
Bringing open data to affordable housing decision makers in Washington DC. A D3/Javascript based website to visualize data related to affordable housing in Washington DC. Data processing with Python.
Contributions:685 commits, 373 PRs, 453 pushes in 3 years 2 months
washington-dcaffordablepythonmakersdecision
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.