Daniel Gomez
Marketing Strategy Specialist
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolDaniel Gomez is a Marketing Strategy Specialist with 11 years of experience blending content creation, campaign execution, and video production oversight to drive measurable engagement for B2B technology brands. He excels at end-to-end campaign coordination—creating targeted email nurture flows, managing external video production, and analyzing performance to optimize outreach. A 2023 ASU graduate in Digital Marketing with a fashion minor, Daniel pairs creative sensibility (including upcycling sewing projects) with data-informed marketing tactics. His cross-cultural leadership in student organizations and hands-on roles from Amazon operations to agency work give him a pragmatic, stakeholder-focused approach. Uncommonly for a marketer, he also contributes to open-source medical imaging projects on GitHub, bringing analytical and technical problem-solving skills to visualization and image-processing improvements. Based in Los Angeles, he’s passionate about crafting marketing that resonates across diverse audiences and mediums.
10 years of coding experience
3 years of employment as a software developer
Minor in Fashion, Minor in Fashion at Arizona State UniversityDiploma, Diploma at Perry High School
Stackoverflow
11,608reputation
1.5mreached
106answers
72questions
unix
top-5%
syntax-highlighting
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bash
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anaconda
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python
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linux
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Github Skills (33)
visualization10
python10
image-processing10
scikit10
machine-learning10
numpy10
neuroimaging10
scikit-learn10
medical-imaging10
visualizations10
syntax-highlighting10
matplotlib9
unix9
linux9
bash9
Programming languages (18)
Github contributions (5)
ANTsX/ANTsPy
Jul 2019 - Feb 2022
A fast medical imaging analysis library in Python with algorithms for registration, segmentation, and more.
Role in this project:
Data Scientist
Data ScientistContributions:14 commits, 8 PRs, 10 comments in 2 years 7 months
Contributions summary:Daniel primarily contributed to the `antsx/antspy` library by fixing bugs and implementing enhancements related to image visualization and processing. Their work included fixing type errors, improving the `plot_grid` function by adding support for colorbars, and fixing overlay visualization issues. The user's contributions focused on improving image analysis functionalities within the context of medical imaging.
python-bindingspythonnormalizationc-plus-plus
nilearn/nilearn
Aug 2018 - Jun 2022
Machine learning for NeuroImaging in Python
Role in this project:Data Scientist
Data ScientistContributions:2 reviews, 8 commits, 8 PRs in 3 years 11 months
Contributions summary:Daniel's contributions primarily focused on enhancing the functionality and usability of the Nilearn library, which is dedicated to machine learning for neuroimaging. They added documentation and examples for plotting color maps and binarizing images. Furthermore, they modified the `threshold_img` function, including adding a `copy` parameter and introducing the `binarize_img` function, while adding tests to verify these code changes. They also added a new feature to NiftiLabelsMasker to extract region signals using different functions such as mean, median, etc., and improved the existing examples.
neuroimagingpythondecodingbrain-connectivitymachine-learning
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