Feiyang Chen
Data Scientist
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolFeiyang Chen is a data scientist with eight years of experience turning ambiguous product problems into measurable growth at companies from Ford to Qualcomm and currently Meta, where he focuses on Instagram Reels, creator monetization, and feature launches. He blends product analytics, experimentation, and data engineering to influence adoption, retention, and monetization strategies, while building tooling that empowers cross-functional teams. A UC San Diego-trained analyst with a background in accounting and finance, he pairs business acumen with rigorous modeling—examples include ARIMA forecasting at Ford and large-scale experimentation at Meta. Beyond product work, he contributes to open-source ML algorithm collections in R and maintains web-scraping tools for multimodal research, signaling a strong applied research and engineering bent. Always curious and continuously learning, he brings both hands-on implementation skills and strategic insight to data-driven product decisions.
8 years of coding experience
3 years of employment as a software developer
University of California, San DiegoBachelor's degree, Accounting and Finance, Bachelor's degree, Accounting and Finance at University of Nebraska-Lincoln
English, Chinese, Korean
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (21)
algorithm10
algorithms10
python10
classification10
machine-learning10
beautifulsoup10
webscraping10
regular-expression10
regression10
http-request10
multimodal9
data-mining9
clustering9
random-forest8
dimensionality-reduction7
Programming languages (14)
Github contributions (5)
Eurus-Holmes/Awesome-Multimodal-Research
Jul 2019 - Oct 2022
A curated list of Multimodal Related Research.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:196 commits, 5 PRs, 198 pushes in 3 years 3 months
Contributions summary:Feiyang primarily contributed to the development of a web scraper to download multimodal research papers. Their work involved writing Python scripts to interact with web resources, parse HTML content using regular expressions and BeautifulSoup, and download PDF files based on search keywords. Furthermore, the user updated existing scripts to adapt to changes in web structures and utilized libraries like requests and urllib for web interactions. The changes show a focus on automating the retrieval of academic papers related to multimodal research.
multimodal-learningpytorchdeep-learningmultimodalmachine-learning
TheAlgorithms/R
Sep 2018 - Jan 2021
Collection of various algorithms implemented in R.
Role in this project:Data Scientist
Data ScientistContributions:72 commits, 29 PRs, 63 pushes in 2 years 3 months
Contributions summary:Feiyang contributed a significant number of machine learning algorithms implemented in R. They added code for various models, including Linear Regression, Logistic Regression, Decision Trees, SVM, Naive Bayes, KNN, K-means, Random Forest, Dimensionality Reduction, Gradient Boosting algorithms, and included data processing utilizing the xlsx package. Their work demonstrates a focus on applying diverse machine learning techniques within the context of the repository's algorithm collection.
regressionr-programmingdata-miningstatistical-learningmachine-learning
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