Tony Ojeda is a Principal AI Data Engineer with two decades of hands-on experience building practical AI, data science, and analytics solutions across startups and enterprise environments. He has led and scaled data teams as EVP of Data Science & AI and founded District Data Labs to deliver measurable business impact—optimizing inventory, forecasting, and decision systems that saved millions and improved forecast accuracy. Tony blends technical depth in applied NLP and vectorization (contributing to projects like foxbook/atap on web scraping, social media ingestion, and Gensim-based vectorization) with strategic leadership in generative AI, RAG, and prompt engineering. He co-founded a major regional data community, growing it from local meetups into a multi-group organization of over 15,000 members, demonstrating aptitude for community building and education. Based in Wake Forest, NC, his background in finance and an MBA informs a pragmatic, ROI-focused approach to AI adoption and productization. Colleagues rely on him to turn ambiguous data problems into repeatable, production-ready systems that senior stakeholders trust.
12 years of coding experience
16 years of employment as a software developer
MBA Strategy Execution & Valuation; and Entrepreneurship, MBA Strategy Execution & Valuation; and Entrepreneurship at DePaul University
MS Finance, MS Finance at Florida International University
Contributions summary:Tony primarily contributed to data analysis and model building within the repository. Their work involved incorporating a Wikipedia HTML file for data analysis, suggesting the use of data visualization techniques. The user then made commits related to list comprehensions and pandas implementation for data manipulation. Their contributions suggest an interest in understanding and building analytical solutions.
Contributions:7 commits, 7 pushes, 7 comments in 1 year
Contributions summary:Tony made several updates to code within the `foxbook/atap` repository, which focuses on applied text analysis. The commits primarily involve modifications to code snippets related to web scraping (news and articles), social media data fetching from Twitter, and parsing RSS feeds. Additionally, the user updated vectorization methods using the Gensim library. Overall, the contributions center on preparing and processing textual data.
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