Greg Kogan
Marketing & Growth Consultant at Redpoint
Greater Philadelphia United States
Summary
🤩
Rockstar🎓
Top SchoolGreg Kogan is a Marketing & Growth consultant with 11 years helping developer-focused companies create product-led growth and memorable positioning. He joined Pinecone as employee #6 and helped turn vector databases into a recognized AI category, contributing to its rapid rise to unicorn status while building a PLG engine. Greg advises a broad portfolio of startups and growth-stage firms (about 60 clients) and now serves as Expert in Residence at Redpoint, focusing on winning developer adoption. His background blends technical fluency—evidenced by hands-on ML/data work like contributing Faiss/HNSW example notebooks for Pinecone—with operator experience in both startups and acquisitions. Earlier in his career he applied engineering rigor as a naval architect, a detail that informs his methodical approach to product and go-to-market strategy. He’s based in Greater Philadelphia and is known for distilling complexity into simple ideas that resonate with technical audiences.
11 years of coding experience
4 years of employment as a software developer
BE Naval Architecture, BE Naval Architecture at State University of New York Maritime College
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
hnswlib10
jupyter-notebook10
vector-database10
faiss10
python10
semantic-search10
ai9
numpy8
llm7
pandas7
Programming languages (5)
Github contributions (5)
pinecone-io/examples
Aug 2021 - Jun 2022
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:1 review, 17 commits, 8 PRs in 10 months
Contributions summary:Greg contributed example notebooks for implementing and experimenting with various vector search techniques within the Pinecone ecosystem. This primarily involved using the Faiss library to build and search HNSW indexes for efficient similarity searches. The user demonstrated skills in handling vector data, utilizing LSH for approximate nearest neighbor search, and product quantization to optimize search performance.
aijupyter-notebookllmpythonsemantic-search
gkogan/sup-save-url-parameters
Jun 2017 - Aug 2017
Sup.js saves URL parameters and inputs them into any form submitted during a visitors session on you site.
Contributions:12 commits, 14 pushes, 1 branch in 1 month
attributionmarketingmarketing-automationmarketing-toolsmarketing-web-ops
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