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Data Driven VC
Community and resources for data-driven venture capital practitioners. Newsletters, events, and
insights on applying data science to VC.
VC Stack
Large directory of tools for venture capital and angel investors.
Newsletters
Stay updated with insights on data-driven VC, infrastructure building, and technology in venture capital.Data Driven VC
Weekly newsletter at the intersection of venture capital and data. 35,000+ subscribers from
firms like a16z, Accel, Index, and Sequoia. By Andre Retterath (Earlybird VC).
Inflection - svrgn
On engineering moonshots and deep technology. Occasional posts by me about Inflection’s tooling
and infrastructure.
AVC
Fred Wilson’s perspectives on venture capital and technology. Occasional posts about tech’s role
in the VC industry.
Level Ventures
Data-driven perspectives on venture capital from an LP point of view.
Books
Essential reading for understanding venture capital from both operational and technical perspectives.| Title | Author(s) | Why Read It |
|---|---|---|
| The Business of Venture Capital | Mahendra Ramsinghani | Comprehensive overview of fund structures, legal frameworks, and the LP-GP relationship |
| Venture Deals | Brad Feld & Jason Mendelson | Essential reading on how VC deals work: term sheets, negotiations, and deal mechanics |
| Secrets of Sand Hill Road | Scott Kupor | Inside look at how VC firms operate from a16z’s COO |
| The First 90 Days | Michael D. Watkins | Critical strategies for successfully transitioning into a new role, especially valuable when joining a VC fund |
Research Papers
Academic research on applying machine learning and data science to venture capital.Machine Learning for VC
Machine Learning for VC
Investment Sourcing & Prediction
- Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems — Transformer-based time series classification for predicting successful VC and Growth Capital investments by analyzing financial patterns. From EQT’s Motherbrain team.
- CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification — Knowledge graph with 1.17M companies and 51M weighted edges representing business relationships. First large-scale dataset from a real investment platform for measuring company similarity. Featured in this book’s Knowledge Graphs chapter.
- Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation — Efficient fine-tuning method (PTEC) for multi-label company classification using LLMs. Addresses limitations of text-to-text approaches in generating valid labels with confidence scores.
- A Scalable and Adaptive System to Infer the Industry Sectors of Companies — Production system for automatically classifying companies into industry sectors using prompt + model tuning of generative language models. Deployed for PE professionals at scale.
- Using Deep Learning to Find the Next Unicorn: A Practical Synthesis — Comprehensive literature review synthesizing deep learning methodologies for startup evaluation across the entire ML lifecycle. First comprehensive synthesis of DL approaches for VC investment decisions.
More papers coming soon. If you have suggestions, please open an
issue.
Open Source Projects
Tools and projects from the VC tech community.attio-mcp
MCP server for Attio CRM integration
ddvc-hackathon
Projects from the Data Driven VC hackathon
Conferences & Events
DDVC Summit 2026
Virtual summit for data-driven VC practitioners
VC Day by Vestberry
Conference focused on VC technology
Hiring
Building a VC tech team? Looking for your next role?If you’re hiring in this space, reach out to Alex. Happy
to add opportunities here.