Super Data Science: ML & AI Podcast with Jon Krohn 601: Venture Capital for Data Science
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Aug 16, 2022 
Sarah Catanzaro, General Partner at Amplify Partners, shares insights on venture capital for data science. Topics covered include early-stage investment selection, accelerating data science ideas to funding, and observational causal inference. Learn about the importance of deeply technical founders and experimentation in product ROI.
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What Early-Stage Venture Capital Means
- Venture capital buys equity in startups that aim to scale to large businesses and possible IPOs.
- Early-stage VC focuses on companies still iterating on product-market fit rather than proven growth metrics.
Pitch A Clear Vision And MVP Roadmap
- Seed-stage founders need a clear long-term vision and a crisp MVP plan to convince early investors.
- Prepare hypotheses for subsequent product steps (A, B, Z) to show pathway from MVP to scale.
Why VCs Need Big Outcomes And Follow-On Funds
- Funds have lifecycle limits and need big outcomes to return LP capital over ~10 years.
- Opportunity funds let firms maintain ownership without over-concentrating flagship funds during follow-ons.