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The Role of Data Science in Early Stage Companies, with Kevin Novak, Managing Partner & Founder at Rackhouse Venture Capital
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If you want to be successful with data, you need to have a fairly solid grasp of statistics and probability, in addition to the mathematics behind them.
The decision to work as a data scientist in venture capital should be motivated by excitement rather than family history; the fact that your father was a data scientist does not automatically qualify you to undertake the same work.
It is important to have some expertise in identifying data trends that scale and those that don't know when working with a diverse range of founders and firms operating in various sectors.
In this episode of The Gradience Podcast, Nigel is joined by Kevin Novak, Managing Partner & Founder at Rackhouse Venture Capital.
They cover:
👉 [ 00:13:23 ] To be good at data, you need to have like a really good understanding of statistics and probability and the underlying mathematics
👉 [ 00:16:55 ] The choice to become a Data scientist in VC is based on enthusiasm and not hereditary; just because your father was one doesn't make you one
👉 [ 00:18:25 ] When dealing with a wide range of founders and businesses in various industries, it's helpful to have some experience seeing data patterns that scale and those that don't
👉 [ 00:21:20 ] If you have a team and leader that you expect is going need to scale, you need to build organizational structures and hire people whose skill sets enable them not to be blocked
👉 [ 00:24:18 ] Communication skills are an essential part and should be the very first order thing when hiring. Some brilliant people can't communicate. You need helper people to foster this in your organization for it to scale smoothly
👉 [ 00:24:30 ] There're roles for people who are so deep in their expertise within a company that they can't communicate well with people outside of it. However, this can be detrimental at the early stage of a company because there's "a need to be able to pretty quickly get on the same page"
👉 [ 00:25:03 ] When hiring your first data scientist, go for someone who is "chronically impatient with their current situation" and in a career transition
👉 [ 00:26:14 ] Another place to find your first data scientist is at academic data science or operations research conferences. Search for people at the poster sessions "who looked like they didn't want to be there"
👉 [ 00:26:57 ] When hiring, find people with an inherent dissatisfaction and who are inherently experimental. These afford them a high pain tolerance and hence make up the personality characteristics that for great anchor employees
👉 [ 00:29:10 ] Covid and the move to Zoom have broadened the spectrum on the caliber of people you can hire
👉 [ 00:32:10 ] VC, as a face-to-face business, has largely moved digital with little interruption
If you’re interested in getting team-building tips from a successful founder, want a peek into being a founder in an unexplored niche, want to learn how to create an organic culture for your team, interested in knowing what it's like to be a founder in an unfamiliar industry, want to build a cross-functional team and a team with the right atmosphere, tune in to this episode of The Gradience Podcast, a Podcast by Build Talent.
Subscribe here:
👉 YouTube: https://bit.ly/3MCamCW
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👉 Spotify: https://spoti.fi/3HbsAan
👉 RSS: https://bit.ly/38yhi4n
👉 Email: podcast@buildtalent.io
And connect with Kevin here:
👉 LinkedIn: https://www.linkedin.com/in/novakkm/
👉 Twitter: https://twitter.com/novakkm
About the Show
“The Gradience” is a podcast by Build Talent & hosted by Nigel Robinson, wherein each episode, we’ll be speaking with a founder or expert as we discuss the art & science of hiring leaders, why it matters, and how you can keep up.
The Gradience is hand crafted by our friends over at: fame.so
Previous guests include: Riley Ennis of Freenome, Matt Birnbaum of Pear VC, Derek Lo of Medallion, Chandan Lodha of CoinTracker, Jennifer Smith of Scribe
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