Uber’s first data science chief just raised a new venture capital fund to support emerging AI startups – TechCrunch

Kevin Novak joined Uber as the 21st employee and seventh engineer in 2011, and in 2014 he was the company’s head of data science. He speaks proudly about that time, but like all good things, she took her course and at the end of 2017, having accomplished what he wanted in the business, he left.

At first, he picked up the pace of his angel investing, work he had already begun to focus on on weekends and evenings, eventually building a portfolio of over 50 startups (including fintech Pipe and the firm of stand-alone checkout Standard cognition).

He also started advising startups and venture capital firms – including Playground Global, Costanoa Ventures, Renegade Partners and Data Collective – and after falling in love with the job, Novak decided this year to start his own private equity firm – risk in Menlo Park, Calif., called Rackhouse Venture Capital. Indeed, Rackhouse has just closed its first fund with 15 million dollars, financed by the first head of engineering of Uber, Curtis Chambers; Steve Gilula, former president of Searchlight Pictures, and Cendana Capital fund of funds. Many of the VCs that Novak knows are also investors in the fund.

We met with Novak at the end of last week to discuss this new vehicle. We also talked about this tenure at Uber, where, be warned, he played a major role in creating surge pricing (although he prefers the term “dynamic pricing”) listen this more comprehensive discussion or check out excerpts from it, slightly edited for length and clarity, below.

TC: You planned to become a nuclear physicist. How did you end up at Uber?

KN: As an undergrad student I was studying physics, math, and computer science, and when I got to college I really wanted to teach. But I also really enjoyed programming and applying physics concepts in the programming space, and the nuclear department had the largest allocation of supercomputer time, which ended up driving a lot of my research – just the opportunity to play on computers while doing physics. Therefore [I] studying to become a nuclear physicist was funded very indirectly by the research that eventually became the Higgs boson. As the Higgs was discovered it was very good for mankind and absolutely horrible for my research budget. . .

A friend of mine heard what I was doing and sort of knew my skills and said, “Hey you should come check out this Uber taxi company which is like a limo company with an app. There is a very interesting data problem and a very interesting mathematical problem. So I ended up applying [though I committed] the cardinal sin of startup apps and wore a suit and tie to my interview.

TC: You are from Michigan. I grew up in the Midwest too, so I understand why you might think people would wear a costume for an interview.

KN: I got off the elevator and the friend who had encouraged me to apply said, “What are you wearing ?! But I was nonetheless asked to join as an engineer in computer algorithms – a title predating the data science trend – and I spent the next two years living in the world of engineering and software. products, to create data functionality and files. . . things like our ETA engine, which basically predicts how long it would take an Uber to reach you. One of my very first projects was working on tolls and tunnels, because figuring out which tunnel an Uber went through and how to calculate time and distance was a common point of failure. So I spent, like, three days driving the Big Dig in Boston to Somerville and back to Logan with a bunch of phones, collecting GPS data.

I learned a lot of very random facts about Uber cities, but my big claim to fame was the dynamic pricing. . . and that turned out to be a really successful cornerstone for the strategy of making sure Ubers were available.

TC: How is it, when you tell people that you invented surge pricing?

KN: It’s a very quick litmus test for determining people’s underlying enthusiasm for behavioral economics and finance. The Wall Street crowd says, “Oh my god, that’s so cool.” And then a lot of people are like, ‘Oh, thank you, yeah, thanks a lot, wonderful, you buy the next set of drinks. . . [Laughs.]

But the data also became the incubation space for a lot of the early special projects like the Uber pool and a lot of ideas around it, okay how would you create a distribution model that allows different people to make requests. grouped carpooling? How to group them efficiently in space and time so that we can get the right match rate that [so this] is the project profitable? We’ve done a lot of work on the theory behind Uber Eats star delivery models and thought about how we apply our learnings about carpooling to food. So I got the first person perspective on a lot of these products when it was literally three people scribbling on a notepad or riffing on a laptop over lunch, [and which] eventually became these great companies nationwide.

TC: You worked on Uber Freight for the last nine months of your Uber career. this company with Anthony Levandowski exploded.

KN: Yeah, it was a very interesting time for me because over six years later, [I was already developing the] attitude of “I did everything I wanted to do”. I joined a company of 20 people and at the time we were approaching 20,000 people. . .and I missed the dynamics of the small team a bit and I felt like I was reaching a natural stopping point. And then Uber’s 2017 came along and and there was Anthony, there was Susan Fowler, and Travis has this horrible accident in his personal life and his head clearly wasn’t in the game. But I didn’t want to be the guy who was known to bail out the worst quarter in company history, so I ended up spending the game. ‘The following year basically keeping the band together and trying to figure out what I could do to keep this tiny part of the business I was running intact and motivated, empathetic and good in every way.

TC: You left at the end of that year and it looks like you’ve been very busy since, including, now, the launch of this new fund with the support of outsiders. Why call it Rackhouse? You used the Jigsaw Venture Capital brand when you invested your own money.

KN: Yeah. One year [into angel investing], I had formed an LLC, I “branded” my portfolio in the market, sending quarterly updates to myself, my accountant and my wife. It was one of those exercises that was a transfer from how I trained managers, in that I think you grow more effectively and successfully if you can develop a few skills at a time. So I was trying to figure out what it would take to run my own back office, even if it was only a matter of transferring my money from my checking account to my “investment account” and writing my own portfolio update.

I was really excited about the possibility of starting my first external fund with other people’s money under the Jigsaw banner, but there is actually a fund in UK. [named Jigsaw] and as I started talking to LPs and said, ‘Look, I want to build this data fund and I want this to be an early stage’, I was getting calls from them saying, ‘We just saw that Jigsaw has does this D-series in Crowdstrike. ‘ I realized that I would be competing with the other Jigsaw from a mind-sharing perspective, so I figured that before things got too big and crazy, I would create my own separate brand.

TC: Did you integrate any of your angel-backed transactions into the new fund? I see Rackhouse has 13 portfolio companies.

KN: There are a few that I have agreed to advance and store for the fund, and we’re just going over the technical details of doing that right now.

TC: And the focus is on machine learning and AI.

KN: That’s right, and I think there are some amazing opportunities outside of traditional industry focus areas which, to the extent that you can find rigorous AI applications, will also be much less. competitive. [Deals] who do not fall in the strike zone of almost as much [venture] business is the game I want to play. I have the impression that this opportunity – whatever the sector, whatever the geography – favors experts in the field.

TC: I wonder if that also explains the size of your fund – your willingness to stay out of the strike zone of most VCs.

KN: I want to make sure that I build a fund that allows me to be an active player in the early stages of businesses.

Matt Ocko and Zack Bogue [of Data Collective] are good friends of mine – they’re mentors, actually, and little LPs in the fund and they told me about how they started. But now they have over a billion [dollars] in assets under management, and the people I [like to back] are two people who moonlight and are preparing to take the plunge and [firms the size of Data Collective] basically excluded themselves from the training and the pre-seeding stage, and I like that stage. This is something where I have a lot of useful experience. I also think this is the stage where, if you come from an area of ​​expertise, you don’t need the five quarters of finance to be convinced.

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