#111 - Jake Klamka and Kevin Hale

**Craig Cannon** (0:00)
Hey, how's it going? This is Craig Cannon, and you're listening to Y Combinator's podcast. Today's episode is with Jake Klamka and Kevin Hale. Jake founded Insight. Insight provides intensive, seven-week professional training fellowships in fields such as data science and data engineering. Insight was in the YC 2011 batch. Kevin's a visiting partner at YC. Before YC, Kevin was a co-founder of Wufoo, which was funded by YC in 2006 and acquired by SurveyMonkey in 2011
You can find Jake on Twitter at Jake Klamka and Kevin at I Like Best.
All right, here we go. So Kevin, for those of our listeners that don't know who you are, what's your deal?

**Kevin Hale** (0:41)
I'm a partner here at Y Combinator. I actually was in the second ever bachelors in winter 2006 and I founded a company called Wufoo.
Ran that for five years and then we were acquired by SurveyMonkey and that moved us from Florida to California and that's when PG asked if I'd be interested in helping out at YC. And I've been there pretty much ever since.

**Craig Cannon** (1:02)
Yeah, and you suggested Jake as a guest for this episode. So Jake, what do you do?

**Jake Klamka** (1:06)
So I'm the founder and CEO of Insight. So Insight is an education company. We run fellows programs that help scientists and engineers transition to careers in data science and AI.
And it's a pretty unique model because they're completely free of these fellowships. They're full time. The companies sort of fund the process.
Engineers, scientists build projects for seven weeks. They meet top data teams and they get hired on those teams. We've got over 2,000 Insight alumni working as data scientists now across the US and Canada.

**Craig Cannon** (1:34)
Nice. And you always haven't been working on this. So you applied to YC for the winter 2011 batch.

**Jake Klamka** (1:40)
That's right, yeah.

**Craig Cannon** (1:41)
And what was your idea then?

**Jake Klamka** (1:42)
So I was back in, so I started my career, and this is relevant to why I started Insight, because I basically started, I wish that had existed when I was around. I was a physicist at the University of Toronto. I thought I was gonna be a scientist for the rest of my life.
And then partway through my PhD, I realized I wanna go into technology. And I think to myself, I'm writing code, I'm building machine learning models. This is great, I've got what I need. And it frankly took me a long time to transition. Eventually got into Y Combinator, came down here from the winter 2011 batch. I was building a bunch of time mobile sort of productivity apps that were machine learning enabled.
And didn't quite get the up and to the right graph that you would hope for after YC.
But it was an incredible experience. And in that sort of late 2011, after, I'll just call it six, 12 months after YC, was searching for a new idea. And actually went, spoke with Paul Graham and a few other advisors, and the recommendation was work on a problem you yourself would have. You're kind of building these apps that, you're trying to use these machine learning models and hopefully somebody's got that as a problem, but flip it around. Start with a problem you've had, then figure out what the solution is. And when I reflected on it, it took me a few years to really make this transition. I've been so close all along, but I didn't know product, I wasn't really connected in the valley.
There's a bunch of, technically I had the fundamentals, but a lot of the tool sets were different in the industry. So I didn't know what I didn't know. And when I got down here and I started talking to people, that's when I finally started figuring it out.
And we're seeing a lot of my friends having that same struggle. So brilliant mathematicians, neuroscientists, biologists, also engineers later, we found the same thing, kind of getting stuck. And they're like, I want to go into data science. I want to go into AI. I want to go into these cutting edge fields, but it doesn't say the right thing on my resume, or I'm kind of like, just getting, that last mile is really hard to cross. And I thought, okay, well, this is a problem I want to solve, because these are some of the most brilliant people I'd ever worked with. A lot of them were my former colleagues from physics. And I thought, what does the solution for this look like? And at first, I was focused on, it's going to be an app again, right? It's some machine learning enabled app. And then I realized, now it actually probably looks more like an in-person program where folks are getting together, building cool projects, and then getting started from there.