Lenny Rachitsky: belief

Transcript context

…Our interview process has in the early stages a coding exercise. So we do our technical screen and a shortened version of a business case. So real world problem solving. Typically, it's something actually from DoorDash history, like a real problem that we had to see how people can problem solve on the fly. I think that that's an important skill to be able to have, which is, how do you take a problem, break it down, talk through it. A little bit like some of those consulting cases that you hear about, but something that's really rooted in real problems. And I think you can learn a lot from those types of cases where, yes, you get to see how people handle ambiguity and structured problem solving, but ultimately most people get something wrong. They make an assumption that's wrong because well, I would hope that the interviewer knows the business better than the interviewee. And seeing how people react to being told they're wrong is a really important signal in my opinion. Seeing how people respond, how they're able to take new information and pivot, how they're able to make a decision. So that's another thing that I like to see in cases where, hey, you may not know the real right decision. You might say, "Hey, I could see it going one way, I could see it going the other way." But I always push people to say, "If you had to make a call right now, what would it be?" So are people able to have a point of view without full information because that's life. Sometimes you have to just pick a direction and make a decision even though you don't have perfect information. So I like to see some of these softer skills and how they manifest throughout a case interview, even if it's not specifically what I'm asking with the literal problem we're solving in the case. Along these lines, but in a different direction. You don't actually have a deep data science data background before you got into this stuff. I know you had some art background, you had an art portfolio back in school, and I think a lot of people wouldn't imagine that for someone being head of analytics for a company like DoorDash. I don't exactly know the question, but I guess is there anything there that you think would be interesting for people to know or hear? Yeah, it's funny. I joke that I have a job I'd never be hired for because I don't have a traditional data science background. And I know that Elizabeth Stone on her podcast with you talked a lot about her non-traditional background for a CTO. So hey, maybe there's something to it. But I became a data scientist out of necessity. I completely self-taught in terms of SQL and Python and I did it because there was a need at DoorDash for someone to help figure out what the right goals were, how we set those goals, how we were performing different markets early in the DoorDash story, so 10 years ago at this point. And I think I just gravitated towards that type of work and Tony recognized that superpower in me even though I don't have that formal training. So yeah, I'm a bit of an artist for fun, but I guess a data scientist in practice or for career. But I think that that non-traditional background has been a great thing because I'm able to hire people who have the technical skills that I don't have, the folks with PhDs in statistics and the data scientists, machine learning and otherwise. I am able to hire those folks and yet keep them really focused on driving business impact because my background was on the finance side, and so I've always been a pragmatist. And for me, the purpose of our team is to drive business impact. And so the mix between the technical skills of the smarter people that I've hired, the smarter than myself, and my grounding in driving business impact has been a really great partnership.…

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