Building a world-class data org | Jessica Lachs (VP of Analytics and Data Science at DoorDash)

26:52Our 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, it's 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 care 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.

27:44But ultimately, most people get something kind of wrong. They make an assumption that's wrong. Because well, I would hope that the interviewer knows the business better than the interviewer. 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 kind of pivot, how they're able to make a decision. So that's another thing that I like to see in cases where you may not know the right, the real right decision. You might say, hey, I could see, 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?

28:28So 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, 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. Kind of along these lines, but sort of in a different direction. You don't actually have a deep data science, data background before you got into this stuff. You, I know you had some kind of art background, you had like art portfolio back in school.

29:09And I think a lot of people wouldn't imagine that for someone being head of analytics or a company like George Dash. I don't exactly know the question, but I guess is there anything there that you think would be interesting for people to know right here? Yeah, it's funny. I sort of 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 sort of non -traditional background for a CTO. So hey, maybe there's something to it, but I became a data scientist out of necessity.

29:44I 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 kind of early in the DoorDash story, so 10 years ago at this point. And I just had a, 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 and statistics and the data scientists, machine learning and otherwise.

30:49I'm 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 kind of grounding in driving business impact has been a really great, great partnership. That's a quite an inspiring story for someone that is just starting out and doesn't necessarily have a lot of experience in data, but also just generally. Like I think this is a really cool example.

31:32You could be successful and I feel that you don't have a ton of background in. I'm curious what you think it was in you that allowed you to succeed in this and get to where you're today. What do you think you did right or what is some habits or ways of thinking that you think helped you achieve that? First off, I have imposter syndrome like everybody else. So it's not like I have this crazy sense of confidence of like I can do anything. I definitely have the same doubts and that others have. I think part of it was probably not even realizing what I was doing. When you're at a startup and things are moving quickly and you see a problem and I've always liked solving problems.

32:16So I was like, all right, how do I solve this problem? I was like, oh, well, I need access to the data. I don't have access to the data. All right, I'll ask an engineer to get me the data. Well, this isn't gonna scale. I can't always bother an engineer. So how do I figure out how to get the data myself? Well, let's learn Python. So I think it kind of came happened organically and I don't think I realized at the time what I was even doing. And then I think if you think about things from first principles about what you need right now in front of you to unblock yourself or solve a problem and you just focus on that instead of thinking about a global org that you're trying to build.

32:58And I think that that helps. So for me, it was always about solving the problem in front of me the best way I could. And if that meant I needed to hire an engineer to report into me through the finance org, then that was what we were gonna do. And nobody was gonna tell me I couldn't do it. So I think it's a belief in yourself and ultimately it's just my desire to solve problems and figure out what has to get done is I think ultimately how it came about. I love that so much. There's so many elements there that I think a lot of people can learn from. I feel like there's also this underlying current of you're just motivated for this to work.

33:38Like you would need, you want to door dash to succeed and just like I will do what I need to do to make this happen. Like I need to solve these problems. I'm not gonna overthink do I have the skills necessarily to do these things? Yeah, I think I'm competitive. I think that's a trait that you find in a lot of early door dash folks and current door dash folks, to be honest, just being really wanting to win and being willing to do whatever you need to to win. So roll up your sleeves, do something that's not your job. I think back to early days of taking out the garbage on Saturday nights because it needed to get done.

34:13I think that that was something that is ingrained in our culture from Tony Shoe from our founder and CEO. And I think that really resonated with me and I feel like I've always sort of operated that way as well. And I think that that helped to help me in my career to be able to do what I've done without really thinking about it too much. Are there any other memories or stories of the early days of door dash that would be fun to share something that sticks with you like, wow, I can't believe that's what it was like. I can feel. Oh man, there's so many, including so many mistakes that we've made, but I think something that really stands out to me is before I moved to the analytics area, I was actually a GM, I was the first GM at DoorDash and I was in Boston in 2014, launching the city of Boston when nobody knew who we were.

35:14And we would wake up early in the morning, five AM and we would go out in the, it was the winter of 2014. We'd go out and we'd hand out promo codes to consumers outside of the tea in Boston. And these promo cards would be attached to kind bars so people would take them. And the whole team, it was a small team, there were four of us, but the whole team would go out in the morning to do this. And I think back to our sales guy, shout out to Joey G, so Joe Grasio is our sales guy in Boston. And he was gold on signing merchants on the platform. That was how it was gold, his compensation was tied to that.

35:58And yet in the morning when we would go out, he was with us handing out promo codes because he was part of the team because he wanted to win. You know, we wanted to grow the business. And I think that that is just a great example of kind of the culture that Tony and the early employees and you know, Stanley and Andy, other co -founders, really instilled in all of us early in those days. So I think that that ownership, that extreme ownership of the outcome is definitely one of the things. I think the other is just being very customer first. And I say customer, I mean, consumers, dashers and merchants as all being our customers.

36:42And the first time I ever went to the office, headquarters in Palo Alto, which at the time was in an animal hospital. The first time I went there, there was a huge site outage. And the whole company, it's like 20 people at the time, you know, the whole company jumped online to do customer support to answer the phones, to make sure that folks were getting refunds for orders that weren't going through, make sure the orders that were out there were getting delivered, just dropped everything and hopped on to do support. And I was brand new, didn't really know how to use the tools. And so was like, how can I be useful?

37:21And so back in those days, we used to order dinner to the office using DoorDash. And so in order to preserve about three dashers who would have had to deliver a food to us, I was like, I'm going to go out, go out dashing, go get everyone pizza so that we could kind of feed the masses, doing credits and refunds and you know, do what we had to to make sure that we were serving our customers well. And I think that that night was one of the largest refunds, like as a percent of our bank account that we that we had ever given out. And I think Tony's talked about some of there were sort of two examples that he's talked about where we just gave a lot of money back to customers because it was the right thing to do because, you know, our service failed and we wanted to do right by them.

38:06So I think that those those are sort of two stories that stick out in my mind and really highlight culturally what makes DoorDash unique and what I think has been a really important part of our success. Reminds me of the story that Tony and all the early employees in Imagine you did this just like word dashers, like it's like a right rotation where you dash for a while, right? Is that part of the culture? Yeah, so we have a program, a wee dash program. And Keith Yandell, who's our chief business officer, did your podcast last year and he talked about this. But four times a year, all the employees go out and go dashing or do customer support.

38:48And it's part of our culture that I love. I actually go pair dashing. So I go together with with one of my one of my colleagues. We've done it for years now. And it's sort of a fun, fun thing that we do together four times a year. Actually, usually more than that. But and it's it's important because you get to use the product. You get to you build empathy with all the audiences. I mean, I think all of us order DoorDash a lot. So we built empathy with the with consumers, but being able to go and understand what it's like to go out dashing. And when you're in the restaurant, I was going and talking with merchants and seeing the experience from their point of view, I think it's just incredibly important.

39:31And and of course, we find a lot of bugs. Like, this doesn't work the way it should. Let me report this. So I think it's also just great for for catching catching bugs in the product. This episode is brought to you by Atio, a radically new type of CRM. There's a world where your CRM is powerful, easily configured and deeply intuitive. Atio makes that a reality. Atio is built specifically for the next era of companies. It syncs with your data sources, easily configures to their unique structures, and works for any go -to -market motion from self -serve to sales led. Atio automatically enriches your contacts, syncs your email encounter, gives you powerful reports, and lets you quickly build zap your style automations.

40:17The next era of companies deserves more than an inflexible one -size -fits -all CRM. Join modal, replicate 11 labs and more, and scale your startup to the next level. Head to atio .com slash Lenny. And you'll get 15 % off your first year. That's atio .com slash Lenny. When I come back to the thread that something you mentioned where you and a lot of the early team had a felt extreme ownership over the company. And that's why a lot of this stuff happened for people that like every founder, every product team that are going to like, yes, we need that. Let's make sure everyone's team feels extreme ownership.

40:55Is there anything that you think that the early team did to create that? Or is it hiring? Just pick people that will have that feeling already? Or is it something or is it cultural? I think it's both. I mean, it's definitely cultural. I think it comes from the top. And I think that Tony exhibits the six -stream ownership and, and looks for it in others. So I think that it that helps. But I think even today, I expect of my team that same kind of extreme ownership over the outcomes. And so I'm more interested in our team figuring out how to solve a problem. Then sort of the box that someone fits in like, I am a data scientist.

41:36And so I only do these things. Right. It's like, no, yes. I mean, yes, you are a data scientist, but your goal is to figure out what's happening. And if that means that you're going to pick up the phone and call customers, then that is what you're going to do. And I think that expecting that and setting that as the norm for the team, this sort of ownership of the outcome is something that we continue to do at Dordash and instill in everyone, whether you were early or just joined last month. Is there an example that it comes to mind if someone practice an extreme ownership like a data scientist calling someone or something like those lines?

42:16Yeah. So I actually had a meeting yesterday morning with the team that's working on some of our affordability initiatives. And we had shipped something that we expected to work. And it didn't. And you know, instead of you can dig into the data to understand the segments of consumers that you would expect it to work with in those that it wouldn't, of course, we did that. But ultimately, it was like, I don't know why. And that's where qualitative research is superior to quantitative research, to asking for the context, actually talking to people to figure out what was the motivation, what worked, what didn't for them.

42:53And so the team data scientists included just sat and made phone calls. And so they they were talking about what they found in from those phone calls. And that's going to inform kind of future decisions. And I think rather than saying, well, that's what the qualitative research team is supposed to do. It was like, no, no, that is what our team, anyone's team is supposed to do because that's what's needed to unblock us from this next test that we want to run because we need to know what we, what we're testing. So I think that that that it happens, it happens every day. I think I really love when I see team members go outside the sort of traditional bounds of what a data science role might be and, you know, do some product management work, right?

43:39Do some engineering work. I think that that's that's part of what keeps the job interesting. I think it's part of what makes our team special is that that is not only, you know, allowed, it's encouraged, which is and probably also a reason why we've had folks who've gone from my team to the product org and to the ops org and to the finance org is because they get to do and experience parts of that job and get a good sense for what that's like and then realize it's something that they love. So I think it's definitely something we encourage at DoorDash. I love that. I want to move in a slightly different direction.

44:21When your colleagues told me that you're incredibly good at defining metrics, which is so important to get right for a business, especially when it's complex at DoorDash. And I hear you're especially good at finding the right metric to drive the right incentive, especially when the business is really messy and things like that. So I'm just curious what you've learned about how to pick good metrics and align incentives. Well, I've learned a lot of things about metrics, mostly from bad metrics. I actually think you learn a lot from picking the wrong metric. Ultimately, you want to find a short term metric.

44:55You can measure that drives a long term output. So people always talk about, oh, we want to drive an improvement in retention. Retention is a terrible thing to go on because it's like, it's almost impossible to drive in a meaningful way in the short term. And yet you want to be able to experiment and iterate quickly. So what are the, what are the things that drive retention? What are the inputs? So I think it's really important to find the right inputs. And then through experimentation, test, weather, and not those short term inputs are driving the long term output that you're looking for. I think that's one thing.

45:32I think keeping things simple is another thing I've learned over the years. Maybe it's data scientists, but they tend to love these composite metrics. Like, you know, with a coefficient, we're going to wait this input, you know, at X, and this input at X2. And and then you end up with like a metric that nobody really understands that like it doesn't actually mean anything. And you're like, I don't know if a 0 .1 increase is a lot, is a good, is bad. So they're just hard to work with. And so I always encourage folks just pick something simple. Even if it's not perfect and your composite would be more perfect.

46:14If people understand it, if they have an intuition around it, if it's something that people can talk about across the company, it's going to be a much better metric in terms of driving real outcomes, then you're made up composite score that nobody understands. So I think keeping things simple is also really important. And then I think the last thing I'll say is it's important to understand how metrics across the company equate to one another. And so we spend a lot of time quantifying things in terms of a common currency. So for example, if I were to lower price by a dollar, what would I get in terms of, we'll say volume?

47:00Well, what if I lower delivery times by a minute? What do I get for that in terms of volume? And so now you can make trade -offs between maybe your marketing team and your logistics team because you have this common currency that everyone can talk talk about. And so we've done that. We've tried to quantify all of the levers of our business, price, selection, quality, income in terms so that if we have say a dollar to spend, we know what we get depending on where we put it over what time frame. And I think that that helps us make decisions more quickly because we sort of know what our options are.

47:44We know that we have our inventory of things that we can do, short -term, long -term, and what we get for it. So it definitely helps us to make decisions more quickly and hopefully better decisions. These are so awesome. I definitely want to follow up on some of this. This is so good. So maybe on this last one, which we did at Airbnb also, just like, how does everything translate into Nightsbook and booking? Like every decision we make, what is the actual Nightsbook impact? And so imagine your case, you don't, I don't know if you want to talk about these things, imagine it's like transactions or purchases or GMV or something like that.

48:20It's some guessing. It's the final metric. I don't know. Is that something you talk about or we don't talk about that? So I mean, we measure things in terms of GOV, so gross order value and also volume. So we've got it. Okay. So basically every other metric that people are gold on as much as you can can translate. There's a model that translate that into gross order value and volume. Awesome. So when a team is saying like, hey, we're going to change the onboarding flow and impact conversion here. I don't know. I guess what's, yeah, we're some examples of other metrics on teams that potentially translate into GOV and volume just to make it even more real.

49:01Yeah. So everything from the example that you started with with it, which is like an improvement in the login flow, right? How many more consumers are getting onto the app and ultimately placing orders? And so you can translate that to, of course, orders and GOV. But then something as interesting as selling a tie restaurant in Sacramento, right? We're able to say, what do we think that that gets us in terms of GOV from the consumer by selling that tie restaurant? So it's every area of the business. It's mobilizing more dashers on the road. What does that do to our quality metrics in terms of delivery times?

49:45How does that translate? And so because of that, we're able to figure out if we want to spend, you know, spend the dollar or spend the time, the team's time on improving conversion or spending more money in marketing or onboarding more dashers, we're signing more restaurants, we're adding more grocery stores, right? So we're able to look across the whole business and figure out what is what is the right mix of actions to take to achieve our goals. I could see as you talk about this, why this is so important in a marketplace, especially a multi -sided marketplace where there's always tradeoff decisions between supply investment and demand growth and dash or growth.

50:29I don't even know. My brain would explode trying to think about all these things. So I get exactly why this is so important to business. Okay. And then in terms of the simple recommendation, I think when people here, like, yeah, keep it simple, they're like, yeah, yeah, we're going to keep it simple. What are some things that point to this is not simple that tell you like, no, this is way too complicated. You should try to simplify this metric, even though it's not ideal. It's not the perfect metric, but it needs to be simpler. Yeah. So we had a score from from merchant health, which we tried experimenting with, which was a combination of factors that we had found would lead to a merchant being on the platform and getting an order.

51:14So we wanted to make sure that the merchant would had active hours on the platform and had images and had a full menu that was accurate and robust and a number of different inputs. And we created a composite that weighted all of these different inputs. And then we were like, what is our merchant health score? Right. And you were like, it's, you know, 0 .35. It's not 35%. So what is that? What is like that 0 .35? I don't know what it is. So instead of that, we said, what are the most important factors in order? First, let's measure how many of the new merchants are getting an order within their first say seven days on the platform.

51:57And then let's look at how many of our merchants are doing these things we know are important. So these inputs. So let's goal our team on getting like merchant photo coverage up. Let's goal the team on making sure that we have open hours, accurate hours, right? So figuring instead of, yes, someone might say it's simpler to have a composite metric, but it was so hard to understand what it was and how to move it that it became meaningless and ultimately moving to something that was simpler to understand, even if it meant having three metrics instead of one, it ultimately was better for the team because folks knew what they were trying to move.

52:41And so yeah, maybe we missed number four, five and six on the list of things, but you got one through three and that's 95 % of it anyway. So once we get success with that 95, then let's talk about figuring out the other five percent. It's so funny because this is exactly what we went through at Airbnb. We had a, we call that a healthy host. I led the host quality team for a while and we came up with this healthy host metric. That was six factors of a host like the cancellation rate, the review rate, their response rating, things like that. And then we're just like, cool, let's move this, miss make more host healthy.

53:16And then you end up like, okay, we're just one to weave or Cassan and what about all these others? And we ended up basically focusing on one at a time. And so let's just make that the goal for now and then rotate through the different biggest lever opportunities to move. Exactly. I think in hindsight, for the example, you give like which of those six things are actually the most important, right? And if you're able to then quantify which one matters most, you work on that one first and you materially move that one. And then you, you know, you work on the next one. You want to move them all, but like being able to prioritize and know what you're going to get for a 20 % improvement in, say, your cancellation rate, right?

53:53That's, that's where analytics, I think, can add a lot of value because, yes, ultimately, you'll get to all of them. But the way you do that and the time can have a meaningful impact on your growth. If you can target the most problematic things first and solve those, you get more bang for your buck and that compounds over time. And so doing the things that matter first and most quickly, like, is a competitive advantage, in my opinion. The other thing we found along those same lines is rotating between different metrics is so not efficient because you get good at we're going to move this metric and your teams like cool, we totally understand this lever, like cancellation rate, we become really smart at cancellation rate.

54:35And then three months later, you need a switch to response rate and they have to learn a whole new paradigm of how to think about it. And it's just super inefficient. So we found basically just like keep a team on the metric until there's no more opportunities and find, give another team one of these other metric that yeah. So many lessons. Okay. And the first thing you said on how to pick a good metric about this idea of short term metric that have long term impact. How did you phrase that again? Yeah. So we find proxy metrics for long term outcomes. Awesome. And it's simple. It's so similar to the simple metric and it all comes down to again, just like the metric should be something you probably you can move, you can understand.

55:15That's close enough to this ideal, perfect metric, but it's an necessarily the entire idea. Okay. Also, many things as long as lines of just like picking metrics, working with metrics that you've learned that would be worth with metrics, we're often looking at the average. And I think we talked about this a little bit earlier, but but making sure that you're looking at the edge cases and your failstates is also really important. And so we often will set goals actually and create metrics around those edge cases. So like the disaster delivery is the ones that go terribly wrong. So we have this concept of never delivered, which is orders that are never delivered.

55:56We're really great at naming things that do our task. And they they're very rare. And so if you were just looking at the average effect or the average consumer experience that would never come up, if you were just measuring quality based on sort of average values of delivery times and lateness and sort of those you would these wouldn't show up because they are so rare, but they're terrible. I mean, they're just they're terrible experiences for consumers. They lead to churn. They're incredibly expensive, because you're refunding an order or repurchasing food to and having to send another dash or to deliver that repurchased food.

56:37So they're very expensive. They're costly from a consumer experience standpoint. And I think if you're not looking for these failstates, they are often missed. So I think when you're picking metrics, yes, you want to improve engagement and you want to improve conversion. And there's a lot of things that are kind of averages overall that you want to move. But it's so important to find these edge cases and these failstates and actually set concrete goals around eliminating them because it can be really powerful. So the tip here is actually make that a goal like never deliver it. Some team just keep cutting that down.

57:17Exactly. So we have one part of our quality analytics team and we have product engineering and ops on it as well. Their goal is to eradicate never delivered. And in order to do that, you have to understand why they happen, right? Sometimes it's human error, sometimes it's fraud. And then figure out ways that you can prevent them, that you can kind of fix them while it's happening. And ultimately, just get rid of them from the system. And you're never going to completely get rid of them, but you can make a meaningful impact to make them even more rare than a fraction of a fraction of a fraction of a percent.

58:00Yeah. And I feel like people may be hearing this. And like, of course, why would you not focus on terrible word experiences? But I think in most companies, they look at the big numbers, they look at the averages, as you said, like, it's almost never happens. Why do we even spend any time on this? And your point is you should actually spend time on these really terrible experiences, even if it's a tiny portion of your business. I guess maybe share why that's important is that just because that has trickle down effects on the brand. Yeah. I mean, I think it's a couple things. So just because something doesn't happen frequently, doesn't mean that it's not important.

58:36So the never delivered example is a great one, and that this is leading directly to churn. And it's also costing a lot of money far more than its frequency would suggest. And I think the fact of the matter is, is when you have things that cause churn, you're losing all of that consumer's subsequent orders. And that is not necessarily observed. You're just seeing one bad experience. You're not seeing all of the lost orders because they're lost. And so I think that sometimes this is an area where the data doesn't show you the full picture. And being able to quantify the impact on engagement, on profitability, will make it stand out as something that really matters that you would, you know, maybe miss if you if you weren't really looking for it.

59:25And then I think the other thing is with something like log in errors, sometimes you don't see it in the data because people can't even get into the data. If you're not able to log in, right, you're not making any purchases, you're not ordering. And so you may not see it in the data that you're looking at. And so that's also something that I think is important for data folks to think about, which is what data don't we have, what data might we be missing, where might there be opportunities and things that we actually need to identify and fix that we may not see because in this case with log in failures, they're not able to log in.

1:00:02And so we're missing out on there, they're not in the denominator. And so we're missing out on on them from the data set entirely. Just a couple more questions. There's one that I skipped that I'm just going to come back to. It's completely out of nowhere. But I think it might be interesting is about global, a global data org. So you were on a global data org, you have data scientists and analysts and biz ops people all over the world, not just the US. I'm curious just what the how is it different managing data people in different countries versus just the US would have be what's a big difference.

1:00:36Everyone always asks about the differences. What I am surprised by is how similar things are, how similar people are, the data scientists themselves, but also consumers and dashers and careers as we call them at volt. There's a lot more similarities than differences. I do think that when you built a business in the US and then you introduced new countries, having different currencies and different languages adds complexity that you weren't necessarily familiar with. I think similarly in EU countries versus non -EU countries in Europe, there's different regulation. So that adds a fun layer of complexity.

1:01:21So I do think that it adds complexity to what you're to the problem set. But ultimately, so many of the problems are the same. It feels a little bit like going into a test with like having seen the answer key. And so for me, there are problems we've encountered at volt through volt analytics where I'm like, oh, I feel we've had a similar problem. I have an instinct for what the answer might be. Let's still test because there could be differences, cultural or otherwise. But I feel like I know where we're going to end. And then sometimes there are problems where it's new for one reason or another and it's exciting.

1:02:07Let's see if things are different here. Let's see what ideas might work in a volt country that don't work in a door dash country and vice versa. So I think I tend to focus more on what's the same and then am pleasantly surprised when I find things that are different because that keeps it keeps you on your toes and keeps things interesting. I'm going to take us to AI corner. This is the segment we have in the podcast where I try to understand how people are using AI in their day to day and in their business. I'm curious if you've found some really interesting way of using AI ideally and like you can go in either one of these directions and how you and your team work day to day using AI tools to make it more efficient or integrating AI into your product making door dash better.

1:02:59Yeah, I mean, I think that there are opportunities in both. I think one of the things I'm really excited about is actually so the former. So in helping to make the team more productive, we do something called office hours at DoorDash, the analytics team. And it's something that we started eight years ago. And it was a way to provide support for teams at a time we just didn't have the bandwidth to support. So we would go we would in the early days we go sit in a room and we'd say come on in and we'll help you with anything you need help with. We'll help teach you SQL. We'll help look at some of your work.

1:03:40We'll be a thought partner. You could just come learn what we're working on whatever it was. We would do two hours every week of office hours at different times to be friendly to different time zones. And I think one of the things I'm excited about is being able to really empower some of the folks that are still coming to office hours for one thing or another to be able to use AI to help edit queries on their own, for example, to be able to say here's a query I want to make this please adjust this to our grocery business so that I can see you know the GOV for grocery. And so working to build these tools that will help not just our team in terms of time saving and also to be honest folks folks are going to use it on our team but really to be able to empower non technical users to be able to to do things on their own and not have to take up bandwidth for for the analytics team.

1:04:40So essentially it's the chatbot that anyone in the company can talk to you to get advice on how to write SQL queries query data and things like that. Yeah. Is there a clever name for this chatbot per chance? So it's not clever. It's called AskDataAI and that's named for our internal Slack channel that used to be the open kind of Q &A for people to ask data. So it's not at all clever but again it's clear. It's clear. It's a theme of very very specific naming conventions that we have at at DoorDash never delivered and ask data AI. I love it. Just clear clarity above all else. That's something I've learned from an editor that I work with.

1:05:25Jess is there anything else that you want to share or leave listeners with for folks that are trying to build their data teams, make their data teams more efficient? Is there any final wisdom nugget you'd want to share? I think the only thing that I sort of want to reiterate is that you don't necessarily need a formal training in whatever it is you're building and I think that also goes towards the folks that you hire onto the team. And so I mentioned earlier that we've had a lot of folks go to product or go to ops from the team. What I didn't mention is how many folks we've actually had join the analytics team from partner teams.

1:06:08So whether that was from engineering or from our ops team or marketing or finance, we've had a lot we've actually had a lot more input. We are a net importer of talent as opposed to a net exporter of talent. And I think that that's because I my own experience coming over from operations from being a GM and making that transition into analytics. I find that I'm drawn to other folks who want to make a similar transition. Now again, you have to have the technical skills and most of these folks have acquired these skills on the job, you know, whatever job they are doing at DoorDash before they transitioned to the analytics team or they had maybe some formal training in school.

1:06:55But I love seeing the folks that make that transition and actually want to join the analytics team even if that they're not a career or data scientist. I think it creates a really unique environment where you have folks on the team from different backgrounds with different expertise who can teach each other thing. So I can teach you how to build a discounted cash flow model in Excel. And I can learn how to make kickass slides, you know, from someone who has a background in consulting. And I can learn about common gotchas in statistics from someone who comes to us with a master's or a PhD in statistics.

1:07:38And we've got our econometrics folks and we've got our economists. And we just have a group of people with different backgrounds who can all teach each other how to be better. And we're not all carbon copies, you know, of each other. So I'm hearing as you try to optimize almost for a lot of different complementary skills in very different backgrounds almost. Exactly. And also people who have experience at different size companies. I think, you know, I love folks from startups who have that that hustle and grit. But I also love folks who've seen what scale looks like and can help us see around corners as far as what problems we will encounter as the business is growing.

1:08:23And I think it's not just about a diversity of skill and a diversity of background. It's also, you know, diversity of sort of prior company and stage that can be really a unique way to think about structuring your team so that you get the best of both worlds. Amazing. Well, just when you thought we were done, we reached our very exciting lightning round. Are you ready? I am. Let's do it. Let's do it. Okay, first question. What are two or three books that you've recommended most other people? I tend to read fiction, particularly historical fiction. And I love spy novels. So I think my brain is always in problem solving mode, even when reading a recent book that I read that I enjoyed was The Rose Code by Kate Quinn.

1:09:14And it's about women code breakers in World War II. And I just I really enjoyed that. But rather than recommending a book, I guess I did just recommend a book. But rather than recommending another book, I am going to recommend the Libby app and supporting your local public library. Because I love the library and I love Libby. So I'll give that as my other recommendation. Beautiful. Very on brand of cheering economy company stuff. Libby, cool. Okay, next question. Favorite recent movie or TV show? Yeah, I don't actually watch a lot of TV. Definitely don't watch a lot of movies. In fact, haven't seen some of like the movie great.

1:09:54I get yelled at a lot by my friend. I can't believe you haven't seen that. I tend to rewatch things. So series from the past over and over again. I think it's just like how I shut my brain off. So I've recently rewatched The West Wing, which is one of my favorite shows of all time, probably for like the 50th time. Oh my god. And Aelius, which was like a Jennifer Garner series from like the early 2000s. Also spy. So I'm noticing like a theme. I think I really love these spy. The spy genre. But yeah, I watch those. They're both great, but not at all current. Perfect. Perfectly acceptable. You have a favorite product that you recently discovered and that you really loved.

1:10:39This is a bit of a curve ball. So Korean sunscreens. I so I burn really easily. So I have to wear sunscreen. And I love Korean sunscreens. It was introduced to them by a friend of mine. And they're just far superior to what we have in in the US. So I highly recommend people give Korean sunscreens a try. Particularly, there's a beauty of Josian brand at sunscreen. It's just amazing. And it's delightful to wear, which is important when you have to wear it every day. So I've been trying to wear more sunscreen as an age. And so this is a really good tip. Is that it was that a brand you recommended or yeah.

1:11:15So beauty of Josian is the brand is another brand is entry, which also has a great sunscreen. But I'll be honest, almost every Korean sunscreen I've tried is just is great. Okay. I'm googling this as soon as we get off. Do you have a favorite life motto that you often come back to and share and or share with family and friends even work earnly? I do. So there's a John Steinbeck quote, which I'm not big on quotes. But I like this one which is that it's a common experience that a problem difficult at night is resolved in the morning after the committee of sleep has worked on it. I find that that's something I really live by.

1:12:00I think first off, I love sleep. And I try to get as much of it as possible. But the other thing is that if I'm stuck on a problem or if I am writing a response to something where like a tense issue or an emotional issue, often I find that if I put down my thoughts, go to sleep, check it in the morning. I end up with a better outcome. So I, you know, all of a sudden you have a new perspective and clarity on a problem you were stuck on or you realize that you weren't clear in the way you were communicating your thoughts because you were emotional about something and you're able to put together a much better response to to an email or to whatever problem you're handling.

1:12:43So sleep can solve lots of problems. I love sleep as well. Moe is telling me, wife, let's come to sleep. Okay, I'll be there soon. I love that advice. Okay, two more questions. Who's influenced you most in your career? Is there something that comes to mind? So I think two answers, multi -part answer. So I think first, you know, my career has been in male -dominated industries and I've worked with just some incredible women who've really influenced me. When I was a banker, there was, there were two senior bankers, Vanessa Roberts and Gina Taron who at Lehman Brothers where I worked and they were just so incredible.

1:13:25They were just so good at their jobs and I found that really inspiring. And then at Dordash, Tia Sharingham who is our GC and Liz Jarvisheen who leads comms are just like dominant in their fields. And I think that that's really empowering and been big influences on me to just see strong, powerful women kind of kicking ass and that helps me believe that I can do the same. So that's one answer and then the other answer, sort of cliche, but my parents, my mom was a statistician at the UN before she got married and she actually chose to stay home and raise three children. But when I, so I'm the youngest and when I was in, I think it was elementary school, she decided to go back to school, switch careers and become a nurse.

1:14:16And so the fact that she embarked on this completely new career in her 40s, after 15 years as a stay at home mom and my father supported this, I think that that was really, really influential and was probably the first time I saw that you can do whatever you put your mind to, no matter your age, no matter your circumstances. So that was really influential and I don't think I've ever told her that. So hi mom. But yeah, I think that was that was influential for my career definitely. So beautiful answer. Fun fact, I worked with Liz at Airbnb. Your person just mentioned in the comms team. Love you, J .S.

1:14:57She's amazing. Final question. So when you joined Dordash, imagine it wasn't obvious that it was going to work. Imagine it was still like, this is a crazy idea. Maybe it'll work. Maybe not. Is there a moment you recall where you're like, I think this is going to be a big success. I think this is actually going to work out. To be honest, I went into Dordash because I wanted to learn for the experience. I thought it was interesting problems with interesting people. I never thought too much about whether it would work. I didn't even want it to win. I think there's sort of two moments that stand out.

1:15:36One was when the third party market share data showed that we had become the number one player after I think we started at number four or five. And I think that was really exciting to see the trajectory and to see to see us gain in category share. That was exciting. I think I probably didn't see it until like months after it had happened because we don't spend a ton of time focusing on it. But I do remember somebody wanted to include the graph in some presentation, some sales material. Oh, we're number one. That's incredible. We used to be number five. So I'd say that that was one. The other one that stands out was I used to the first talk I gave in a lot of these like startup talks in the early days in Boston.

1:16:28And I'd ask the audience like how many of you have usedordash? And they'd be like three people who would raise their hand. And then it was a few years ago maybe like 2018, 2019. And I was giving a talk and I asked the audience like how many of you have usedordash? And like almost everyone's hands went up. And that was actually pretty memorable for me because in my mind, we were still the sort of small startup that no one had heard of where I had to over -enunciate the Ds in door dash. So people didn't think I worked for George Ash, the 90s denim company. And so that was pretty meaningful to me when just so many people had used the product and or were consumers of door dash was pretty exciting.

1:17:18And I still get excited. I saw door dash mentioned in a book recently. So that was reading. And I was like, where the book? So those little things when you become part of the kind of cultural lingo that I think are really really special. Well, I'm a very happy customer of door dash. I've never had a never deliver. It's always it's always there sometimes a little late. Usually it's perfect. Thank you for everything you do go team door dash. Two final questions. Working folks find you online if they want to follow stuff that you do. I know you've been doing more writing on LinkedIn and things like that.

1:17:51So just help people understand where to find you. And how can listeners be useful to you? Yeah. So as you mentioned, to find me, LinkedIn, I don't have a huge social presence, but I am on LinkedIn and I am currently writing a series of blog posts about my experience building a global analytics org at door dash. Some of the lessons I've learned over the last 10 years. So definitely check those out. And as far as your second question of how listeners can be useful to me, I guess read read the post on LinkedIn. And I'd love to hear what people think. Whether you agree with my point of view or not, that being said, be nice.

1:18:31Like I want honest feedback, but I want kindness as well. So yeah, just engage with the content and let me know what y 'all think. I think I do have a broader ask, which is just to encourage folks listening to truth seek. Something I take seriously at door dash, it's a company value. But there's a lot of misinformation out there and it's often up to us as individuals to figure out what's backed and what's fiction. So I have a sort of a plea for folks to do your best to search for the truth and speak the truth. And I think we'll all be better off for it. And of course, use door dash. So yes, I had three, there are three things that listeners can do.

1:19:18Your door dash dot com. That was awesome. I love that last point as well. At the end of the edition to use door dash, Jessica, thank you so much for being here. Thank you for having me. It was a lot of fun. Same for me. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts Spotify or your favorite podcast app. Also, please consider giving us a rating or a leaving review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast dot com. See you in the next episode.