Jessica Lachs
VP of Analytics & Data Science, DoorDash. On 1 show, 1 appearance. The Shows tab opens the full record on each.
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Jessica Lachs built and oversees the global analytics and data science organization at DoorDash, having joined the company in 2014 as its first General Manager to launch new markets. Before DoorDash, she began her career in investment banking at Lehman Brothers and founded the social gifting startup GiftSimple.
1shows
1appearances
28statements
2resolved
2supported
0contradicted
100%fully supported
Everything Jessica Lachs said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Centralized analytics teams are superior to embedded analysts, says Lachs
“I think there are people out there who think that analytics should be embedded into business units. I strongly disagree. I believe a central model, a center of excellence is superior and I'm happy to talk about why, but that's something that I feel quite stron…”
Lachs: Retention is a terrible target metric for rapid experimentation
“Retention is a terrible thing to goal on because it's like it, 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.”
Lachs: Analytics must drive business impact, not act as a support desk
“The first is I believe that analytics should have a seat at the table, just like engineering and product and sort of the business folks, the operators. For me, analytics is a business impact driving function and not purely a service function.”
Lachs: Centralized data teams must share partner business teams' goals
“I think on the goal side, that is something where we have the same goals that our partner teams have. And I think that that's actually an important part of a successful central model.”
Lachs: Data teams earn their influence by surfacing proactive business opportunities
“We get the seat at the table and we need to earn it by bringing opportunities that we all can go and go after.”
Lachs: Centralized data orgs maintain higher, more consistent hiring bars
“So the first thing is a consistent and high talent bar. I think this is something I saw when we would have some sort of pockets of analytics folks embedded is the having a consistent bar for talent in terms of what we're looking for. What are the technical ski…”
Lachs: Centralized data orgs boost retention through internal mobility
“Number two is actually growth opportunities. So if you're siloed, you may be the most senior data person within keep picking on marketing. But you might be the most senior sort of data scientist within marketing. Where do you go from there? I think when you ha…”
Lachs: Centralized data orgs unify metrics and prevent redundant models
“The third thing is just consistency of methodologies and metrics. So you don't have sales that was as defined by one team and sales as defined by another team. You just have. Sales and everybody is using kind of the same metrics, the same methodologies, and yo…”
Lachs: Analytics teams must set explicit goals to protect exploratory research
“You have to be very intentional to carve out time for exploratory work for deep dives, because as you mentioned, there are always more questions and more work to be done than hours in the day. And so I think being intentional about it and setting goals for you…”
Lachs: Curiosity Cannot Be Taught to Team Members
“You can't teach curiosity. Or at least I haven't found a way to do it.”
Candidate reaction to being corrected is a crucial hiring signal
“Seeing how people react to being told they're wrong is, 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”
Lachs: Non-Technical Data Leaders Keep Deep Technical Teams Focused on Business Impact
“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, you know, I, I'm ab…”
Lachs: Solving immediate problems beats prematurely designing global orgs
“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 like, you know, a global org that you're trying to build…”
Lachs: Simple, intuitive metrics outperform complex composite scores
“If 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 than your made up composite score that nobody understands.”
Teams must goal against edge cases and fail states, not just averages
“With metrics, we're often looking at the average, and I think we talked about this a little bit earlier, but making sure that you're looking at the edge cases and your fail states is also really important. And so we often will set goals actually and create met…”
Lachs: Standard metrics understate churn because lost future orders are not observed
“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 somet…”
DoorDash structures analytics centrally while podding members directly to partner teams
“For us, we have a central analytics team, but we are, we're divided up into pods that map perfectly with how product engineering, operations, marketing are structured as well. And so our team de facto has these Folks embedded with our partner teams, even thoug…”
Lachs: DoorDash Referral Payback Was Bimodal Due to Online Fraud and Couponing
“And what we noticed was that referral as a channel was a bit misleading when you would look at the average in terms of payback and that it was really a bimodal distribution. And you had one group of really great consumers who were referring other really great …”
Lachs: Making trade-offs visible gets stakeholders to drop non-essential asks
“Sometimes people don't necessarily realize the trade-offs and when you make them apparent and you put them front and center, they realize that, oh, actually, you know what? That, that asset's not important. That can wait. So I think that that's definitely some…”
Lachs: DoorDash uses real company historical problems for early interview business cases
“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 …”
Lachs: DoorDash employees must do deliveries or support four times yearly
“We have a program, a WeDash program, and Keith Yandel, 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”
Lachs: Translating metrics into a common currency enables cross-functional trade-offs
“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 vol…”
DoorDash uses Gross Order Value and volume as common decision currency
“So, I mean, we measure things in terms of GOV so gross order value and also volume.”
One line per show, most statements first. The link opens Jessica's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 LENNY'S PODCASTLEDGER | VP of Analytics & Data Science, DoorDash | 1 | 28 | 100% 2/2 | full record on Lenny's Podcast → |