Jessica Lachs (VP of Analytics and Data Science at DoorDash)14 July 2024

Building a world-class data org

8Frameworks

15Insights

Episode overview

Jessica Lachs explains why analytics should be a business-impact function with a seat at the table and why DoorDash uses a centralized reporting structure with analysts organized into cross-functional pods. She describes hiring for curiosity and ownership, protecting time for exploratory work, and using simple short-term proxy metrics that predict long-term outcomes. The conversation also covers DoorDash's early operating culture, customer-focused programs such as WeDash, failure-state metrics like “never delivered,” and AI tools that help nontechnical employees work with data.

Key ideas

Centralize analytics reporting to preserve a consistent talent bar, shared methodologies, career mobility, scale, and team culture while embedding pods with partner functions.
Give analytics the same goals as product, engineering, marketing, and operations so it acts as a proactive business partner rather than a ticket-driven service.
Protect exploratory work through explicit goals and hackathons; a referral deep dive exposed fraud and produced changes such as stronger checks and referral caps.
Hire for curiosity, self-motivation, structured problem-solving, adaptability to corrective feedback, and the ability to decide with incomplete information.
Choose simple, intuitive short-term proxy metrics that experimentation can validate against desired long-term outcomes instead of opaque composite scores.
Translate business levers into a common currency such as gross order value and volume to compare investments across pricing, logistics, marketing, Dashers, and merchants.
Measure rare but costly failure states separately; DoorDash's “never delivered” metric captures experiences hidden by averages that drive refunds and customer churn.
Encourage extreme ownership across role boundaries, including customer calls, support work, product testing, and qualitative research when those actions unblock decisions.

Transcript available · source text is retained privately and is not published

Frameworks in this episode

Leadership5 steps

Central Org, Embedded Pods

Keep analytics reporting centrally, but mirror partner-team structure with pods that share their goals

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Strategy5 steps

Fail-State Goaling

Name your worst-case experiences, set a team's goal on eradicating them, and hunt for the data you don't have

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Leadership5 steps

Outcome Ownership Over Role Boundaries

Goal people on the outcome, not the job description, and let them cross any function to reach it

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Innovation5 steps

Protected Deep-Dive Time

Goal the team on self-directed insight and use hackathons to stop exploratory work being eaten by inbound asks

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Strategy5 steps

Proxy Metric Selection

Goal on simple short-term inputs you can prove drive the long-term output you actually want

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Strategy5 steps

The Common Currency Model

Convert every lever in the business into one shared unit so cross-team tradeoffs become arithmetic

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Leadership5 steps

The Curiosity Case Interview

Use a real historical company problem as the case, then test soft skills the candidate doesn't know you're testing

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Communication5 steps

Tradeoff-Transparent Pushback

Never say no to a request — show the three things it displaces and let the asker decide

Read →

People & resources mentioned

Attributed to the moment in the episode. Timestamps are approximate.

People · 15

Jessica LachsMentionsJessica is Vice President of analytics and data science at door Dash
“today my guest is Jessica LAX Jessica is Vice President of analytics and data science at door Dash”
▶ Watch at ~0:30approx.
Elizabeth StoneMentionsElizabeth Stone on her podcast with you talked a lot about her sort non-traditional background for a CTO
“this was referenced when we had Elizabeth Stone on the podcast too she approaches data the same way”
▶ Watch at ~2:00approx.
Lenny RachitskyMentionstoday we've got another very special compilation episode something I've been pulling on more and more with the podcast and the newsletter
“Lenny there's two main things that I think are important when you're structuring a team”
▶ Watch at ~5:00approx.
Riley NewmanMentionsDescribed as Airbnb's first data scientist and former head of data science.
“I don't know if you know Riley Newman well but he built airbnb's First Data team”
▶ Watch at ~14:30approx.
Tony XuCoinedthat was something that is ingrained in our culture from Tony Shu from our founder and CEO
“that was something that is ingrained in our culture from Tony Shu from our founder and CEO”
▶ Watch at ~34:00approx.
Joey GrazianoMentionsJoe graio is our sales guy in Boston
“shout out to to Joey G so Joe graio is our sales guy in Boston”
▶ Watch at ~35:30approx.
Andy FangCoinedStanley and Andy other co-founders really instilled in all of us early in those days
“Stanley and Andy other co-founders really instilled in all of us early in those days”
▶ Watch at ~36:00approx.
Stanley TangCoinedStanley and Andy other co-founders really instilled in all of us early in those days
“Stanley and Andy other co-founders really instilled in all of us early in those days”
▶ Watch at ~36:00approx.
Keith YandellMentionsKeith yendell who's our our chief business officer did your podcast last year
“Keith yendell who's our our chief business officer did your podcast last year”
▶ Watch at ~38:30approx.
Kate QuinnCoinedmentioned
“a recent book that I read that I enjoyed was the rose code by Kate Quinn”
▶ Watch at ~1:09:00approx.
Jennifer GarnerMentionsmentioned
“Alias which was like a Jennifer Garner series from like the early 2000s”
▶ Watch at ~1:10:00approx.
John SteinbeckCoinedEast of Eden by John Steinbeck
“there is a John Steinbeck quote which I'm not big on quotes but I like this one”
▶ Watch at ~1:11:30approx.
Gina TerroneMentionsthere were two senior Bankers Vanessa Roberts and Gina terrone Who at Leman Brothers where I worked
“there were two senior Bankers Vanessa Roberts and Gina terrone Who at Leman Brothers where I worked”
▶ Watch at ~1:13:00approx.
Vanessa RobertsMentionsthere were two senior Bankers Vanessa Roberts and Gina terrone Who at Leman Brothers where I worked
“there were two senior Bankers Vanessa Roberts and Gina terrone Who at Leman Brothers where I worked”
▶ Watch at ~1:13:00approx.
Tia SherringhamMentionsTia sharing him who's now our general counsel
“Tia sheringham who is our our GC”
▶ Watch at ~1:13:30approx.

Resources · 22

DoorDashMentionscompany · DoorDash
“Jessica is Vice President of analytics and data science at door Dash”
▶ Watch at ~0:30approx.
GiftSimpleCoinedcompany · Jessica Lachs
“previously Jessica founded GS simple a social gifting startup”
▶ Watch at ~1:00approx.
Lehman BrothersMentionscompany
“began her career in investment banking at Leman Brothers”
▶ Watch at ~1:00approx.
Lenny's PodcastCoinedpodcast · Lenny Rachitsky
“welcome to the podcast thank you so much for having me”
▶ Watch at ~1:30approx.
WebflowMentionssoftware · Webflow
“this episode is brought to you by webflow”
▶ Watch at ~2:30approx.
AnvilMentionssoftware · Anvil
“this episode is brought to you by Anvil their document SDK helps product teams build and launch software for documents fast”
▶ Watch at ~3:30approx.
JiraMentionssoftware · Atlassian
“maybe even through Jura tickets or build dashboards”
▶ Watch at ~5:00approx.
AirbnbMentionscompany · Brian Chesky, Joe Gebbia, and Nathan Blecharczyk
“he built airbnb's First Data team and it was actually an analytics team”
▶ Watch at ~14:30approx.
PythonUsessoftware · Guido van Rossum
“well Let's Learn Python so I think it kind of came it happened organically”
▶ Watch at ~32:30approx.
DoorDashUsescompany · DoorDash
“four times a year all the employees go out and go dashing or do customer support”
▶ Watch at ~38:30approx.
AttioMentionssoftware · Attio
“this episode is brought to you by ATO a radically new type of CRM”
▶ Watch at ~39:30approx.
AirbnbUsescompany · Brian Chesky, Joe Gebbia, and Nathan Blecharczyk
“which we did at Airbnb also just like what how does everything translate into into nights booked”
▶ Watch at ~48:00approx.
WoltMentionscompany
“as we call them at Vault there's a lot more similarities than differences”
▶ Watch at ~1:00:30approx.
SQLUsessoftware
“we'll help teach you SQL we'll help look at some of your work”
▶ Watch at ~1:03:30approx.
Ask Data AIUsestool · DoorDash
“it's called ask data AI and that's named for our internal slack channel”
▶ Watch at ~1:04:30approx.
ExcelUsessoftware · Microsoft
“I can teach you how to build a discounted cash flow model in Excel”
▶ Watch at ~1:07:00approx.
The Rose CodeRecommendsbook · Kate Quinn
“a recent book that I read that I enjoyed was the rose code by Kate Quinn”
▶ Watch at ~1:09:00approx.
The West WingRecommendstv show · Aaron Sorkin
“I've recently rewatched the westwing which is one of my favorite shows of all time”
▶ Watch at ~1:10:00approx.
AliasRecommendstv show · J. J. Abrams
“Alias which was like a Jennifer Garner series from like the early 2000s also spy”
▶ Watch at ~1:10:00approx.
Beauty of Joseon sunscreenRecommendsproduct · Beauty of Joseon
“particularly there's a beauty of joson branded sunscreen it's just amazing and it's delightful to wear”
▶ Watch at ~1:11:00approx.
Isntree sunscreenRecommendsproduct · Isntree
“there's another brand isn't tree which also has a great sunscreen”
▶ Watch at ~1:11:00approx.
LinkedInUseswebsite · LinkedIn
“I am currently writing a series of blog posts about my experience building a Global analytics or at door Dash”
▶ Watch at ~1:18:00approx.

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Hot Take· 2

Hot Take05:00

Analytics Should Have a Seat at the Table, Not Be a Ticket Queue

Jessica Lachs argues analytics is a business-impact function, not a service org that answers Jira tickets and builds dashboards on request. Her team is expected to have a point of view on decisions and surface opportunities, not just explain the 'why' but recommend the 'so what do we do now'.

Analytics should sit alongside engineering, product, and operations with a real seat at the table
A pure service model, answering questions and building dashboards on demand, was never the team she wanted to build
The job is to find opportunities and bring a point of view, not just answer inbound questions
The team earns its seat by bringing opportunities everyone can go after

“for me analytics is a business impact driving function and not purely a service function”
Jessica Lachs · 05:00

“not just answering the why but answering the the so what so what do we do now that we know this”
Jessica Lachs · 05:30

#analytics#data-teams#leadership

Hot Take05:30

Why a Centralized Data Org Beats Embedding Analysts in Business Units

Lachs holds a contrarian view that a central 'Center of Excellence' model is superior to embedding analysts inside marketing, product, or ops. DoorDash divides its central team into pods that map to partner teams so analysts feel embedded day-to-day, but report up through a central org and share their partners' goals.

Central means reporting lines run through the analytics org, not that goals differ from partner teams
Marketing analytics is part of the broader analytics team rather than reporting into marketing
Pods map one-to-one to how product, engineering, ops, and marketing are structured
Shared goals with business partners keep incentives aligned so your success is their success
They experimented with the embedded alternative in the past and found it more problematic

“I strongly disagree I I believe a central Model A Center of Excellence is superior”
Jessica Lachs · 06:00

#org-design#data-teams#analytics

Explainer· 3

Explainer11:00

The Real Benefits of a Central Analytics Org

Lachs lays out why the centralized model wins: a consistent, high talent bar; more growth paths for analysts; shared methodologies and metric definitions; the ability to see problems across teams and get ahead of them; and a strong team culture and brand that helps recruiting and retention.

One consistent, higher talent bar and hiring rubric across the whole team
Growth opportunities across functions so a senior analyst who is bored in marketing can move to merchant analytics
Consistent methodologies and metrics, so 'sales' means one thing company-wide
Build one churn model with input from six teams instead of six separate ones
Seeing the same problems across teams helps you spot what to automate and see around corners
A proud team culture and brand that aids recruiting and gives analysts peers to learn from

“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…”
Jessica Lachs · 12:30

#org-design#hiring#data-teams#culture

Explainer25:30

How to Test for Curiosity and Judgment in Interviews

Lachs plants something not-quite-right inside a real DoorDash business case to see if candidates notice and investigate. She also watches how people react when told they're wrong, and pushes them to commit to a decision under incomplete information, because that's the real signal she's after even if it isn't what the literal question asks.

Embed something not quite right in the case and see whether candidates notice and where they go with it
Cases use real problems from DoorDash history to test structured problem-solving under ambiguity
Most candidates make a wrong assumption; how they respond to being corrected is a key signal
Push people to make a call under imperfect information because that's how real decisions work
Some questions assess a soft skill separate from the literal problem being solved

“have something that is not quite right within the case that you're presenting and see if people notice”
Jessica Lachs · 25:30

“I always push people to say if you had to make a call right now what would it be”
Jessica Lachs · 28:00

#hiring#interviewing#data-teams

Explainer46:30

Quantify Every Lever in a Common Currency

DoorDash converts every business lever, price, selection, quality, delivery time, into a common currency of gross order value and volume. Knowing what a dollar of price cut or a minute of faster delivery buys in volume lets teams like marketing and logistics make tradeoffs against each other and decide where to spend a dollar for the most return.

Translate disparate metrics into a shared unit so teams can compare across functions
Example: what does lowering price by a dollar, or delivery time by a minute, get you in volume?
A common currency lets marketing and logistics trade off against each other
Everything ultimately maps to gross order value (GOV) and volume
Having an inventory of levers and their payoffs speeds up and improves decisions

“if I were to lower price by a dollar what would I get in terms of we'll say volume”
Jessica Lachs · 46:30

#metrics#marketplace#decision-making

Story· 4

Story17:30

The Cupcake Fraud Deep Dive That Fixed DoorDash's Referral Channel

A hackathon deep dive found referrals looked below-average as an acquisition channel, but the team refused to just cut spend. They committed referral fraud themselves, ordering cupcakes over and over, and discovered the channel was bimodal: great customers referring great customers, versus discount-hunters posting codes online. The fix was fraud caps and better checks, and a lesson that averages hide bimodal distributions.

Referrals looked below-average on engagement and payback versus other channels
The team created new accounts and committed referral fraud, ordering cupcakes repeatedly, to see how the abuse worked
The channel was bimodal: strong customers referring strong customers, and discount-hunters posting codes online
Lax fraud rules and no caps let the bad group drag down the whole channel's efficiency
Outcome: better fraud checks and caps on referrals, plus a reminder that the average can be misleading

“we actually tried referring each other we tried committing referral fraud creating new accounts to get around rules and we uncovered a lot of fraudulent…”
Jessica Lachs · 18:00

“the average can be incredibly misleading and so looking at distributions”
Jessica Lachs · 20:00

#growth#fraud#referrals#data-analysis

Story28:30

The Head of Analytics With No Data Science Degree

Lachs jokes she has a job she'd never be hired for, because she has no traditional data science background, an art portfolio in school and a finance start at Lehman Brothers. She taught herself SQL and Python out of necessity in DoorDash's early days, and argues that finance-rooted pragmatism plus hiring people smarter than her keeps the team focused on business impact.

No formal data science training; background is art and investment banking
Self-taught SQL and Python because DoorDash needed someone to set and measure market goals ten years ago
Founder Tony Xu recognized the aptitude despite the lack of formal training
Her non-technical background lets her hire PhDs and ML experts while keeping them focused on business impact

“I sort of joke that I have a job I'd never be hired for”
Jessica Lachs · 29:00

“I became a data scientist out of necessity uh I completely self-taught uh in terms of SQL and Python”
Jessica Lachs · 29:30

#career#data-science#self-taught

Story33:30

Extreme Ownership: Garbage Duty, Cupcakes, and Dashing Pizza

Lachs traces DoorDash's culture of extreme ownership to founder Tony Xu, from taking out the garbage on Saturday nights to a sales rep handing out promo codes at 5am despite being comped only on signing merchants. During an early site outage the whole 20-person company jumped on customer support, and Lachs, too new to use the tools, went dashing to deliver pizza to the office herself.

Ownership means doing whatever the outcome needs regardless of your title
In 2014 Boston, a four-person team handed out promo cards attached to KIND bars outside the T at 5am
Sales rep Joey Graziano joined the promo handouts even though he was only comped on signing merchants
During a major early outage, the whole ~20-person company jumped on phones to do refunds and support
Lachs went dashing to deliver pizza so three Dashers wouldn't have to, one of the largest refund nights relative to their bank balance

“roll up your sleeves do something that's not your job I think back to you know early days of taking out the garbage on Saturday…”
Jessica Lachs · 34:00

“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”
Jessica Lachs · 37:30

#culture#ownership#startups#doordash

Story45:30

Why the Merchant Health Composite Score Failed

Data scientists love composite metrics with weighted coefficients, but nobody understands whether a 0.1 move is good. DoorDash's Merchant Health score, blending active hours, photos, and menu completeness into one weighted number, became meaningless. They replaced it with a few simple, understandable metrics the team could actually goal against, even if that meant three metrics instead of one.

Composite metrics with coefficients are hard to interpret; a 0.1 change tells you nothing intuitive
The Merchant Health score bundled active hours, images, and full accurate menus into one weighted number
A score of '35' was meaningless because nobody knew what it represented or how to move it
They switched to concrete inputs: measure new-merchant first-order rate and goal teams on photo coverage and accurate open hours
A simple metric people understand and can talk about beats a more 'perfect' composite nobody grasps
Getting 95% right with three clear metrics beats one opaque composite

“I always encourage folks just pick something simple even if it's not perfect”
Jessica Lachs · 46:00

“it was so hard to understand what it was and how to move it that it it became meaningless”
Jessica Lachs · 52:00

#metrics#simplicity#analytics#merchants

Tool· 1

Tool1:02:30

'Ask Data AI': Empowering Non-Technical Users to Self-Serve

DoorDash's analytics team has run weekly office hours for eight years to teach SQL and act as thought partners. Lachs is now excited about an internal AI tool, 'Ask Data AI', that lets non-technical users edit and adapt SQL queries themselves, freeing analyst bandwidth. It's named after the internal Slack channel, in keeping with DoorDash's habit of very literal naming.

Analytics office hours have run two hours weekly for eight years across time zones
The AI tool lets users adapt an existing query, e.g. to the grocery business, on their own
Goal is to empower non-technical users to self-serve rather than consume analyst bandwidth
It's named 'Ask Data AI' after the internal Slack Q&A channel, favoring clarity over cleverness

“it's not clever it's called ask data AI and that's named for our internal slack channel”
Jessica Lachs · 1:04:30

#ai#tools#self-serve#sql

Takeaway· 5

Takeaway15:30

Be Intentional About Protecting Time for Exploratory Work

Exploratory deep dives are the first thing to get cut when inbound requests pile up, because a known deliverable feels higher expected-value than an uncertain-ROI investigation. Lachs protects that time deliberately, setting team goals around self-directed insight work and running hackathons to carve out days for it.

Deep dives are always the first thing sacrificed when inbounds surge
Set explicit team goals around finding insights through self-directed work to stay accountable
Hackathons carve out dedicated days to chase interesting threads and find opportunities
Business partners support and even encourage this time because so many road-map-driving insights came from it

“you have to be very intentional to carve out time for exploratory work for deep Dives”
Jessica Lachs · 15:30

“we would do hackathons for our team to carve out days to just go and look into these really interesting things and find Opportunities”
Jessica Lachs · 16:30

#analytics#productivity#team-management

Takeaway20:30

How Data Teams Should Push Back: Share the Tradeoffs

Rather than flatly saying no or suffering in silence, Lachs recommends making tradeoffs explicit. Because her team shares goals with business partners, they can ask whether a new data pull is more important than the three things already planned, which often reveals the ask can wait.

Saying no is hard, especially for people-pleasers when the ask is quick and easy
Leadership should set the operating model so junior folks aren't forced to always say no
Shared goals let you frame requests as 'is this more important than these other three things?'
Making tradeoffs front and center often surfaces that the ask actually isn't important
Occasionally just knock out a quick request to build goodwill

“always share the tradeoffs don't kind of suffer in silence”
Jessica Lachs · 22:00

#prioritization#team-management#analytics

Takeaway24:30

The One Trait She Hires For: Curiosity You Can't Teach

Beyond a table-stakes technical bar, the differentiator among top analysts is curiosity and self-motivation, the instinct to pull on a thread when something looks off rather than stopping once the question is answered. Lachs says she hasn't found a way to teach it.

Technical skills are table stakes, screened separately
Curiosity and self-motivation are the standout traits in top talent
The valuable person digs into something that seems odd even after they've technically finished the task
Curiosity, in her experience, can't be taught

“the first thing is just curiosity you you can't teach curiosity or at least I I haven't found way to do it”
Jessica Lachs · 24:30

#hiring#data-teams#curiosity

Takeaway44:30

Goal on Short-Term Proxies, Not Retention

The core lesson Lachs draws from bad metrics: find a short-term metric you can move that drives a long-term output. Retention is a terrible thing to goal on because it's nearly impossible to move meaningfully in the short term, so you should identify and experiment on its input drivers instead.

You learn the most about metrics from picking the wrong ones
Find a short-term measurable metric that drives the long-term output you actually care about
Retention is a poor goal metric because it can't be moved meaningfully in the short term
Isolate the inputs that drive retention and test whether moving them moves the long-term output

“ultimately you want to find a shortterm metric you can measure that drives a long-term output”
Jessica Lachs · 44:30

“retention is a terrible thing to goal on because it's like it it it's almost impossible to to drive in a meaningful way”
Jessica Lachs · 45:00

#metrics#analytics#retention

Takeaway55:00

Set Goals Around Fail States, Not Just Averages

Rare disasters like a 'never delivered' order vanish inside average quality metrics, yet they drive churn and cost far more than their frequency suggests, refunding the order, repurchasing food, and sending another Dasher. Lachs makes eliminating these fail states an explicit goal, and notes some failures, like login errors, never even appear in the data because affected users drop out of the denominator.

Rare edge cases and fail states are invisible when you only track averages
'Never delivered' orders are rare but drive churn and cost far more than their frequency implies
A dedicated team with product, engineering, and ops has the explicit goal to eradicate never-delivered
Churn costs are unobserved: you see one bad order, not all the future orders you lost
Login failures don't show up in the data because users who can't log in never enter the dataset
Data folks should ask what data they're missing, not just analyze what they have

“we have this concept of never delivered which is orders that are never IED were really great at naming things at door Dash”
Jessica Lachs · 55:30

“their goal is eradicate never delivered”
Jessica Lachs · 57:00

#metrics#quality#churn#analytics

Key ideas

Frameworks in this episode

Central Org, Embedded Pods

Fail-State Goaling

Outcome Ownership Over Role Boundaries

Protected Deep-Dive Time

Proxy Metric Selection

The Common Currency Model

The Curiosity Case Interview

Tradeoff-Transparent Pushback

People & resources mentioned

People · 15

Resources · 22

Insights & moments

◆Hot Take· 2

Analytics Should Have a Seat at the Table, Not Be a Ticket Queue

Why a Centralized Data Org Beats Embedding Analysts in Business Units

✶Explainer· 3

The Real Benefits of a Central Analytics Org

How to Test for Curiosity and Judgment in Interviews

Quantify Every Lever in a Common Currency

❝Story· 4

The Cupcake Fraud Deep Dive That Fixed DoorDash's Referral Channel

The Head of Analytics With No Data Science Degree

Extreme Ownership: Garbage Duty, Cupcakes, and Dashing Pizza

Why the Merchant Health Composite Score Failed

✦Tool· 1

'Ask Data AI': Empowering Non-Technical Users to Self-Serve

▲Takeaway· 5

Be Intentional About Protecting Time for Exploratory Work

How Data Teams Should Push Back: Share the Tradeoffs

The One Trait She Hires For: Curiosity You Can't Teach

Goal on Short-Term Proxies, Not Retention

Set Goals Around Fail States, Not Just Averages

Hot Take· 2

Explainer· 3

Story· 4

Tool· 1

Takeaway· 5