DoorDash Data Scientist Interview Experience
DoorDash · Staff
The SQL interviewer literally stopped me mid-explanation and said, “We’re not interested in your explanation. My job is just to make sure you get the right answer,” which honestly made the whole DoorDash process feel a lot like Meta.
Interview process
A recruiter reached out to me on LinkedIn, and the process started with a very basic recruiter screen that was mostly just a resume walkthrough and interest in DoorDash. Round one was a two-part screen: 30 minutes of SQL and a case on cold food arriving at customers. The SQL interviewer was extremely blunt, stating that he only cared about whether I got the right answer. After that, they team matched me before the final, which I hadn't really seen before, and I got matched to the new verticals area. The final loop was four interviews across two days with one business partner and three data scientists, and most of it was product analytics case work on metrics, segmentation, funnels, and A/B testing rather than deep theory. The most difficult part for me was the hiring manager round because she started at such a high level that it was hard to know where to anchor the answer.
Interview rounds · 3
- 1
Recruiter screen
BehavioralThis was just a simple recruiter screen after they reached out to me on LinkedIn. It was very high level, mostly resume walkthrough, tell me about yourself, and why DoorDash. There was nothing technical in it at all.
- Q1. Tell me about yourself.
- Q2. What interests you about DoorDash?
- 2
Phone screen
SQLCodingAnalyticalProduct StrategyStatistics & ExperimentationRound one was a single call split into two halves: a fast SQL screen and an open-ended case. The SQL part felt pretty intense and very result-focused. The case part was broad, so the main thing was to put structure on it and use DoorDash's three-sided marketplace lens.
Q1. Write SQL to answer progressively harder questions from the tables provided.
How they answeredThey gave me tables and four SQL questions that got harder as we went, including stuff that needed window functions, CTEs, and ranking. I treated it like a fast-paced Meta-style screen and tried to get through as much as possible in 30 minutes. The one thing that stood out was that I started explaining my logic, and the interviewer stopped me and basically said he wasn't interested in my explanation, just whether I got the right answer.
Follow-up questions- Explain the SQL code you wrote.
Q2. We have cold food that's arriving at the customer and they're unhappy. What would you do to solve it?
How they answeredI treated it like a classic product analytics case and structured it around the three-sided marketplace: customer, dasher, and merchant. I talked through where cold food could be coming from on each side, picked metrics, and then proposed testing a fix with an experiment. We got into high-level A/B testing and how I'd know the change was meaningful, like checking whether the difference was statistically significant. The interviewer also mentioned lower customer calls as one possible angle. The hard part was just keeping it structured because the prompt was so open-ended.
Follow-up questions- How would you break this down using DoorDash's three-sided marketplace?
- If we pick one of your solutions, how would you test it?
- How would you know if a difference in the metric is meaningful?
- Would lower customer calls matter here?
- 3
Final / onsite round
BehavioralCross-FunctionalProduct StrategyExecutionAnalyticalStatistics & ExperimentationAfter round one, they team matched me, which I thought was pretty unusual. My final loop was four interviews split across two days: one business partner round and three rounds with data scientists, including a manager from a different team doing a cross-check. The whole loop was much more product analytics than pure stats or theory, with a lot of A/B testing, segmentation, metrics, and funnel thinking.
- Q1. How do you work with non-technical stakeholders?
- Q2. Tell me about one of your favorite projects.
Q3. How do you measure if our current advertising product is working properly or not?
How they answeredThis was basically a product analytics question on ads. I framed it around defining what success means first and then not relying on one blended number, so I talked about segmentation like new users versus existing users. We also talked about experimentation again, so A/B testing came up as part of how I'd validate changes. Looking back, the extra prep I would've done is less deep stats and more studying the actual DoorDash user journeys so my answers were more grounded in their product.
Follow-up questions- How would you think about segmentation here, like new users versus existing users?
- How would you test whether a change is actually working?
Q4. How would you measure success for adding in-store search to grocery on DoorDash?
How they answeredHe gave me a more concrete prompt than the other DS rounds. Grocery already existed, but they wanted to add in-store search because otherwise you can end up scrolling forever to find something simple like milk. I first asked what goal we actually cared about and we aligned on improving user experience. From there I went through the usual flow of goal, primary metric, guardrails, funnel, and then how I'd A/B test the feature. This interviewer was much more forthcoming, so it was easier to navigate than the vaguer rounds.
Follow-up questions- Right now customers mainly use product category pages and curated carousels. How does that change your thinking?
- What's the goal of launching this?
- What primary metric and guardrail metrics would you use?
- How would you experiment on it?
Q5. How do you justify investing more resources into the new verticals product space?
How they answeredShe started way higher level than everyone else and asked how I'd justify putting more headcount and analytics support into new verticals. I answered in terms of return on the analysis and support we were providing and whether those verticals were actually generating profit, because if not, I'd question the investment. Then she pulled it back into a more standard product analytics problem around the funnel from store page to checkout. Honestly I think I did pretty poorly here because the opening question was so broad.
Follow-up questions- How would you optimize the experience from the store page to checkout?
Tips from the candidate
I'd tell a friend to know SQL cold and be ready to move fast, because at least on my screen, they did not care about hearing me narrate my logic. For the case rounds, put structure on everything or you'll ramble yourself into a hole. I would prep with the three-sided marketplace lens in mind and actually use the product with the customer, merchant, and dasher in mind. Also, be ready to go from problem to goal to metric to guardrails to experiment over and over again, because that's basically the pattern of the whole loop.
Company culture
My impression was that DoorDash is hiring data scientists in a very product analytics-heavy way. They seemed to use the same process across L5 and L6 and then decide level based on how you perform, not by giving a clearly different loop. The weirdest thing to me was team matching after round one instead of before or after the offer, although it sounds like you can still try to pivot teams later if the fit feels off. The recruiters were unusually transparent and even read out feedback from interviewers, which I almost never see. Interviewer style also seemed pretty level-dependent: the more senior people were much vaguer and seemed to want to see how I'd create structure myself, while others were more collaborative.