Updated by DoorDash candidates
Data Scientist Interview Experience
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.
- Recruiter screen
- Phone interview
- Final round
Interview tips
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.
Questions asked
Overview
After 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.
Question types asked
Specific questions asked
How do you measure if our current advertising product is working properly or not?
How would you think about segmentation here, like new users versus existing users?
How would you test whether a change is actually working?
This 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.
How would you measure success for adding in-store search to grocery on DoorDash?
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?
He 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.
How do you justify investing more resources into the new verticals product space?
How would you optimize the experience from the store page to checkout?
She 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.
Get full access with a membership, or share your experience to try it free.
