Deliveroo Data Scientist interview typically runs 5 rounds: recruiter screening, technical screen, SQL round, case study, behavioral interview. It usually takes several stages and is notably deep on behavioral follow-up.
$81K
Avg. Base Comp
$118K
Avg. Total Comp
5
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Deliveroo is less interested in flashy theory than in whether you can reason cleanly from messy business data. The SQL portion leans on fundamentals, but with enough structure to expose gaps: distinct counts, joins, and window functions show up in combinations that require careful filtering and aggregation, not just memorized syntax. We’ve also seen the questions framed around product and marketing metrics, which suggests the team wants people who can translate raw tables into decisions about growth, retention, and operational performance.
A recurring theme is that Deliveroo pushes beyond surface-level answers. In the case discussion, candidates were asked to unpack correlation versus causation and explain why one does not imply the other, which tells us they care about sound causal reasoning even when the prompt is open-ended. On the behavioral side, interviewers reportedly kept digging whenever answers stayed too high level. That pattern usually means they are listening for ownership, specificity, and the ability to describe how you actually moved a project forward when things got stuck. The strongest candidates here tend to be the ones who can stay precise under follow-up and connect their analysis back to a concrete business outcome.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Deliveroo process.
I interviewed online, had a recruiter screening call first, followed up by technical screen which was essentially covering my background and experiences. Then I had SQL round, which covered multiple questions, including simples counts, group by's, joins and window functions. Followed by case study, and behavioral interview. The behavioral one really dived deep, any time I remained relatively high level, they prompted to get more information out of me.
Questions asked: SQL ones was about simple distinct counts with where filters, third question you had to use window function to get cumulative counts and filter on dates using weekly aggregation. Case study was about correlation vs causation and reasoning why one does not lead to another - quite open ended. And behavioral were standard - tell me about a time when etc etc.
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Topics based on recent interview experiences.
Featured question at Deliveroo
What metrics would you use to determine the value of each marketing channel?
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Cumulative Sales By Product | |
| Lasso vs Ridge | |
| Causal Inference Without A/B | |
| Ranking Metrics | |
| Extra Delivery Pay | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Experiment Validity | |
| Top Three Salaries | |
| Upsell Transactions | |
| Month Over Month | |
| Button AB Test | |
| Merge Sorted Lists | |
| String Shift | |
| Compute Deviation | |
| Download Facts | |
| Average Quantity | |
| Longest Streak Users | |
| Random SQL Sample | |
| Average Order Value | |
| Manager Team Sizes | |
| Over-Budget Projects |
Synthesized from candidate reports. Individual experiences may vary.
An initial online call with a recruiter to discuss your background, experience, and overall fit for the Data Scientist role. This appears to be the first step before any technical evaluation.
A screening conversation focused on your background and past experience, rather than deep coding. The interviewer used this stage to understand your technical scope and relevant project work.
A dedicated SQL interview covering multiple question types, including simple distinct counts with filters, GROUP BY, joins, and window functions. One question involved using a window function for cumulative counts and filtering on dates with weekly aggregation.
An open-ended case discussion centered on correlation versus causation and why one does not imply the other. The interviewer probed your reasoning and how you would think through the problem.
A behavioral round with standard experience-based questions such as 'tell me about a time when.' The interviewer pushed for detailed answers and followed up when responses stayed too high level.
