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.
$70K
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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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 | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Experiment Validity | |
| Subscription Overlap | |
| Top Three Salaries | |
| Upsell Transactions | |
| Monthly Customer Report | |
| Button AB Test | |
| Merge Sorted Lists | |
| String Shift | |
| Compute Deviation | |
| Download Facts | |
| Average Quantity | |
| Longest Streak Users | |
| Random SQL Sample | |
| Manager Team Sizes | |
| Average Order Value | |
| Weekly Aggregation | |
| Month Over Month |
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.