Candidates describe Zalando Outlet data science interviews as heavy on walking through past projects. Applied Scientist candidates also report recruiter and manager conversations, algorithm questions and scheduling problems.
$103K
Avg. Base Comp
$125K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The one candidate who reported a Data Scientist interview describes a process built around past work rather than a technical grilling. The opening question was to tell them about a data science project you have worked on, and the conversations with several team members and stakeholders kept returning to that experience. The candidate said interviewers seemed most interested in how closely previous work matched the team's own problems, and that the process was framed around problem-solving, collaboration and cultural fit.
Two other candidates, who interviewed for Principal and Senior Applied Scientist roles, describe a similar start: a recruiter screen, then a conversation with a hiring manager about the role and possible projects. The senior candidate also reported a project walkthrough, some optimization and algorithmic questions that did not always match the job description, and an easy floodfill coding problem. Because those roles differ from Data Scientist, treat the coding material as possible rather than expected.
Prepare one or two projects in depth: the problem, your decisions, trade-offs, and how you worked with stakeholders. Be ready to connect that work to e-commerce or fashion retail problems. Also keep a light refresher on basic algorithms.
All three candidates reported no offer, and two described slow or unanswered follow-ups, with one reporting canceled interviews and a process of about two months. Ask about next steps at each stage and keep written confirmation of scheduling.
Synthesized from 3 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Zalando Outlet process.
Honestly, this didn't feel like a technical grilling to me. The question that opened things up was simply to tell them about a data science project I had worked on, and most of what followed was me walking through work I had already done. I spoke with several people from the team along with some stakeholders, and the conversations kept coming back to my past experience. Zalando's Berlin process was structured but friendly, and they framed it around innovation, problem-solving, collaboration, and cultural fit. Technical challenges were part of the process, but the emphasis was on behavioral questions about what I had actually built. What stuck with me was how much the interviewers seemed focused on how close my previous work was to what their team does. I didn't receive an offer. Looking back, I'd prepare to explain my projects in real depth rather than rely on general technical knowledge.
Prep tip from this candidate
Come ready to walk through one or two past data science projects in detail, covering the problem, your decisions, and how you worked with stakeholders, since the team focused heavily on that. Be prepared to explain how your past work relates to the team's day-to-day problems.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Zalando Outlet
Design a secure, real-time, scalable portfolio management system with notifications and caching.
| Question | |
|---|---|
| Explaining Linear Regression to Different Audiences | |
| Stakeholder Communication | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Upsell Transactions | |
| Merge Sorted Lists | |
| Compute Deviation | |
| Experiment Validity | |
| Download Facts | |
| Average Quantity | |
| Random SQL Sample | |
| Manager Team Sizes | |
| Month Over Month | |
| Button AB Test | |
| P-value to a Layman | |
| Flight Records | |
| Prime to N | |
| Paired Products | |
| Top 3 Users | |
| Swipe Precision |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation about motivation and fit for the role. Two of the three candidates describe this stage, so expect to explain why you want the position and how your background matches it. Asking about timelines and next steps here may help.
Candidates report a conversation with a hiring manager about the role and the kinds of projects the person might work on. One candidate felt it went well. Concrete examples of past projects tied to the team's work are likely to help.
The Data Scientist candidate reports speaking with several team members and stakeholders, starting with a request to describe a data science project. Questions kept returning to past experience and how close it was to the team's work. Behavioral and collaboration themes were emphasized.
One Senior Applied Scientist candidate reports deeper technical rounds with optimization and algorithmic questions, including an easy floodfill problem, plus a project walkthrough. This was a different role, so a Data Scientist loop may include less coding. Brush up on basic algorithms as a precaution.
