Glassdoor Data Scientist interview typically runs 4 rounds: phone screen, hiring manager interview, panel, and final round with the director. It usually takes a few weeks and is structured and efficient.
$111K
Avg. Base Comp
$180K
Avg. Total Comp
4
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Glassdoor is looking for data scientists who can move comfortably between clean SQL, product thinking, and clear explanation. The standout signal here is not raw difficulty so much as structured reasoning under practical constraints: one candidate described straightforward SQL that still demanded precision, alongside a product case with A/B testing where the real test was how well they could articulate experiment design and tradeoffs. That combination tells us Glassdoor cares about people who can connect analysis to decisions, not just produce an answer.
A recurring theme is the tone of the interviews themselves. Multiple candidates noted that interviewers were kind, professional, and conversational, which usually means the company is paying close attention to how you collaborate and communicate when the problem gets ambiguous. We’ve seen that the behavioral portion is less about rehearsed stories and more about whether you can explain your thinking naturally and credibly. In other words, clarity of reasoning matters as much as correctness.
The questions shared also point to a fairly standard but revealing bar: binary tree validation and SQL-style prompts show they still expect solid fundamentals, while the experiment and survey questions suggest a product analytics mindset. The non-obvious make-or-break factor is whether you can keep your answers grounded in the business context of a marketplace like Glassdoor — where measurement, user trust, and product tradeoffs are tightly linked.
Synthetized from 1 candidates 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 Glassdoor process.
I went through a pretty structured process for the Data Scientist role at Glassdoor, starting with a phone screen and then moving into a hiring manager interview, a panel, and a final round with the director. The version I experienced felt organized and efficient, and each interviewer came across as kind and professional, which made the whole thing a lot less stressful than I expected. The main topics were SQL, a case study, and behavioral questions, so it was a mix of hands-on technical work and discussion about how I think through product problems.
What stood out most was that the case portion wasn’t just abstract theory — I also had to talk through a product case with A/B testing, so it helped to be comfortable explaining experiment design and tradeoffs clearly. The SQL questions were straightforward but still required being precise, and the behavioral part was very conversational. Overall, I had a positive impression of the team and the process, and I ended up receiving an offer. My main takeaway is to be ready for a fairly standard interview loop, but don’t underestimate the need to communicate your reasoning clearly in both the case and behavioral rounds.
Prep tip from this candidate
Be ready to discuss a product case with A/B testing in detail, not just answer SQL questions. I’d also practice explaining your reasoning clearly in behavioral rounds, since that seemed to matter alongside the technical work.
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 Glassdoor
Write a query to get the percentage of users that have never liked or commented rounded to two decimal places
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Forecasting New Year Revenue | |
| Survey Response Randomness | |
| Binary Tree Validation | |
| Testing Constraints | |
| ETA Experiment | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Experiment Validity | |
| Comments Histogram | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Merge Sorted Lists | |
| Customer Orders | |
| Employee Salaries | |
| Closest SAT Scores | |
| Download Facts | |
| First to Six | |
| Subscription Overlap | |
| Button AB Test | |
| Upsell Transactions | |
| 500 Cards | |
| Monthly Customer Report | |
| First Touch Attribution | |
| User Experience Percentage | |
| Weighted Keys | |
| Random SQL Sample | |
| Compute Deviation | |
| Raining in Seattle |
Synthesized from candidate reports. Individual experiences may vary.
The process starts with an initial phone screen to assess baseline fit and technical readiness for the Data Scientist role. Based on the experience shared, this stage likely includes a mix of behavioral questions and early technical screening around SQL and product thinking.
Next is a hiring manager interview focused on deeper discussion of your background, how you approach product problems, and how you communicate your reasoning. The candidate experience suggests this round is conversational and may include behavioral questions alongside a case-style discussion.
The panel round covers the core technical and case components of the loop. Topics included SQL, a product case, and an A/B testing discussion, with emphasis on explaining experiment design, tradeoffs, and precise analytical thinking.
The last stage is a final interview with the director, serving as a wrap-up evaluation before the decision. This round likely revisits technical depth, case reasoning, and overall fit, and in the reported experience it led to an offer.