Notion Labs Data Scientist interview typically runs 5 rounds: recruiter screen, technical screen, virtual onsite, hiring manager behavioral, and manager conversations. It usually moves fast, often within weeks, and is highly structured and product-oriented.
$161K
Avg. Base Comp
$200K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
We’ve seen Notion’s data science interviews reward candidates who can turn technical work into a clear product story. In the experience we reviewed, the hiring manager immediately centered the conversation on a model the candidate had built, and the biggest failure point wasn’t a weak algorithm — it was a live demo that broke mid-presentation. That’s a strong signal that presentation quality and narrative control matter here almost as much as the underlying analysis. If your work can’t be explained crisply, it’s easy to lose momentum.
A recurring theme is how much Notion cares about product judgment. Multiple candidates reported questions about the key metrics for Notion and described the onsite as broader and more product-oriented than expected, with system design framed around a user-facing application and conversations that probed stakeholder management and cross-functional communication. That tells us the team is looking for people who can connect data decisions to the product experience, not just produce accurate outputs.
We also see a structured, high-signal process that seems to test whether candidates can operate like owners. The mix of SQL, pandas, coding, AI-assisted coding, stats, and manager conversations suggests they want breadth, but the differentiator is how well you can justify tradeoffs and explain impact. Our candidates report that the process feels demanding but fair; the ones who do best are the ones whose past work is easy to follow, easy to trust, and clearly tied to user value.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Notion labs process.
I got a response from the hiring manager almost immediately after applying online, which set the tone for how fast the process moved. The first conversation was only about 20 minutes and was very focused on a model I had built. It felt pretty scripted, and the interviewer mostly wanted me to walk through my resume and then demo the work. That part was actually the hardest for me because my model crashed in the middle of the presentation, and once that happened the interview was basically over. It was a good reminder that for this role, being able to present your work cleanly mattered just as much as the technical substance.
After that, the process I went through included a technical screen with SQL and pandas, then a virtual onsite. The onsite was broader and more product-oriented than I expected: there was coding that was similar to the screen but more complex, an AI-enabled coding round, a system design interview where I had to design a user-facing application end to end, and conversations with managers. There was also a behavioral round with the hiring manager that dug into how I work with stakeholders, plus stats, product sense, cross-functional communication, and career history. One of the questions I remember most clearly was about what the key metrics for Notion should be, which made it obvious they cared a lot about product thinking, not just analysis. The team was nice throughout, but the process was demanding and pretty structured. I ended up not getting an offer, and my main takeaway is to prepare both the technical fundamentals and a polished story around your past work, especially if you need to demo a model live.
Prep tip from this candidate
Be ready to present a model you’ve built smoothly and defensively, since the first screen centered on a live demo. Also practice SQL and pandas, then go beyond that into product metrics, stakeholder communication, and end-to-end system design for a user-facing application.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Notion labs
What’s the expected churn rate in March for all customers that bought the product since January 1st?
| Question | |
|---|---|
| WhatsApp Metrics | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Subscription Overlap | |
| First to Six | |
| Upsell Transactions | |
| Monthly Customer Report | |
| Experiment Validity | |
| First Touch Attribution | |
| Download Facts | |
| Employee Salaries (ETL Error) | |
| Random SQL Sample | |
| Compute Deviation | |
| Button AB Test | |
| Raining in Seattle | |
| Average Quantity | |
| String Shift | |
| 500 Cards | |
| Last Transaction | |
| Minimum Change | |
| Top 3 Users | |
| Manager Team Sizes | |
| Network Experiment Design |
Synthesized from candidate reports. Individual experiences may vary.
The process started very quickly after applying online, with an initial conversation from the hiring manager. This stage was focused on walking through the candidate’s resume and discussing a model they had built, including a live demo of the work.
Next was a technical screen covering SQL and pandas. The interview tested core data manipulation and analysis skills before moving into the more extensive onsite rounds.
The onsite was a structured virtual loop with several interviews. It included coding that was more complex than the screen, an AI-enabled coding round, a system design interview for a user-facing application, and conversations with managers.
Additional onsite conversations focused on behavioral fit, stakeholder management, product sense, stats, cross-functional communication, and career history. One discussion centered on the key metrics for Notion, showing that product thinking was an important part of the evaluation.