Netflix Data Scientist interview questions

23 Data Scientists have shared notes from their Netflix interviews, with the questions they were asked in each round. That is 12% of the 191 Netflix interview notes on Screna AI.

23 Data Scientist interview notes

Netflix·Data Scientist·Technical Phone Screen

Jul 2026
Netflix data scientist interview that was basically one giant SQL problem split into three parts, all around ad frequency capping. The schema wasn't complicated but the logic got hairy fast, especially the rolling window stuff and the ROI calculation for impressions beyond cap.
  • Given a schema with users, campaigns, impressions, clicks, and conversions, write SQL to compute per-campaign frequency cap metrics at a specific timestamp: how many users are exactly at the 7-day rolling cap, how many are one below it, and what percentage of active users would exceed the cap if each received one more impression right now.
  • For every user-campaign pair seen in the last 7 days, compute the next eligible timestamp when serving another impression would not breach the frequency cap, because the oldest in-window impression has rolled out of the window.
  • For a specific 7-day window, compute per-campaign ROI for impressions that were served beyond the frequency cap: total revenue attributed to those over-cap impressions divided by the count of over-cap impressions. Handle users with no clicks or conversions, multiple conversions per click, and impressions straddling the campaign end date.

“This part looked manageable until I realized 'active in last 7 days' and 'at cap' are two different populations and I was conflating them.”

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Netflix·Data Scientist·Hiring Manager Screen

Jul 2026
Netflix hiring manager round for a Data Scientist role, focused almost entirely on a deep project walkthrough and how you handle criticism. Pretty intense for what I expected to be a casual conversation.
  • Walk me through a project where you owned the technical direction end to end. What trade-offs did you have to make, and how did you define whether it succeeded?
  • Tell me about a time you got negative feedback you weren't expecting. What was your reaction, and what actually changed because of it?

“This is the kind of question that sounds easy until you're actually in it and realize your 'impactful project' story has no real metrics attached.”

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Netflix·Data Scientist·Technical Phone Screen

Jul 2026
Netflix Data Scientist interview with a meaty ML system design question around fraud detection. One question, but it had a lot of surface area to cover and I definitely felt the pressure trying to hold all the pieces together at once.
  • Walk through the full ML workflow for building a fraud detection model on an online payments product, covering data collection through deployment and monitoring. How do you deal with severe class imbalance and concept drift?

“This question sprawls in every direction and I underestimated how much they wanted on the monitoring side.”

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Netflix·Data Scientist·Recruiter / HR Screen

Jul 2026
Netflix data scientist interview that was basically one big compensation negotiation exercise. You had to justify a number, defend trade-offs across different pay structures, and then actually roleplay pushing back on a lowball offer. More rigorous than I expected for something that sounds like an HR conversation.
  • What is your total compensation target for an all-cash offer at this level and location? Give a target range and a walk-away floor, backed by at least two market sources, and explain your assumptions about the role's scope.
  • Walk through the trade-offs between base salary, signing bonus, performance cash, title, scope, start date, and flexibility. How do you weigh these against each other?
  • How does your compensation expectation change if the role's scope increases or decreases by roughly 10%? What about if the benefits package is below market?
  • Roleplay a negotiation: the initial offer comes in 15% below your target. How do you respond? Include a conditional concession tied to something measurable.

“This tripped me up more than it should have.”

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Netflix·Data Scientist·Onsite - System Design / Architecture

Jun 2026
This was a deep ML system design round for a notifications modeling role. The question was essentially one giant end-to-end case study and they just kept pulling on threads for the full hour. Felt more like a technical dissertation defense than a standard interview.
  • How would you define training labels for a purchase propensity model when historical notifications have already influenced user behavior? How do you handle post-treatment leakage, multiple exposures, and the choice between intent-to-treat vs. treated-only labels?
  • What behavioral and contextual features would you build for this model, and how do you prevent target leakage, enforce time-consistent feature joins, and reduce training-serving skew?
  • How would you handle class imbalance in this purchase propensity model, and what calibration approach would you use? How do you monitor and recalibrate over time?
  • How would you evaluate this model offline? What metrics would you use, how would you construct your train/validation splits, and what slice analyses matter?
  • Without a randomized experiment in the historical logs, how would you estimate the incremental revenue impact of sending notifications to the top 20% of scored users?
  • How would you design the online experiment to validate this model in production? What are your guardrails, ramp criteria, traffic split logic, and how do you detect feedback loops or distribution shift?
  • How would you handle cold start for new users and new items on day zero, and how would you backfill training labels as you accumulate data?

“This is where I spent the most time and also where I stumbled most.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data scientist interview centered entirely on experiment design for a streaming feature with network effects. Pretty intense technically, lots of follow-ups, and I left genuinely unsure how I did.
  • How would you select primary and guardrail metrics for an A/B test on a new streaming feature?
  • What threats do network effects pose to a standard A/B test, and how would you address them?
  • Can you explain SUTVA and why it's relevant to this experiment?
  • Walk me through how you'd design and interpret a multivariate test on title artwork.
  • How would you measure heterogeneous treatment effects across different user segments?

“I went straight to engagement metrics like watch time and retention, which felt right, but I fumbled a bit when they pushed on what guardrails I'd use.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data scientist interview with a meaty system design question around fraud detection. Single question but it covered a lot of ground, from feature engineering all the way to post-deployment monitoring.
  • Walk through the full pipeline for training and deploying an XGBoost model to detect fraudulent subscription payments in real time. What evaluation metrics matter most, how do you deal with heavy class imbalance, and how would you catch model drift after the thing is live?

“This question is basically four questions stitched together and I felt it the whole time.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Interviewed at Netflix, got hit with a stats question that I was not fully prepared for. Short and focused, but it stuck with me.
  • What do you do when your test metric is not normally distributed?

“I fumbled around with 'use a non-parametric test' and mentioned the Mann-Whitney U, which was fine but felt incomplete.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data scientist interview focused on causal inference, specifically a scenario around propensity score matching for a UI experiment. Heavy on methodology and the kind of stuff you only know cold if you've actually run observational studies before.
  • Why is a standardized mean difference of 0.1 or below commonly used as the balance threshold in propensity score matching?
  • If logistic regression isn't the right fit for the propensity score model, what alternatives would you use and why?
  • After matching, how would you diagnose whether residual confounding is still a problem?
  • Walk through one method for estimating the variance of the treatment effect under propensity score matching.

“I knew the answer but fumbled the explanation a bit.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data science interview that basically asked me to dissect a project end-to-end, from the business problem down to stakeholder politics. One question, a lot of ground to cover.
  • Pick a skill from the job description and a project where you used it. Walk through the problem and constraints, the specific tools and techniques you used, a failure or edge case you hit, the measurable before/after impact, what you'd change in hindsight, and how you managed stakeholder risk and trade-offs.

“This is a lot to hold in your head at once and I fumbled the ordering.”

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