Tubi

Mid-sized

Tubi is a free, ad-supported video streaming service offering a large library of movies and TV shows. Owned by Fox Corporation, it is known for providing on-demand entertainment at no cost to viewers, monetizing through advertising.

5 interview notes · updated Jul 2026

Tubi·Data Scientist·Technical Phone Screen

Jun 2026

SQL-heavy technical screen for a data scientist role at Tubi. One meaty question about computing user retention metrics from raw event logs, with a bunch of edge cases baked in to see if you'd actually think through the definitions.

Given a raw event log table, write SQL using CTEs to compute new users, retained users, churned users, and net users for a specific target date, broken down by device platform. Definitions are precise: new means first-ever event on that date, retained means active both the day before and on the target date, churned means active the day before but not on the target date, and net equals new plus retained minus churn.

“The definitions sound clean until you start writing the CTEs and realize you need to be careful about what 'first-ever event' means across all of history, not just the two days in scope.”

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Tubi·Data Scientist·Technical Phone Screen

Jun 2026

Tubi data scientist interview with a single very involved measurement question about Super Bowl ad effectiveness. The whole thing was basically one giant case study broken into seven sub-parts, which felt more like a take-home exam than a conversation.

Your app ran a 30-second national Super Bowl ad. Design a full measurement plan to estimate the incremental impact on installs and revenue, covering KPIs and measurement windows, identification strategies without a clean control group, a difference-in-differences setup and its assumptions, confounders, uncertainty estimation, falsification checks, and a power calculation for detecting a 4% lift in daily installs.

“This was one question with seven sub-parts and I genuinely did not expect the power calculation at the end.”

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Tubi·Data Scientist·Technical Phone Screen

May 2026

Tubi data scientist interview with a meaty causal inference question that honestly felt more like a take-home problem than a live screen. The question was well-constructed but I spent way too long on the bias directions before getting to the fixes.

Users can opt into seeing a discount banner (treatment), and treated users show higher conversion. Define selection bias, identify at least three mechanisms causing it and the direction of each bias, propose two methods that produce less biased causal estimates along with their assumptions and how you'd test those assumptions, and walk through a small numerical example where the naive estimate is wrong but the corrected method gets you closer to the truth.

“This is the kind of question that sounds manageable until you're actually in it.”

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Tubi·Data Scientist·Technical Phone Screen

May 2026

Tubi data science interview threw a deep stats question at me that was way more nuanced than I expected. The whole thing was basically a stress test on whether you actually understand p-values or just memorize the definition.

Explain to a non-technical product manager what a p-value of 0.03 from a two-sided A/B test actually means, and just as importantly, what it does NOT mean. Use a concrete scenario: baseline conversion 5.0%, observed lift of +0.4 percentage points, pooled standard error 0.18 pp. Cover the null hypothesis setup and the 'extremeness under the null' interpretation, why p is not the probability that the null is true, how p-values relate to confidence intervals and statistical power, the dangers of peeking at results early, and finish with both a plain-English explanation you'd give the PM and a Bayesian reframe of the same result.

“This question is a monster.”

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Tubi·Data Scientist·Technical Phone Screen

Apr 2026

Tubi data scientist interview with a pretty deep A/B testing question that covers sample size derivation, sensitivity analysis across like six different scenarios, and overdispersed count data. Felt more like a stats exam than a conversation.

For an A/B test with baseline conversion of 8% and a relative MDE of 8% (so +0.64 percentage points), derive and compute the required per-arm sample size using a normal approximation. Show your z-score terms.
Holding everything else constant from the baseline setup, how does required sample size change qualitatively and quantitatively when: the MDE is halved, alpha tightens to 0.01, power increases to 90%, allocation shifts to 75/25, there is user-level clustering with ICC=0.02 and average cluster size 5, and you add group-sequential monitoring with two equally spaced looks using O'Brien-Fleming boundaries?
If the metric you're testing is overdispersed count data following a negative binomial distribution, how would you re-estimate the required sample size?

“The derivation itself isn't bad if you've done it before.”

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