Interview guides · Disney Entertainment & Sports

Disney Entertainment & Sports Data Scientist Interview Questions & Guide 2026

Modeling drills for a Disney Entertainment & Sports data scientist interview: baselines, regression interpretation, class imbalance, model defense.

The 60-second brief

A Data Scientist at Disney Entertainment & Sports sits at the intersection of world-class storytelling and cutting-edge technology. In this role, you are responsible for transforming massive, complex datasets into actionable insights that power the platforms millions of fans engage with daily. Whether you are optimizing content recommendation engines, analyzing streaming performance, or shaping marketing strategies, your work directly influences the digital experiences of a global audience.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Diagnose rebuffering by device, network and POP
Correct discovery slates for position bias
Define a qualified stream and defend it

Overview

This position is critical because Disney Entertainment & Sports operates at a scale that few organizations can match. You will navigate high-stakes environments where your models and analyses must not only be technically rigorous but also deeply aligned with business objectives. You will collaborate with cross-functional teams, including product managers, engineers, and stakeholders across various media verticals, requiring you to translate technical complexity into clear, strategic narratives.

The Disney Entertainment & Sports interview process

Disney Entertainment & Sports candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Assessments

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Management Interviews

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Leadership Interviews

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Where candidates lose points

Comparing consumption week over week across the release calendar and the rights calendar.

A major release, a season drop or a live event produces a spike that dwarfs almost any treatment effect, and the effect is not confined to the new title because it pulls attention from everything else in the same window. Separately, licensed content leaves the catalogue when its window expires, so consumption falls with no product change and the drop is attributed to whatever shipped that week. Both need to be handled by an explicit control: a comparison period chosen for calendar equivalence, a covariate for scheduled releases, or a pre-registered rule for excluding a window, decided before the numbers are seen.

Reading recommendation quality from click-through or play-through on logged slates.

Slate position drives interaction independently of relevance, and the ranker that produced the log also decided what got exposure, so logged data is confounded by the policy you are trying to evaluate. Inverse-propensity estimators fix this only when the logging propensities are known and every item had a non-zero chance of being shown; without exploration slots that positivity condition fails silently and the estimate is confidently wrong. The visible symptom is a feedback loop: offline metrics improve, consumption concentrates further into the head of the catalogue, and the online test does not reproduce the offline gain.

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Answering a product-sense question with a list of features

Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.

Questions & practice

Can you explain the difference between these two machine learning models in a production environment?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

Can you explain the trade-offs you made when choosing this particular algorithm?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

What was your specific contribution to this model, and how did you measure its success?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.

Follow-up

How would you choose the decision threshold, and who owns that choice?
Where could label leakage enter this setup?

Permutation test for hours per account across two ranker arms

Medium · permutation-test · heavy-tails · experiment-analysis

arm_hours holds one row per account: account_id, arm in {control, treatment}, qualified_hours over a seven-day window. Roughly 40 thousand accounts per arm, about 38 percent of them at zero hours, and the non-zero tail is long. Without calling a library test function, write a permutation test on the difference in mean hours with 10,000 relabellings. Then run it as two parts: the difference in the share of accounts with any hours, and the difference in mean hours among accounts with hours. Report all three and say which belongs in the readout.

Approach

Shuffle the labels, not the data. Draw a permutation of the arm indicator over accounts, which is the unit that was randomised, and hold the hours vector fixed.
Make each replication O(n): precompute the grand sum and the arm sizes, so a shuffled difference is the treated subset sum over n_t minus (grand sum minus that subset sum) over n_c. Ten thousand replications then take seconds instead of a minute.
Use the two-sided p-value (1 + count of permuted absolute differences at or above the observed) divided by (B + 1). The plus one is not cosmetic: it makes the p-value valid rather than optimistic, and it means the smallest reportable value here is 1/10001, not zero.
For the two-part version, run the same machinery on the 0/1 indicator for the rate, then on the non-zero subset for the conditional mean, and say plainly that conditioning on a post-treatment outcome breaks the randomisation, so the conditional arm is descriptive rather than causal.
Report the rate test and the overall mean test as the result, with the conditional mean as colour, and give the effect size in hours beside each p-value, because at 80 thousand accounts almost anything is detectable.

Worked solution (30 min)

Pull hours into a float array and arm into a boolean array; record n_t, n_c, the grand sum and the observed difference of means.
Loop B = 10,000 times, taking np.random.permutation(n)[:n_t] as the treated index set and accumulating hours[idx].sum().
Convert each shuffled treated sum into a difference of means, compare its absolute value against the observed, count, and apply the plus-one form.
Repeat with hours replaced by (hours > 0).astype(float) for the rate test.
Subset to hours above zero, re-run for the conditional mean, and assemble a three-row result carrying effect, p-value and the unit each is defined on.

Expected result: Three rows: the mean-hours difference with a permutation p-value bounded below by 1/10001; the streaming-rate difference in percentage points with its own p-value; and the conditional mean difference among non-zero accounts, labelled descriptive because it conditions on a post-treatment outcome.

Verify it yourself

The permutation distribution of the mean difference is centred on zero within its own Monte Carlo error. A centre away from zero means the labels were not actually being reshuffled between replications.
The permutation p-value on the rate agrees to two decimal places with a two-proportion z-test, which is a fair cross-check because a 0/1 indicator is not heavy-tailed.
Doubling B leaves the p-value stable to within roughly 1 over sqrt(B). A p-value that moves a lot between runs is not yet resolved.
Multiplying every hours value in both arms by 60 leaves all three p-values identical under the same seed and scales only the effect sizes, since a permutation test is invariant to a common positive rescaling.

Follow-up

The permutation p-value on the mean is 0.03 and the rate test is flat. What is the most likely explanation, and does it change the decision?
How would CUPED on pre-period hours change your power here, and what would disqualify a covariate?
Accounts are households. Does that affect the validity of this test, or only its interpretation?

How would you structure a SQL query to extract user engagement metrics over a specific time window?

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.
Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

How does the query change if the join becomes one-to-many?
How would you verify this result without re-running the same query?

Can you perform a live coding exercise to solve this data manipulation problem?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

What breaks if events arrive late or out of order?
How does the query change if the join becomes one-to-many?

Weekly engaged accounts from qualified streams on distinct days

Easy · aggregation · active accounts · qualification threshold

fct_stream has one row per playback with account_id, started_at (UTC), played_seconds, is_qualified (true when played_seconds >= 30) and event_date, a date partition key derived from started_at. dim_account has account_id and is_test_account. Write one query returning, for the seven days ending 2026-03-31 inclusive, the count of weekly engaged accounts: distinct non-test accounts with at least one qualified stream on two or more distinct UTC dates inside that window. Return a single row. Count at the account level, not the profile level.

Approach

Filter fct_stream on event_date BETWEEN '2026-03-25' AND '2026-03-31' and is_qualified = true. Filtering on started_at alone leaves the engine no partition to prune, so it reads every day the table holds; event_date is the partition key and is what belongs in the predicate.
Drop test accounts before the date count, with a join or semi-join to dim_account on is_test_account = false. Excluding them afterwards is too late, because a test account that streamed on two days has already been counted as engaged.
GROUP BY account_id and take COUNT(DISTINCT event_date) AS active_days. The distinct is the entire metric: an account with forty qualified streams inside one calendar day has one active day.
Filter HAVING COUNT(DISTINCT event_date) >= 2, then wrap in an outer COUNT(*). The group-by has already made account_id unique, so an outer COUNT(DISTINCT account_id) adds a sort for nothing.
Sanity-bound the answer before reporting it: it must be no larger than the count of distinct non-test accounts with any qualified stream in the window.

Worked solution (20 min)

Compute the ceiling first: COUNT(DISTINCT account_id) over non-test accounts with any qualified stream in the window.
Build a CTE of (account_id, COUNT(DISTINCT event_date) AS active_days) over the filtered rows.
Select COUNT(*) from that CTE where active_days >= 2.
Re-run the final filter at >= 1 and >= 3 to see the shape of the distribution and confirm the metric is not being carried by accounts sitting exactly on the boundary.

Expected result: A single integer. It must satisfy count(>=3) <= count(>=2) <= count(>=1), where count(>=1) is the ceiling computed in step one.

Verify it yourself

The three thresholds are monotone: raising the active-days requirement never raises the count.
Removing the is_test_account filter raises the count or leaves it unchanged, never lowers it.
No account_id appears more than once in the pre-aggregate CTE.

Follow-up

If you counted distinct dates per profile and then rolled up to the account, would the number rise or fall, and which unit does the metric actually want?
An account plays one 40-second track at 23:58 and another at 00:03. That is two distinct UTC dates and therefore engaged. Is that the intent, and what would you change if not?
Offline plays upload in bulk several days late with started_at backdated. Which part of this query silently changes when they land?

How did you handle data quality issues or missing values in this specific project?

Medium

Approach

State your assumptions explicitly before working the problem.
Work from the decision backwards to the evidence you would need.
Say what you would check first and why it is the highest-information step.

Follow-up

How would you know your answer was wrong?
What assumption would you test first?

Size a ranker test on heavy-tailed weekly listening hours

Easy · power · minimum detectable effect · heavy tails

You are sizing a two-arm, account-randomised test of a new home-row ranker. The primary metric is qualified hours per active account-week from fct_stream (sum(played_seconds)/3600 where is_qualified = true). Over the last four complete ISO weeks the per-account weekly figure has mean 4.6 hours and standard deviation 9.8 hours, median 1.9 hours, and the top 1 percent of accounts hold 14 percent of hours. Eight million accounts are eligible, split evenly, over a 14-day window. Give the minimum detectable effect at 80 percent power and alpha 0.05 two-sided, and say what you would change before trusting it.

Approach

Fix the unit of analysis to the randomisation unit: one row per account holding its 14-day total, not one row per account-week and certainly not one per stream, because two weeks from the same account are positively correlated and do not count as two independent observations.
Apply the two-sample formula MDE = (z_0.975 + z_0.80) * sigma * sqrt(2/n), with z_0.975 + z_0.80 = 1.960 + 0.842 = 2.802 and n the accounts per arm, then divide by the control mean for a relative figure.
Check what the skew actually threatens. At n in the millions the central limit approximation for the sample mean is comfortable, so skew does not break the test; it makes the variance hostage to a few hundred accounts and to whatever released inside the window.
Pre-register a variance control and commit to reporting both readings: hours capped at the 99th percentile, and a two-part decomposition into the share of accounts with any qualified stream times conditional hours among those who had one.
Compare the MDE against the smallest effect worth shipping. If the MDE lands far below that, power is not the binding constraint and the design effort belongs on calendar confounding and on the active-account denominator instead.

Worked solution (20 min)

Collapse fct_stream to one row per account_id over the 14-day window: total qualified played_seconds divided by 3600. Record the count of assigned accounts with zero qualified hours separately, because the active-account denominator is itself treatment-sensitive.
Set n = 4,000,000 per arm and sigma = 9.8, then compute sqrt(2/n) = sqrt(5.0e-7) = 7.071e-4.
MDE = 2.802 * 9.8 * 7.071e-4 = 0.0194 qualified hours per account-week, which is about 70 seconds.
Convert to relative: 0.0194 / 4.6 = 0.42 percent.
Repeat steps 2 to 4 with sigma taken from the 99th-percentile-capped series and put both MDEs in the design doc, with the cap fixed before any data is unblinded.

Expected result: MDE approximately 0.0194 qualified hours per account-week, or 0.42 percent relative, at 80 percent power and alpha 0.05 two-sided. The correct conclusion is that the test is over-powered for any effect the team would act on, so the real design risk is bias from the release calendar and from a shifting active-account denominator, not power.

Verify it yourself

Halving n to 2,000,000 per arm must multiply the MDE by exactly sqrt(2) = 1.414, giving 0.0275 hours.
The standard deviation of the 14-day total must be less than sqrt(2) times the weekly standard deviation, because weeks within an account are positively correlated; if it is not, the collapse is wrong.
An A/A split should put 95 percent of observed differences inside plus or minus 1.96 * sigma * sqrt(2/n) = 0.0136 hours, which is 70 percent of the MDE. A/A differences that routinely exceed the MDE mean the variance estimate or the independence assumption is wrong.

Follow-up

Capping at the 99th percentile cuts the standard deviation to 5.1 hours. What does that do to the MDE, and what have you given up if the true effect lives in the heavy accounts?
A season drop lands in the second week of the window. Does that change the variance, the bias, or both?
The team wants the same MDE in seven days instead of fourteen. What are you spending to get it?

Trial conversion fell for cohorts that have not matured

Hard · cohort-maturity · mix-shift · acquisition-quality

Trial-to-paid conversion — cohort accounts with a period_index = 1 row in fct_subscription_period whose payment_status is 'paid' or 'retried_paid', over cohort size — is reported weekly by trial_start_ts cohort and has fallen from 38% to 29% across five weeks. Trial length is 14 days. You have dim_account (trial_start_ts, signup_channel, signup_platform, signup_country, is_test_account, first_paid_ts) and fct_subscription_period (account_id, period_index, payment_status, dunning_attempts, period_start_ts). Establish whether conversion fell and for whom. Deliverable: an age-aligned series plus a composition-adjusted estimate of the real change.

Approach

Align cohort age first. No period_index = 1 row can exist before the trial ends, so cumulative conversion is identically zero through day 13 and the whole curve lives between day 14 and the close of the dunning horizon — roughly 14 plus 21 days. The three most recent cohorts sit inside that window and cannot read anything but low. Plot each cohort's cumulative conversion against days since trial_start_ts and read every cohort at one common age.
Quantify how much maturity alone explains. Take the conversion-by-age curve from fully matured cohorts, read off the fraction of final conversion it has reached at each age, and multiply that fraction by a mature cohort's final rate to predict what a young cohort should be showing today. Whatever the prediction reproduces is not a regression and should never have been plotted as one.
Decompose the residual across signup_channel, signup_platform and signup_country. Paid social and carrier bundles convert at very different rates from organic, so a weight shift between them moves the blended figure with every channel's own rate flat.
Make the composition adjustment explicit rather than rhetorical: reweight recent cohorts to the channel-by-country mix of a fixed reference cohort and recompute, then publish the reweighted series beside the raw one and name which channels moved and by how much.
Only then look for a within-channel product cause, and rule out the instrumentation confounders first: is_test_account leakage, attribution opt-out inflating the organic bucket at a platform-specific rate, and app-store payment_status rows arriving later than card-direct ones.
Deliver a number with an interval rather than a direction: the age-aligned, composition-adjusted change, with the cohorts too young to read named explicitly as excluded.

Follow-up

Attribution opt-out shifts accounts into the unattributed bucket at a platform-specific rate. How does that corrupt your reweighting, and what do you do about it?
If the residual concentrates in one paid channel, what do you need from marketing before calling it acquisition quality rather than a product regression?
How many mature cohorts do you need before the composition-adjusted change clears the noise floor?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

What the behavioural round is actually scoring

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

How did you communicate the results of this analysis to non-technical stakeholders?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.

Follow-up

What would you do differently if you ran that project again?
What did you decide not to do, and why?

Why are you interested in joining Disney Entertainment & Sports specifically?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Pick a story where you drove the decision, not one where you observed it.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What would you do differently if you ran that project again?
What did you decide not to do, and why?

Disagree with a product manager about a completion metric

Medium · disagreement · metric design · influence

A product manager proposes making duration-normalised completion rate the team's primary metric for the quarter, and wants a plain global unweighted version "so it's simple enough to put on a wall." You expect the unweighted version to move several points on catalogue mix alone, with no change in how satisfying anything was. You have one meeting, they own the roadmap, and you will work with them for years. Deliverable: the argument, the evidence you bring, and the fallback you accept if they still want the simple version.

Approach

The probe is whether you can lose the decision without damaging either the metric or the relationship. Bring the failure already reproduced: recompute the unweighted global rate over the last two quarters and point at the weeks it moved several points where the only input that changed was which content people played.
Decompose the variance instead of objecting. Attribute the movement in the unweighted rate to between-cell mix — (content_type, duration decile) — versus within-cell movement. A decomposition is an argument; an assertion that mix matters is a preference.
Concede the real cost honestly: the mix-weighted version is harder to explain and harder to recompute. Bring the mitigation rather than dismissing the objection — weights fixed from a stated reference month, published once, so anyone can reproduce the number without redoing the weighting.
Offer the fallback deliberately and in advance: ship both, with the unweighted rate labelled a diagnostic and the weighted one as the decision metric, and pre-agree the one observation that would mean the simple version has misled the team.
Leave the decision with them, with the consequence written down before it happens. That is what makes a later correction a shared prediction coming true rather than a retrospective argument about who was right.

Follow-up

They ship the simple version and it moves four points during a heavy release week. How do you raise it without saying you told them so?
Is there a case where the unweighted rate is genuinely the right primary metric?
Suppose you are wrong and the mix effect turns out to be small. What would you change about how you argued this?