A Data Scientist at Prime Video sits at the intersection of massive-scale entertainment data and customer-obsessed innovation. You are responsible for transforming raw signals—such as viewership patterns, engagement metrics, and content performance—into actionable insights that influence the future of streaming. Whether you are optimizing recommendation algorithms, forecasting demand for new content, or measuring the impact of UI changes, your work directly shapes the experience of millions of global users.
This role is critical because Prime Video operates in an environment of extreme scale and complexity. You will not just be building models; you will be solving high-stakes business problems where technical precision meets strategic decision-making. You will collaborate closely with product managers, software engineers, and business leaders to ensure that data is not just an output, but a primary driver of the service's growth and competitive edge.
Candidates should emphasize their ability to connect technical machine learning outputs to tangible business outcomes, as this is the primary indicator of seniority and impact at Prime Video.
Initial Technical Screen
reportedThis 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
Technical Deep-Dives
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
Behavioral Rounds
reportedBehavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.
What to demonstrate
- Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
- Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
- Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier
How to prepare
- For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
- Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
- Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
Meet Team Members
reportedBecause 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.
Leadership Interview
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
PracHub editorial advice for the preparation topics above.
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.
Treating the account as the person, or the profile as the person.
A household account carries several people, profiles frequently are not switched, and shared-screen, car and speaker playback often lands on a default profile with no user behind it. Personalisation trained on a profile therefore learns a mixture, retention regressions attribute one member's behaviour to another, and a per-account taste statistic describes a household composite. The practical consequence is that apparent personalisation wins can be device or context effects, so any identity-level claim needs a stated unit and an acknowledgement of what that unit actually aggregates.
Reporting a mean for a heavy-tailed metric without saying what it hides
For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.
Over-explaining the method and under-explaining the implication
Lead with the answer and what you would do about it, then give the approach when asked. Roughly one sentence of method per three of implication is the right ratio for a stakeholder-facing answer; the interviewer already knows what a regression is.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the bias-variance tradeoff and how you address it in your proj…
Explain the bias-variance tradeoff and how you address it in your projects.
Approach
- Write down the assumption the method needs before you use the method.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- Which assumption here is most likely to be violated in practice?
- What sample size would you need to detect an effect half this size?
How would you handle imbalanced datasets in a churn prediction model?
How would you handle imbalanced datasets in a churn prediction model?
Approach
- Set a baseline first, so any model has something honest to beat.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- 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?
- Where could label leakage enter this setup?
Can you explain the mechanics of a specific algorithm you have used in…
Can you explain the mechanics of a specific algorithm you have used in production?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- 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
- 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
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.
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 do you ensure your code is modular, readable, and maintainable for…
How do you ensure your code is modular, readable, and maintainable for a team environment?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Given a list of integers, how would you find the k-th largest element …
Given a list of integers, how would you find the k-th largest element efficiently?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Explain the difference between a `LEFT JOIN` and an `INNER JOIN` in th…
Explain the difference between a LEFT JOIN and an INNER JOIN in the context of large-scale data tables.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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?
How would you use window functions to identify the top three most-watc…
How would you use window functions to identify the top three most-watched titles per user category?
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.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Sessionise playback into listening sessions with an idle gap
fct_stream has profile_id, stream_id, content_version_id, started_at, ended_at (nullable when the client stopped reporting) and played_seconds. Collapse consecutive streams by the same profile into listening sessions: a new session begins when a stream starts more than 30 minutes after the latest end reached by any of that profile's earlier streams. One profile can play on two devices at once, so streams overlap and 'the previous stream' is not well defined — the idle gap is measured against the running maximum end, not against the row immediately before. Treat a NULL ended_at as started_at plus played_seconds. Return one row per session with profile_id, session_start, session_end, stream_count, distinct content_version_id count and total played_seconds. Use window functions; a self-join on fct_stream is not an acceptable answer.
Approach
- In a first CTE compute effective_end = COALESCE(ended_at, started_at + played_seconds * INTERVAL '1 second'). This both fills the abandoned-client case and bounds it, since played_seconds is advancing playback only and cannot run past the true end.
- Take prev_max_end = MAX(effective_end) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING), and flag is_new_session = (prev_max_end IS NULL OR started_at > prev_max_end + INTERVAL '30 minutes'). LAG(effective_end) is the reflex here and it is wrong under concurrent devices: with A 10:00-12:00, B 10:05-10:10 and C starting 10:45, LAG hands C the end of B at 10:10, so C opens a new session 35 minutes into a session still running until 12:00. The NULL branch is not defensive noise either: the first row of every profile has no preceding frame and would otherwise fall outside any session.
- Number the sessions with SUM(is_new_session::int) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS UNBOUNDED PRECEDING). A running count of boundaries is the gaps-and-islands device. Specify ROWS rather than letting the default RANGE frame apply, because RANGE lumps peer rows that share a started_at into one frame position — the same reason the ORDER BY carries stream_id as a tie-break.
- Group by (profile_id, session_number) and aggregate: MIN(started_at), MAX(effective_end), COUNT(*), COUNT(DISTINCT content_version_id), SUM(played_seconds).
- The running maximum makes the sessions disjoint in wall-clock time, but it does not make SUM(played_seconds) an elapsed-time figure: two devices playing at once contribute overlapping seconds, so the total can exceed session_end minus session_start. Report it as consumption and say which of the two the requester asked for.
Worked solution 30 min
- Materialise the effective_end CTE and check for rows where effective_end < started_at, which means negative played_seconds or a clock-skewed ended_at and must be quarantined before anything else.
- Add the running MAX and the boundary flag; verify that exactly one row per profile has prev_max_end IS NULL and that it is flagged as a session start.
- Add the running SUM to number sessions; confirm the numbers are dense and begin at 1 within each profile.
- Aggregate and reconcile: SUM(stream_count) across all sessions must equal the input row count.
- Run the overlap fixture — one profile with A 10:00-12:00, B 10:05-10:10 and C at 10:45 — and confirm it collapses to one session. With LAG(effective_end) in place of the running MAX it splits into two, and the second session's start, 10:45, sits inside the first session's span.
Follow-up
- Two devices on one profile play simultaneously. Is that one session or two, and what changes in the query for each answer?
- Re-run at a 10-minute and a 60-minute idle gap. How far does session count move, and which number do you publish and defend?
- Can SUM(played_seconds) for a session ever exceed session_end minus session_start, and what does it mean when it does?
Cut variance with CUPED and pre-period stratification
A 14-day account-randomised test on qualified hours per account-week has a 0.90 percent MDE, and the team believes the true effect is near 0.40 percent. You hold eight weeks of prior fct_stream data. Using each account's played_seconds over the 28 days before assignment as covariate X and the in-test metric as Y, with corr(X, Y) = 0.62 measured in the control arm, apply CUPED and pre-assignment stratification. Give theta, the variance reduction, the resulting MDE, your handling of accounts with no pre-period, and the check that the estimate is still unbiased.
Approach
- Define the adjusted outcome Y_cuped = Y - theta * (X - Xbar), with Xbar pooled across arms and theta = Cov(X, Y) / Var(X). The estimator is unbiased for any theta; this theta is the one that minimises variance.
- Translate correlation into reduction: Var(Y_cuped) = Var(Y) * (1 - rho^2). The MDE scales with the standard deviation, so it falls by a factor of sqrt(1 - rho^2), not by 1 - rho^2.
- Insist the covariate is strictly pre-assignment. Any window overlapping the ramp, and any in-test behaviour, is treatment-affected: the variance still falls, the estimate becomes biased, and the scorecard looks cleaner while being wrong.
- Handle accounts with no pre-period by stratum, not by deletion. Put them in their own stratum where (X - Xbar) is zero so they receive no adjustment, and estimate theta separately there. Dropping them changes the estimand to returning accounts only.
- Stratify assignment itself on pre-period hours decile crossed with signup_platform. That guarantees balance rather than correcting for it afterwards, and its gains are not additive with CUPED because both draw on the same covariate information.
- Report adjusted and unadjusted estimates side by side. They must agree within noise; a large gap diagnoses a broken covariate or broken randomisation, not successful variance reduction.
Worked solution 30 min
- Build one row per account: X = sum(played_seconds) over [assignment_ts - 28d, assignment_ts), Y = qualified hours over the 14-day test. Keep every assigned account, covariate or not.
- Estimate theta by regressing Y on X within the control arm: theta = Cov(X, Y) / Var(X), which with corr = 0.62 equals 0.62 * sd(Y) / sd(X).
- Variance reduction = 1 - 0.62^2 = 1 - 0.3844 = 0.6156, a 38.4 percent cut in variance.
- The MDE scales by sqrt(0.6156) = 0.7846, so 0.90 percent becomes 0.706 percent, an improvement of about 22 percent.
- Compare against the target: 0.706 / 0.40 = 1.77 remaining in MDE, which is 1.77^2 = 3.1 times the accounts or the duration.
Follow-up
- CUPED gets you to 0.71 percent and you need 0.40 percent. What is the cheapest way to close the rest, and what does running longer actually buy given within-account correlation across weeks?
- Someone proposes using the first three days of in-test hours as the covariate because the correlation is 0.81. What do you say?
- Which accounts gain nothing from CUPED, and what does that imply for the variance reduction available in a new-user test?
Trade retention against revenue on an ad-supported tier
The free_ad_supported tier proposes raising ad breaks per delivered hour. The company metric is net revenue per active account-month; the stated guardrails are advertising seconds per delivered content hour and trailing 90-day retention of ad-supported accounts. Using fct_stream (account_id, ad_breaks_served, ad_seconds_played, played_seconds, is_qualified) and fct_subscription_period (account_id, net_amount_usd, period_end_ts, renewal_outcome), design the decision: primary metric, guardrails, and an explicit exchange rate saying how much retention you would spend for a revenue point. A four-week test is all you are offered.
Approach
- Say plainly that this is a real conflict, not a monitoring exercise: raising ad load moves the primary metric up and the guardrail down through the same mechanism, so no definition of either metric makes both improve and the job is to price the trade, not to find a metric that hides it.
- Decompose the primary so the conflict is visible in the algebra: net revenue per active account-month equals ad seconds per delivered content hour times delivered content hours per active account-month times net revenue per ad second, and the change raises the first term while lowering the second, with the third term falling too if added inventory clears at a lower price.
- Establish that a four-week test cannot read the guardrail: the retention response is lagged past the window, so the test is powered on the numerator and will return a significant revenue gain against a retention estimate whose confidence interval spans the effect sizes that would kill the launch. State the required holdout instead, held at least 90 days at a size chosen to detect the retention drop that would break even.
- Convert both sides into one currency before arguing: expected lifetime value per ad-supported account equals net revenue per active account-month times expected retained months, so a 1 percent revenue gain is worth taking only if the retention cost is under roughly 1 percent of expected retained months, and write that break-even as the exchange rate.
- Keep one guardrail non-tradeable: advertising seconds per delivered content hour gets a hard ceiling that no revenue gain buys past, because an exchange rate alone permits an unbounded walk up the ad-load curve one defensible step at a time.
- If the four weeks must produce a decision, name the surrogate explicitly and its bias: weeks three and four qualified hours per account-week among treated accounts, which understates the retention harm because the accounts most likely to leave are still present and still consuming during the window.
Follow-up
- Net revenue per ad second falls as load rises because added inventory clears cheaper. How does that change your break-even, and does it make the decision easier or harder?
- How large must the 90-day holdout be to detect a one-percentage-point retention drop, and what does that cost in forgone revenue while it runs?
- The four-week result is a 3 percent revenue gain with a retention estimate of minus 0.4 points plus or minus 1.5. What do you recommend, in one sentence, and what do you refuse to claim?
Involuntary churn share jumped after a dashboard rebuild
Involuntary share of gross churn — fct_subscription_period rows with renewal_outcome = 'failed_involuntary' over rows with renewal_outcome in ('cancelled_voluntary','failed_involuntary'), keyed on period_end_ts month — reads 31% for last month against 19% for the three months before. Billing reports no change in decline rates and dunning_attempts per failed period is unchanged. Using fct_subscription_period (period_end_ts, renewal_outcome, payment_status, dunning_attempts, cancel_requested_ts, billing_provider), determine whether involuntary churn actually rose. Deliverable: the corrected month-on-month series and the specific change that produced the step.
Approach
- Check the month's maturity before anything else. Periods ending recently still have retries in flight, and a row reading 'failed_involuntary' today becomes payment_status = 'retried_paid' next week. Recompute every one of the last twelve months as of a fixed age — period_end_ts plus the maximum retry window — instead of as of today.
- If the step survives age alignment, hunt for a definition change: diff the model or query against the version that produced 19%, and check specifically whether the month is now keyed on cancel_requested_ts rather than period_end_ts, and whether 'refunded' or 'chargeback' rows have entered one side of the ratio.
- Settle it with one query rather than a narrative. Run the old definition and the new definition on the same month. Two numbers from one month under two definitions is decisive; two numbers from two months under one definition is not.
- If a definition did change, restate history rather than leaving a step in the chart: run the new definition back across all twelve months and publish both series with the cutover marked, so nobody re-discovers this in a quarter.
- Cut the horizon-closed series by billing_provider before concluding nothing moved. Card-direct, app-store and carrier billing have different retry mechanics and different success rates, so a mix shift between them moves the ratio with every provider's own rate flat.
Follow-up
- How would you set the dunning horizon from the data instead of taking billing's stated maximum?
- Under cancel-at-period-end, the cancellation request and the entitlement end fall in different months. Which timestamp keys this metric, and what does the other choice do to the curve?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.
Describe a time you had to choose between a simple model and a complex…
Describe a time you had to choose between a simple model and a complex one; what was the deciding factor?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- 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.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that project again?
Give an executive a number you are not sure of
An executive needs a figure for a board deck by end of day: the effect on month-6 paid retention of a dunning-schedule change that has been running nine weeks. Your estimate is +1.2 points with a 95 percent interval of [-0.3, +2.7], and the month-6 cohort has not matured — you are projecting from month-2 behaviour. They say "one number, no ranges, it's a board deck." Deliverable: the figure you supply, the single sentence that sits under it, and what you do if the sentence is deleted.
Approach
- The probe is whether you can be useful under a constraint you disagree with. Supply the number. A refusal gets replaced within the hour by someone else's number carrying no caveat at all, which is strictly worse than yours carrying a short one.
- Separate the two uncertainties, because they behave differently and only one shrinks with patience. Sampling error is the interval and narrows as weeks accumulate. Extrapolation error from month-2 to month-6 does not narrow at all and is bounded only by evidence.
- Bound the extrapolation empirically before the deck goes out: on the last several fully matured cohorts, check how closely month-2 retention predicted month-6, and report that spread. This converts "we are projecting" from a hedge into a number, which is the only form of caveat that survives a slide.
- Translate the interval into the decision rather than the statistic. The executive needs the range only where it flips an action, so say at which end of it the change stops paying for itself, and otherwise give the point estimate.
- Commit to the date the figure becomes an observation — the cohort reaching 180 days of age, with monthly and annual billing intervals reported as separate curves since an annual account has had no opportunity to churn before day 365 — and put that date in the footnote so the projection has an expiry.
Follow-up
- The deck ships with your number and the footnote removed. What do you do, and when?
- How does your answer change if the interval were [-1.5, +2.7] — wide and straddling zero more evenly?
- What is the difference between what you have produced here and a forecast, and does the executive need to know?
Disagree with a product manager about a completion metric
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?
- 01
Describe a time you had to choose between a simple model and a complex one; what was the deciding factor?
- 02
An executive needs a figure for a board deck by end of day: the effect on month-6 paid retention of a dunning-schedule change that has been running nine weeks. Your estimate is +1.2 points with a 95 percent interval of [-0.3, +2.7], and the month-6 cohort has not matured — you are projecting from month-2 behaviour. They say "one number, no ranges, it's a board deck." Deliverable: the figure you supply, the single sentence that sits under it, and what you do if the sentence is deleted.
- 03
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.
Is this an official Prime Video interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Prime Video. Rounds and questions reflect what candidates have reported, not a process Prime Video has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical rounds?
The difficulty is moderate to high. You should expect to be challenged on your knowledge of fundamental algorithms and your ability to write clean, efficient code under time pressure.
PracHub interview research ↗What is the best way to prepare for behavioral rounds?
Focus on your past projects. Use the STAR method (Situation, Task, Action, Result) to structure your stories, ensuring you highlight your personal contribution and the impact on the business.
PracHub interview research ↗How long does the process usually take?
While it varies, the process can span several weeks to a month. Maintain regular communication with your recruiter to stay updated on your status.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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