As a Data Scientist at Rocket, you play a pivotal role in transforming data into actionable insights that drive decision-making across the organization. Your work is essential for enhancing product offerings, optimizing user experiences, and informing strategic business initiatives. By leveraging advanced statistical methods, machine learning algorithms, and data visualization techniques, you contribute to projects that may span various domains, such as natural language processing (NLP), predictive analytics, and operational efficiency.
The impact of your role extends beyond technical contributions; you will collaborate with cross-functional teams to identify key business challenges and develop data-driven solutions. Given the scale at which Rocket operates, your insights will directly influence product features, customer engagement, and ultimately, the company's bottom line. This position is not only about analyzing data; it is about telling compelling stories that resonate with stakeholders and inspire action.
Phone Screen
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Interview
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Behavioral Interview
reportedThis round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.
What to demonstrate
- Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
- The counterfactual: what would have been done without your analysis, and why that default was worse
- How far your involvement ran past the handoff, and whether you checked that the change did what you predicted
How to prepare
- Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
- Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
- Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.
Final Interviews
reportedA day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.
What to demonstrate
- Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
- Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
- Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method
How to prepare
- Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
- Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
- Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
- Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times
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.
Testing hours, revenue or completion with a difference in means on a heavy-tailed distribution.
Listening and viewing hours per account are strongly right-skewed and content popularity is close to power-law, so the variance of a sample mean is dominated by a few accounts and the central limit approximation converges slowly at realistic sample sizes. A t-test on mean hours can flip sign when one heavy account's week changes, and an experiment can appear significant because a single title released into one arm's window. Capping at a pre-registered percentile, or decomposing into a rate (did they stream at all) and a conditional intensity, controls the variance, at the stated cost that capping biases toward zero exactly when the true effect lives in the tail.
Accepting a metric definition without asking about the denominator
Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.
Comparing periods without accounting for seasonality or day-of-week
Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe a machine learning project you've worked on. What challenges …
Describe a machine learning project you've worked on. What challenges did you face?
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.
- 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?
What algorithms would you use for a classification problem?
What algorithms would you use for a classification problem?
Approach
- Set a baseline first, so any model has something honest to beat.
- Check what information would not exist at prediction time, and exclude it.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Measure catalogue concentration with a Gini written from scratch
One calendar month of fct_stream (content_version_id, played_seconds, is_qualified) joins to dim_content_version (content_version_id, content_id, content_type). Roll versions up to content_id, then report, overall and per content_type: the number of content_ids, the share of qualified hours held by the top one percent, and the Gini coefficient over content_ids by hours. Write the Gini from its definition rather than importing one. State which content_ids are in the population you measure over, and defend that choice.
Approach
- Roll versions to content_id before anything else. A remaster or a dubbed rendition is a separate content_version_id sharing a content_id, so leaving it split spreads one work's hours across rows and reports the catalogue as less concentrated than it is.
- Decide and state the population: content_ids with at least one qualified stream this month measures concentration among what was played, while the full territory-eligible catalogue adds the zero-hours tail and pushes both statistics up. Either is defensible; the two are not comparable to each other.
- Implement Gini on the ascending-sorted hours vector as 2 times sum(i times x_i) over (n times sum(x)) minus (n + 1) over n, with i one-based. Verify it on two hand-made vectors before pointing it at real data.
- For the top one percent, take ceil(0.01 times n) content_ids by hours descending over the total. Say what you did at the boundary, because with a few thousand titles the rounding rule moves the answer visibly.
- Compute each content_type inside its own population, and note that the per-type Ginis do not aggregate to the overall one: concentration is not additive across strata, and the overall figure carries between-type inequality the per-type figures exclude.
Worked solution 25 min
- Filter to is_qualified, join content_id and content_type, then group by content_id summing played_seconds and dividing by 3600.
- Write gini(x): drop negatives, sort ascending, n = len(x), return 2 times (arange(1, n+1) times x).sum() over (n times x.sum()) minus (n + 1) over n. Assert gini([1,1,1]) is 0 and gini([0,0,1]) is 2/3.
- Top one percent: k = ceil(0.01 times n); sort hours descending and divide the k-largest sum by the total.
- Repeat both statistics inside each content_type group, keeping each type's own n.
- Assemble a frame of scope, n_content_ids, top1pct_share and gini, with one overall row plus one row per content_type.
Follow-up
- This month's Gini rose by 0.03. Name three things other than a ranking change that could produce that.
- How would you measure breadth so a ranking team could act on it, rather than reporting one summary number?
- A rights window expired mid-month and removed 400 titles. How do you keep the month-over-month comparison honest?
How would you optimize a SQL query for better performance?
How would you optimize a SQL query for better performance?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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 would you verify this result without re-running the same query?
Home-row impressions that never converted, and an empty result
fct_impression has impression_id, profile_id, content_version_id, surface, slate_position, rendered_at and viewport_visible_ms. fct_stream has stream_id, profile_id, content_version_id, started_at, is_qualified and a nullable impression_id, populated only when the play started from a rendered slate. For surface = 'home_row' impressions rendered yesterday with viewport_visible_ms > 0, return content_version_id and the number of impressions that produced no qualified stream within 30 minutes. A colleague's draft filters WHERE impression_id NOT IN (SELECT impression_id FROM fct_stream) and returns zero rows on data that plainly contains misses. Explain why, then write the correct query.
Approach
- Name the mechanism rather than the symptom. fct_stream.impression_id is nullable, so the subquery returns a set containing NULL. In SQL's three-valued logic x NOT IN (..., NULL) evaluates to UNKNOWN whenever x matches nothing else, never to TRUE, so the WHERE clause admits no rows. This is correct engine behaviour on correct data.
- Fix with NOT EXISTS, which is a correlated existence test and is NULL-safe by construction, or with LEFT JOIN ... WHERE s.impression_id IS NULL. Adding IS NOT NULL to the subquery also works but leaves the same landmine armed for whoever edits the query next.
- Put is_qualified = true and the 30-minute window inside the join or the correlated predicate, never in an outer WHERE over a LEFT JOIN. An outer filter on a right-side column turns a LEFT JOIN back into an inner join and returns the exact complement of the set you were asked for.
- Match on impression_id, not on (profile_id, content_version_id). The second is a many-to-many between two event tables and will count one stream against every impression of that title for that profile.
- Keep viewport_visible_ms > 0. A row that was never scrolled into view is not a recommendation that failed; it is a recommendation that was never made, and including it depresses every title's conversion by whatever share of the row went unseen.
Worked solution 20 min
- Prove the mechanism in one line: SELECT COUNT(*) FROM fct_stream WHERE impression_id IS NULL. A non-zero count is the entire explanation and should be shown before the rewrite.
- Rewrite as NOT EXISTS, with is_qualified and started_at BETWEEN rendered_at AND rendered_at + INTERVAL '30 minutes' inside the correlated predicate.
- Compute eligible and converted impression counts per content_version_id separately; the requested answer must be their difference.
- Spot-check one high-count content_version_id by listing its impressions alongside any streams sharing those impression_ids.
Follow-up
- Two home rows on the same screen render the same title, and the stream carries one impression_id. Should the other impression count as a miss? Defend whichever rule you pick.
- Would NOT EXISTS and LEFT JOIN ... IS NULL produce the same plan here, and is there a case where you would prefer one over the other?
- What attribution window between rendered_at and started_at do you allow, and why does an unbounded window overstate conversion rather than merely add noise?
How do you prioritize your tasks when working on multiple projects?
How do you prioritize your tasks when working on multiple projects?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
Given a dataset with customer feedback, how would you analyze it to de…
Given a dataset with customer feedback, how would you analyze it to derive actionable insights?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
If you were tasked with predicting sales for the next quarter, what st…
If you were tasked with predicting sales for the next quarter, what steps would you take?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you approach a project to improve customer retention for a p…
How would you approach a project to improve customer retention for a product?
Approach
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
Explain the difference between supervised and unsupervised learning.
Explain the difference between supervised and unsupervised learning.
Approach
- Clarify what is being asked and what a complete answer would contain.
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Triage a sample ratio mismatch before reading segment cuts
A 50/50 account-randomised test of a new detail-page layout has run ten days. The scorecard reports 2,014,880 accounts in control and 1,997,340 in treatment. It also reports 22 pre-declared segment cuts across signup_platform, device_type and signup_country, of which three are significant at p < 0.05, including a strongly negative one on game_console. The PM wants to ship with a console carve-out. State what you check first and what it returns, what the three segments are worth, and what would have to be true before any part of this scorecard can be read.
Approach
- Run the sample ratio check before looking at any effect: a chi-square goodness-of-fit against the intended 50/50 on raw assignment counts, one degree of freedom. Treat p < 0.001 as a hard stop rather than a warning banner.
- If it fires, stop reading effects entirely. Unequal counts mean the arms are not exchangeable, so every estimate on the scorecard, overall and per segment, carries a bias of unknown size and unknown direction.
- Hunt the cause in the order that explains most mismatches: assignment happening downstream of a redirect or a feature-flag fetch that fails at different rates per arm, exposure logging dropped on one arm's code path, bot and is_test_account filtering applied after assignment rather than before, and deduplication of accounts that were reassigned.
- Treat the segments as a multiplicity problem even in a clean test. Twenty-two cuts at alpha 0.05 give 1.1 false positives in expectation, and the chance of at least one is 1 - 0.95^22 = 68 percent, so three hits is barely news.
- Apply Benjamini-Hochberg at FDR 0.05 across the 22 p-values, and apply it as the step-up procedure it is: order the p-values ascending, compare the k-th against k * 0.05 / 22, take the largest k that passes, and reject everything at or below it. Require any survivor to come with a pre-registered mechanism before it becomes a product carve-out instead of a post-hoc story.
Worked solution 15 min
- Expected count per arm = (2,014,880 + 1,997,340) / 2 = 2,006,110, so the deviation is 8,770 accounts.
- chi-square = 2 * 8,770^2 / 2,006,110 = 2 * 76,912,900 / 2,006,110 = 76.7 on one degree of freedom.
- Convert: z = sqrt(76.7) = 8.76, so p is on the order of 1e-18. The realised split is 50.22 / 49.78.
- Set the segments aside, and write down the Benjamini-Hochberg rule correctly for when there is a valid scorecard to apply it to. Order the 22 p-values ascending and find the largest k with p_(k) <= k * 0.05 / 22. The ladder is 0.00227 at k = 1, 0.00455 at k = 2, 0.00682 at k = 3, rising linearly to 0.05 at k = 22. Because it steps up, that largest k also rejects every smaller p-value beneath it: p-values of 0.001, 0.004 and 0.006 would all be rejected, even though two of them sit above 0.00227. The single threshold 0.05 / 22 = 0.00227 is Bonferroni, a different and more conservative rule.
Follow-up
- An engineer finds that assignment happens after a client-side flag fetch that times out more often on smart_tv. Is that a mismatch cause, and can the test be salvaged by excluding those accounts?
- Suppose the counts had been balanced. What evidence would make the game_console result worth acting on?
- Under Benjamini-Hochberg, a p-value of 0.006 can be rejected among these 22 cuts while a p-value of 0.004 elsewhere in the same family is not. Explain how both can be true at once.
Consumption fell the week a new ranker shipped
Qualified hours fell 4% in the seven days after a home-row ranker shipped to 100%, and a rollback is being demanded for tomorrow. You have fct_stream (played_seconds, is_qualified, content_version_id, start_source, started_at, play_territory), fct_impression (ranker_version, content_version_id, rendered_at), dim_content_version (content_id, parent_id, rights_expires_at, available_territories, catalogue_added_ts, is_active_version), and no holdout — the rollout went from 5% straight to 100%. Determine whether the ranker caused the fall. Deliverable: a defensible causal read, or an explicit statement of what cannot be concluded and what you would run instead.
Approach
- Check supply before demand. Sum the prior period's qualified hours on content_version_ids whose rights_expires_at falls inside the window, whose is_active_version flipped, or whose available_territories lost a territory. If titles carrying several points of hours left the catalogue that Monday, the fall is partly accounted for before the ranker is even discussed.
- Separate the mechanically unavailable from the merely deranked. Hours lost on items still available but no longer surfaced belong to the ranker; hours lost on items that cannot legally be served do not, and no ranking change recovers them. Only the first category is evidence for a rollback.
- Recover a comparison the rollout destroyed. With no holdout, use the 5% phase as a pre-period and territories unaffected by the expiry as a control, running difference-in-differences with the expiry as the treatment. State the parallel-trends assumption out loud and test it on the four pre-window weeks; abandon the estimator if it fails rather than reporting it with a caveat.
- Measure displacement instead of gross hours. For accounts whose usual items left, did total hours fall or did they substitute within the catalogue? Fully substituted removal costs nothing in hours and shows up only in the affected rights holder's payout, which is a different problem with a different owner.
- Ask separately what the ranker did to exposure: impression share by content_id under each ranker_version, and top-1% exposure concentration. A ranker that concentrates exposure can depress hours through a mechanism nobody has named yet, and this is what distinguishes that story from the rights story.
- Refuse the causal claim the rollout cannot support. Say plainly that a 5%-to-100% ramp across a confounded week yields no unbiased effect estimate, and propose the switchback or re-randomised holdout that would, with its cost stated.
Follow-up
- Design the switchback: slice length, burn-in long enough to clear carryover on a personalised surface, and how the standard error handles correlation between adjacent slices.
- If the rollback is ordered regardless of your analysis, what do you measure during the rollback to get a second, independent read?
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.
A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.
Discuss a time when you had to refactor code for efficiency.
Discuss a time when you had to refactor code for efficiency.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
Follow-up
- How did you know the outcome was caused by your change?
- 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?
Defend a flat result on a flagship discovery launch
A new ranker ran to 50 percent of accounts for four weeks. Your read: qualified hours per active account-week is +0.4 percent with a 95 percent interval of [-0.9 percent, +1.7 percent]. Meanwhile the share of qualified streams with start_source = 'algorithmic_slate' rose six points and the share with start_source = 'search' fell five. The team reads the slate shift as the win and wants to ship. The launch review is Friday. Deliverable: a five-minute verbal position and the one table you put on the screen.
Approach
- The probe is whether you can hold a position under social pressure without overclaiming in the other direction. Separate the two claims explicitly: the start-source shift is well measured and real; the hours effect the launch was justified on is not distinguishable from zero at this sample size. Those are different statements and only one is contested.
- Do the power arithmetic before the meeting, using the observed per-account variance on a right-skewed hours metric. Arrive able to say "at this n we could not have detected less than X percent", which is a fact, instead of "it didn't work", which is an opinion the room can simply disagree with.
- Show the substitution as arithmetic, not interpretation: decompose treatment hours by start_source and show slate hours rising by approximately what search hours fell. A start-source mix shift with no change in total is exactly what displacement looks like, and the decomposition sums to the total so there is nothing to argue about.
- Refuse the symmetric overclaim. The interval's upper bound is commercially meaningful, so "the ranker does nothing" is not supportable either; say that out loud, because it is what makes the rest of your position credible.
- Offer the falsifiable next step with a price: a longer read powered for the hours effect, or CUPED on a pre-period consumption covariate measured before assignment, with the required account-weeks stated. Put the disagreement on the pre-registered metric, not on the team's judgement.
Follow-up
- The PM argues start-source mix is a leading indicator of retention. How would you test that claim, and how long would it take?
- Suppose the interval had been [+0.1 percent, +1.9 percent]. Does your position change, and by how much?
- The decision goes against your read and the ranker ships. What do you do on Monday?
- 01
Discuss a time when you had to refactor code for efficiency.
- 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 new ranker ran to 50 percent of accounts for four weeks. Your read: qualified hours per active account-week is +0.4 percent with a 95 percent interval of [-0.9 percent, +1.7 percent]. Meanwhile the share of qualified streams with start_source = 'algorithmic_slate' rose six points and the share with start_source = 'search' fell five. The team reads the slate shift as the win and wants to ship. The launch review is Friday. Deliverable: a five-minute verbal position and the one table you put on the screen.
Is this an official Rocket interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Rocket. Rounds and questions reflect what candidates have reported, not a process Rocket has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How challenging is the interview process for a Data Scientist at Rocket?
The interview process is thorough but designed to be accommodating. Candidates often find it balanced, with a focus on both technical and behavioral assessments. Preparation is key to navigating the process successfully.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate a strong blend of technical expertise and interpersonal skills. They are able to communicate complex ideas clearly and show adaptability in problem-solving.
PracHub interview research ↗What is the company culture like at Rocket?
Rocket fosters a collaborative environment that values diverse perspectives and encourages innovation. Team members are supportive and prioritize creating a positive workplace culture.
PracHub interview research ↗What is the typical timeline from initial interview to offer?
Candidates can expect a timeline of 4-6 weeks from the initial screen to an offer, although this may vary based on the specific team and role.
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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