As a Data Scientist at Whoop, you are at the intersection of human performance science and cutting-edge technology. You are responsible for transforming complex, high-frequency biometric data into actionable insights that empower users to optimize their sleep, recovery, and strain. The role goes beyond standard analytics; it requires a deep commitment to understanding the physiological signals that define human potential and translating those into scalable, product-ready models.
Your work will directly influence the Whoop ecosystem, from refining the algorithms that power the recovery score to designing experiments that validate new hardware and software features. You will collaborate closely with product managers, engineers, and researchers to solve high-stakes problems, such as identifying the drivers of sleep quality or predicting long-term strain trends. Success in this role requires a balance of rigorous statistical thinking, a product-first mindset, and the ability to articulate complex technical concepts to non-technical stakeholders.
Recruiter Screen
reportedA 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
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
Data Challenge
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.
Conversational Interviews
reportedAn 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.
Final Rounds
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
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Whoop Senior iOS Engineer Interview Experience — Four Onsite Rounds, Rejected on System Design
I recently interviewed for a Senior iOS position at WHOOP (had a referral). Got rejected on the last round of the onsite, System Design. Didn't make it. My overall impression of this company's interview process was really, really good. The company also supports H1B and green card, so I hope anyone who needs that applies quickly. First, my impressions of WHOOP: Very efficient — from the initial co…
Read full experiencePracHub editorial advice for the preparation topics above.
Counting on an identity key that changes underneath the metric
visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.
Slicing a flat experiment until a segment reaches significance
Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.
Ending an analysis without a recommendation or next step
Close with what you would do and what would change your mind, stated as a condition you can check later. If the evidence is genuinely inconclusive, recommend the specific next measurement and say what it costs in time or exposure.
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.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
What are the risks of performing multiple hypothesis tests simultaneou…
What are the risks of performing multiple hypothesis tests simultaneously?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- How would you explain this result to someone who does not know statistics?
- Which assumption here is most likely to be violated in practice?
Rebuild per-visitor ordering without groupby convenience methods
You have a DataFrame of 2 million fct_event rows with visitor_id, occurred_at_utc and event_id, unsorted and containing duplicate timestamps within a visitor. Produce three new columns: event_rank, the 1-based position of the event within its visitor ordered by occurred_at_utc; seconds_since_prev, the gap to that visitor's previous event, NULL for the first; and is_first_for_visitor. You may use sort_values, shift, cumsum, numpy and boolean masking. You may not use groupby.transform, groupby.apply, groupby.cumcount, groupby.rank or merge_asof. Break timestamp ties on event_id.
Approach
- Sort once by ['visitor_id', 'occurred_at_utc', 'event_id'] and reset the index. The whole exercise reduces to row arithmetic on a sorted frame, and the tiebreak on event_id is what makes the result reproducible across runs.
- Mark visitor boundaries with is_first = df['visitor_id'].ne(df['visitor_id'].shift()). This is the single fact every other column derives from.
- Compute seconds_since_prev as the diff of the timestamp column, then overwrite it with NaT/NaN wherever is_first is True. The shift crosses the boundary between visitors and will otherwise hand the first row of each visitor the last event of the previous one.
- Build event_rank from a running counter that resets at boundaries: take a global cumulative position (np.arange(len(df))) and subtract, per row, the global position at which that visitor started. Get the start position by forward-filling the positions where is_first is True, which is a cumsum-free reset and is O(n).
- Verify against the forbidden method once, as a test rather than as the implementation, and confirm the two agree on every row.
Follow-up
- The frame does not fit in memory. How does your approach change if you can only process one visitor-partitioned chunk at a time?
- occurred_at_utc is client-supplied and sometimes runs backwards within a visitor. Does your seconds_since_prev go negative, and should it?
- How would you extend this to reset the counter at every change of surface as well as visitor?
Split a pooled conversion drop into rate and mix
You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.
Approach
- State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
- Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
- The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
- Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
- Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
- Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.
Worked solution 25 min
- Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
- Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
- rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
- contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
- assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.
Follow-up
- The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
- Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
- Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?
Write a query using SQL window functions to calculate rolling averages…
Write a query using SQL window functions to calculate rolling averages of heart rate variability (HRV) for users.
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.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
How would you join user activity data with physiological data to ident…
How would you join user activity data with physiological data to identify trends over time?
Approach
- 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.
- 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?
Given a table of event logs, how would you calculate the time between …
Given a table of event logs, how would you calculate the time between specific user actions?
Approach
- Say which table is the grain you start from, and join outward from it.
- 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?
Find reactivation gaps in account paid-period history
fct_subscription_period holds account_id, subscription_id, period_start_utc, period_end_utc, period_status and change_reason. A mid-period plan or seat change closes one row and opens another, so a single continuous paid tenure is often many rows, and an account may hold two overlapping subscriptions. Collapse rows with period_status in ('active','past_due') into continuous tenures per account, treating gaps of three days or less as continuous. Return account_id, tenure_start, tenure_end, and for every tenure after the first, the gap in days that preceded it.
Approach
- Filter to paid rows only: period_status IN ('active','past_due'). Trialing periods are not tenure, and including them turns every trial that never converted into a one-period tenure followed by a fake churn.
- Order by period_start_utc and take a running maximum of all prior ends: MAX(period_end_utc) OVER (PARTITION BY account_id ORDER BY period_start_utc, period_end_utc, subscription_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING). Those three columns are the only stable ordering this schema exposes, so check first that they are unique within an account; if rows tie on all three, the island numbering is order-dependent between runs and you need a real row key before the result is reproducible.
- LAG on its own is wrong here because with overlapping or nested periods the immediately preceding row by start date is not the one that ends latest, so the running maximum is the part that cannot be shortcut.
- Flag a new island when prior_max_end IS NULL OR period_start_utc > prior_max_end + interval '3 days', then number islands with a running SUM of the flag over the same ordering and an explicit ROWS frame.
- Group to (account_id, island) taking MIN(period_start_utc) and MAX(period_end_utc), then LAG(tenure_end) OVER (PARTITION BY account_id ORDER BY tenure_start) to compute the preceding gap in days for every tenure after the first.
- Sanity-check with change_reason, which is the only lineage this schema carries: list its distinct values first, then confirm that rows recording a plan or seat change sit inside a tenure rather than opening one, and that every tenure after the first opens on a row whose reason records a restart rather than an ordinary renewal. Do not reconcile against a churn timestamp on dim_account, which this schema does not define; and where such a column does exist, a cancellation timestamp records when the request was made and routinely sits weeks before the period it ends.
Worked solution 35 min
- Find an account with a known mid-period upgrade and dump its period rows to use as the trace case.
- Check that (period_start_utc, period_end_utc, subscription_id) is unique per account, since the whole ordering rests on it.
- Write the paid-rows CTE and the running MAX with the explicit frame.
- Add the island flag and the running SUM, then verify the trace account yields one island.
- Group to tenures and add the LAG-based gap in days.
- List the distinct change_reason values, then count accounts with more than one tenure and compare against the count of accounts carrying a restart-flavoured reason anywhere in their history.
Follow-up
- Why three days of grace? What do 0 and 30 days each do to the count of accounts classed as reactivated?
- An account runs two concurrent subscriptions for different teams. One tenure or two, and what does the revenue reader expect?
- How would you turn these tenures into a monthly gross logo churn series without double-counting an account that churned and returned in the same month?
How would you design a metric to measure the success of a new sleep-tr…
How would you design a metric to measure the success of a new sleep-tracking feature?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
A key engagement metric has dropped suddenly; how would you investigat…
A key engagement metric has dropped suddenly; how would you investigate the root cause?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you prioritize your work when faced with competing requests fro…
How do you prioritize your work when faced with competing requests from different teams?
Approach
- 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.
- Name one primary metric, then the guardrail that stops it being gamed.
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?
Describe the most common experimentation pitfalls you have encountered…
Describe the most common experimentation pitfalls you have encountered.
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
How do you determine the sample size required for statistical signific…
How do you determine the sample size required for statistical significance in an A/B test?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
How would you define "value" for a Whoop member, and how would you tra…
How would you define "value" for a Whoop member, and how would you track it?
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer would contain.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Define success for halving the free trial length
A proposal cuts the free trial from 30 days to 14. Trial-to-paid conversion is defined on fct_subscription_period: numerator, subscription_id whose first row with is_first_paid_period = TRUE has period_status IN ('active','past_due') and period_start_utc no later than 14 days after trial end; denominator, subscription_id whose first row has period_status = 'trialing' and period_start_utc in the cohort week. dim_account (account_id, account_created_at_utc, signup_surface) is also available and joins to fct_subscription_period on account_id. Explain why conversion rate alone cannot decide this, define the metric that can, and state the horizon and lag your readout needs. Deliverable: the decision metric and the readout schedule.
Approach
- Show the incomparability numerically before arguing about it: at a fixed calendar readout date, a larger share of the 14-day arm's cohorts have completed trial end plus 14 days plus settlement, so the short arm leads mechanically and the lead shrinks as both arms mature.
- Fix the censoring first, and do not mistake a rescaling for a fix. The given conversion rate already has trial starts as its denominator, so 'paid accounts per 1,000 trial starts' is that same ratio multiplied by 1,000 and answers nothing new. What the censoring needs is a fixed cohort age: read both arms at account_created_at_utc plus 60 days, which clears the long arm's full 30 + 14 + 3 = 47-day path to a settled first payment, so neither arm is censored at readout.
- Then move the denominator upstream, which is a separate fix and the only one that catches a higher share of a smaller population: paid accounts per 1,000 dim_account rows created in the cohort week. Precondition — this diverges from the conversion rate only when trial length is visible before the trial starts, on pricing pages, in ads or in the signup flow. If the arms are assigned at trial start and nothing upstream differs, trial starts per 1,000 new accounts is equal across arms by construction and the two metrics are the same comparison rescaled. Report that ratio per arm and say which regime you are in rather than assuming one.
- Add the leg neither denominator can see: a shorter trial converts users who had less time to reach activation, so read month-3 gross revenue churn and week-4 retention of the converted cohort, and read activation at trial day 7 to see whether the converted population changed composition.
- Audit 'past_due' before comparing, since the definition counts it as converted: report the rate with and without it per arm, because a gain that sits entirely inside past_due is a billing artefact rather than a conversion effect.
- Be explicit that the twelve-month consequence is not observable inside a planning cycle: pre-register that the decision is made on the 60-day metric, that the twelve-month cohort read will be published as a check, and what action follows if the two disagree.
Worked solution 35 min
- Tabulate, at a fixed calendar readout date, the share of each arm's cohorts that have completed trial end plus 14 days plus 3 days of settlement, and show the gap.
- Define the decision metric as paid accounts per 1,000 dim_account rows created in the cohort week, evaluated at account_created_at_utc plus 60 days for both arms, and write the 30 + 14 + 3 = 47 arithmetic that justifies 60 as the common clock.
- Report trial starts per 1,000 new accounts per arm beside it, so the reader can see whether the upstream denominator is doing any work in this test or whether it is arithmetically pinned to the conversion rate.
- Compute the earliest honest readout date from the enrolment window: 60 days after the last account creation in the window plus a 3-day settlement lag, with no partial-cohort comparison permitted before it.
- Add the quality leg: month-3 gross revenue churn, week-4 retention of converted accounts, and day-7 activation per arm.
- Report the conversion rate with and without past_due per arm, and state which version the decision uses.
Follow-up
- What happens to a user who would have converted on day 20, and how would you detect that population in the data?
- The short arm has lower day-7 activation but higher conversion. Reconcile those two facts into one story.
- What randomisation unit do you use here, and what goes wrong with the obvious alternatives?
Decide whether a one-day core-action drop is real
A daily dashboard counts distinct fct_event.user_id with is_core_action = TRUE, filtered on occurred_at_utc, and is read at 09:00 UTC. This morning it shows yesterday down 22% against the day before. fct_event is partitioned on received_at_utc. You have fct_event, fct_session and dim_user with thirteen months of history. Deliver a one-paragraph verdict, escalate or do not escalate, with the evidence that settles it, before anyone proposes a product hypothesis.
Approach
- Identify which two weekdays the comparison actually spans, then pull the same weekday-pair transition for the last 52 weeks and place the observed 22% inside that distribution. A day-over-day comparison in a product with a weekday pattern is a comparison of two different populations, so the reference class is the same transition historically, not the prior day.
- Measure partition completeness rather than assuming it. For each of the last 30 days compute the share of that day's occurred_at_utc rows that had landed by 09:00 UTC the following morning, split by surface; mobile clients buffer events offline, so the freshest partition is systematically short and the shortfall is not uniform across surfaces.
- Recompute the same series keyed on received_at_utc. If the drop survives on both keys it is not a lateness artefact; if it exists only on occurred_at_utc it is the partition filling in.
- Check the two exclusion flags before segmenting anything: a change in is_bot_flagged coverage or a batch of is_internal accounts entering or leaving moves a distinct-user count with no user behaviour behind it.
- Only if the movement survives all of the above, begin the segment decomposition. Say explicitly in the verdict which of these four checks the movement passed, so the next reader does not repeat them.
Follow-up
- What publication lag would you set for this dashboard, and how would you derive the number rather than pick it?
- If you switch the metric to received_at_utc, what does that break for anyone comparing to historical figures?
- How would you detect the same problem automatically, so a human does not have to notice it each morning?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Diagnostic, 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Largest 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Largest 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Second 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗05The 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.
Practice prompt ↗Practice prompt ↗06Retest 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.
Practice prompt ↗Practice prompt ↗07Full 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
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.
Describe a time you had to explain a complex model to a non-technical …
Describe a time you had to explain a complex model to a non-technical stakeholder.
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.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Disagree with a product manager's roadmap claim using data
A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.
Approach
- Recognise what is being probed: whether you can separate a number being right from an inference being wrong, and do it without costing the PM face. The generic answer recites that correlation is not causation; the strong one names the specific confound and proposes the cheapest design that could distinguish the explanations.
- State the alternative concretely. Accounts that connect an integration in week one are accounts that already have a workflow and a technical owner, so week-one intent plausibly drives both the connection and week-four retention. The selection is on intent, which no amount of post-hoc adjustment observes.
- Order the discriminating analyses by cost. First, condition on pre-connection activity by comparing retention within strata of week-one core-action count, which removes the crude version of the confound but not unobserved intent. Second, look for variation in integration availability that was unrelated to intent, such as a staggered release or an outage window. Third, an encouragement design that randomises a prompt to connect and reads the intent-to-treat effect on week-four retention, which is the only version that identifies an effect.
- Run the timing check, because it is nearly free and it is the most persuasive single piece of evidence. If the retention advantage among connectors is already visible before any of them connected, the causal story is largely finished.
- Pre-commit to what would change your mind and say it before you show anything: if the gap survives stratification and the encouragement arm moves week-four retention at all, the feature has a case and you will say so.
- Open the 1:1 by agreeing with the true part, that the correlation is real and worth chasing, then ask what effect size the roadmap plan assumes. That makes the size of the claim the topic instead of its authorship.
Follow-up
- The encouragement test needs six weeks and the roadmap locks in two. What do you recommend in the interim?
- Stratifying on week-one activity closes half the gap. What do you conclude, and what do you still not know?
- How would you word this in the roadmap document so the PM's original number is reframed rather than deleted?
Quantify your own impact without claiming the topline you touched
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
Approach
- Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
- Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
- Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
- Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
- Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
- Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.
Follow-up
- Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
- A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
- If you could only keep two of your three claims, which do you drop, and why that one?
- 01
Describe a time you had to explain a complex model to a non-technical stakeholder.
- 02
A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.
- 03
You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.
Is this an official Whoop interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Whoop. Rounds and questions reflect what candidates have reported, not a process Whoop has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews?
The technical interviews are challenging and test your ability to apply concepts to real-world problems. Expect to go deep into the "why" behind your methods, not just the "how."
PracHub interview research ↗What is the best way to prepare for the data challenge?
Treat the data challenge as a real project. Focus on clean code, thorough documentation, and a clear, actionable final presentation that highlights your insights and limitations.
PracHub interview research ↗How long does the entire process take?
The timeline varies, but typically spans a few weeks. It involves several touchpoints, so keep your schedule flexible during the interview period.
PracHub interview research ↗Is there a focus on machine learning?
Yes, especially regarding the deployment of models. Be prepared to discuss how you take a model from a research environment into a production-ready state.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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