CNN Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at CNN, you play a pivotal role in shaping the way news and information are delivered to audiences around the globe. Your expertise in data analysis and machine learning directly influences the design of algorithms that enhance user experiences, optimize content delivery, and drive strategic decision-making across the organization. The impact of your work is felt not only in the accuracy and relevance of the information presented but also in the innovative tools and technologies that underpin CNN's mission to keep the public informed.

Most of the loop measures decision-making under uncertainty rather than recall. You are scored on whether you state your assumptions, commit to an estimate you can defend, and say explicitly what evidence would change it.

Focus areas

• Decompose a metric move by segment and mix
• Pick a randomisation unit that respects interference
• Separate novelty effects from durable behaviour change

Overview

This role is critical due to the vast amount of data generated daily across various platforms. You will work on diverse problem spaces, from predicting viewer preferences to enhancing content recommendation systems. Engaging with cross-functional teams, you will contribute to projects that are at the forefront of media technology, ensuring that CNN remains a leader in delivering timely and trustworthy news. Expect to face complex challenges that require a blend of technical skills and creative problem-solving, making this position both exciting and rewarding.

The CNN interview process

CNN candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Application Review

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

• Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
• Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
• The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

• Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
• Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
• Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical Coding Tests

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What to demonstrate

• Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
• Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
• Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

• Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
• Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
• If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows

Project Discussion

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

What to demonstrate

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

How to prepare

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

Behavioral Assessment

Rounds of this kind usually include one question about work that did not go well, and it is the part that carries the most information. Anyone can narrate a shipped win. What the interviewer learns from a project that stalled is how you behave without a result to hide behind: whether you noticed the problem yourself, how long it took, and who you told. Answers that route the failure onto a data pipeline or a reorganisation close the topic without answering it, and the follow-up comes back to your own part.

What to demonstrate

• Whether you found the error yourself or someone else found it, and how long it sat before anyone knew
• What you changed afterwards, stated as a check you now run rather than a lesson you now believe
• Whether the mistake you choose has real cost attached, such as a quarter of misdirected roadmap or a metric that was reported upward, instead of one that flatters you

How to prepare

• Choose a failure you caught yourself and be ready to say what tipped you off. A story where someone else caught it is still usable, but you will be asked why you missed it.
• Write down the check you added afterwards and where it lives now, so the correction is a concrete artefact rather than a resolution.
• Rehearse saying the cost out loud. Candidates shrink the number by instinct once the interviewer is in the room.

Final Interview Rounds

A 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

Where candidates lose points

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.

Comparing cohort retention curves of different maturities, or building the curve from users who are still present

A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.

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.

Dropping rows with missing values without naming the mechanism

Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.

Questions & practice

Describe a project where you applied machine learning techniques. What were the results?

Medium · machine learning and modelling

Approach

1. Check what information would not exist at prediction time, and exclude it.
2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
3. Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Discuss the importance of feature selection in a machine learning project.

Medium · machine learning and modelling

Approach

1. Set a baseline first, so any model has something honest to beat.
2. Check what information would not exist at prediction time, and exclude it.
3. Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Simulate the false positive cost of repeated peeking

Medium · simulation · peeking · type i error

Quantify the cost of peeking. Simulate a two-arm experiment with no true effect: each arm accumulates Bernoulli conversions at a base rate of 0.10 up to 40,000 units per arm. Run a two-sided two-proportion z-test at alpha 0.05 at ten equally spaced interim points, and record whether the test ever crossed. Report the false positive rate over at least 10,000 replications, alongside the rate for a single look at the final sample only. Use a fixed seed and report a Monte Carlo standard error on both figures.

Approach

1. Generate each replication as two cumulative sums of Bernoulli draws, then read the interim points off the cumulative arrays. Regenerating data at each look would make the looks independent, which destroys exactly the dependence the exercise is about: later looks share data with earlier ones.
2. Use the pooled-variance two-proportion z: p_pool = (x1+x2)/(n1+n2), z = (p1-p2) / sqrt(p_pool*(1-p_pool)*(1/n1 + 1/n2)), reject when |z| > 1.96. State that the normal approximation is fine here because the smallest look has roughly 400 expected conversions per arm.
3. Vectorise across replications rather than looping: draw a (reps, n) array of uniforms, threshold at 0.10, cumsum along axis 1 and slice the ten look indices. A per-replication loop at 10,000 by 40,000 is unnecessarily slow.
4. Record the any-cross indicator per replication, take the mean, and compute the Monte Carlo standard error as sqrt(p*(1-p)/reps) so the reported figure comes with its own precision.
5. Report the single-look rate in the same run as a control. If it does not land near 0.05, the bug is in the test statistic and not in the peeking argument.

Worked solution (30 min)

1. rng = np.random.default_rng(seed); for memory, batch the replications in chunks and accumulate the any-cross count across chunks.
2. Per chunk: draw (chunk, 40000) uniforms per arm, x = (u < 0.10).cumsum(axis=1), slice columns at indices 3999, 7999, ..., 39999.
3. Compute the ten z statistics vectorised over the chunk, take crossed = (np.abs(z) > 1.96).any(axis=1).
4. Aggregate: peek_rate = total_crossed / reps; single_rate = mean of |z_final| > 1.96; mc_se = sqrt(p*(1-p)/reps) for each.

Expected result: The single-look rate lands at 0.05 within about 0.005 at 10,000 replications. The ten-look rate lands near 0.19, which with 10,000 replications has a Monte Carlo standard error of about 0.004, so anything in roughly 0.18 to 0.20 is consistent and anything near 0.40 indicates independent redraws.

Verify it yourself

• The single-look control recovers 0.05 within two Monte Carlo standard errors; if it does not, the z statistic is wrong and the headline number is meaningless.
• The crossing rate is monotone in the number of looks: 1 look near 0.05, 2 near 0.08, 5 near 0.14, 10 near 0.19.
• Halving the base rate to 0.05 leaves the peeking rate essentially unchanged, since the result is about the test's random walk and not about the conversion level.
• Re-running with the same seed reproduces the figure exactly.

Follow-up

• Re-run with 40 looks instead of 10. Why does the curve flatten rather than continue rising linearly?
• Among the replications that crossed, what is the mean observed lift, and why is it not zero?
• What does an O'Brien-Fleming boundary or an always-valid confidence sequence change about this simulation, and what does each cost in power?

Paying accounts with no active seat in 28 days

Medium · anti-join · null-semantics · retention-risk

dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.

Approach

1. Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
2. Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
3. Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
4. Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
5. Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.

Worked solution (25 min)

1. Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
2. Write the active-account spine and the 28-day activity CTE.
3. Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
4. Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
5. Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.

Expected result: One row per silent active account with seats_licensed and days_since_last_core_action, ordered by seats_licensed descending. Accounts that never emitted a core action show NULL and are reported as a separate group rather than as the worst churn risks.

Verify it yourself

• Silent accounts plus accounts with at least one active user equals the total count of active non-internal accounts.
• The NOT IN variant returns zero rows, which is the demonstration that a NULL is present.
• No account_id appears twice in the output.
• Every days_since_last_core_action is either NULL or greater than 28.

Follow-up

• How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
• Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
• Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?

Seven-day activation rate by weekly signup cohort

Easy · cohorts · activation · date-bounds

dim_user holds user_id, account_created_at_utc, is_internal. fct_event holds user_id, occurred_at_utc, is_core_action. A user is activated when core-action events fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Return, for the last twelve complete weekly signup cohorts, the cohort week, cohort size, activated users and the activation rate. Exclude is_internal users. Every signup in the cohort week stays in the denominator, including users who never returned.

Approach

1. Start from dim_user as the denominator spine with is_internal = FALSE and DATE_TRUNC('week', account_created_at_utc) as the cohort key. Driving the query from the event table instead would silently condition on having events and delete the entire non-activating population.
2. Join fct_event on user_id with is_core_action = TRUE and a per-user bound, occurred_at_utc >= u.account_created_at_utc AND occurred_at_utc < u.account_created_at_utc + interval '7 days'. The bound is correlated to each user's own signup timestamp, not a single global date range.
3. Aggregate per user with COUNT(DISTINCT occurred_at_utc::date) >= 2, then LEFT JOIN that back onto the spine and COALESCE the flag to FALSE so non-activators contribute a zero rather than vanishing.
4. Restrict the published cohorts to those whose week ended at least eight days ago. A cohort younger than that has not finished its seven-day window, so its rate is mechanically low and reads as a decline.
5. Roll up by summing the numerator and denominator per cohort week, and state the two-distinct-days threshold next to the number since it is a choice that re-bases the whole history if changed.

Follow-up

• Why two distinct days rather than one event? What happens to the published history if someone changes it to three?
• Invited seats and SSO-provisioned users get an account_created_at_utc at provisioning and may never sign in. Should they be in this denominator?
• The rate rose 3 points this week. What do you check before believing it?

How do you prioritize tasks when working on multiple projects?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. 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?

Suppose you are tasked with improving the accuracy of a content recommendation system. What steps would you take?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. 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?

How would you approach analyzing viewer engagement data for a new show?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. Name one primary metric, then the guardrail that stops it being gamed.

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?

Explain how you would design an experiment to test a new feature on the CNN website.

Medium · a/b testing and experimentation

Approach

1. Name the guardrails that would stop a launch even on a positive primary result.
2. Say whether units interfere with each other, and switch design if they do.
3. Name the randomisation unit first; it decides the variance and what the test can detect.

Follow-up

• What would you do if you could not randomise at all?
• How would you handle interference between treated and control units?

Explain the difference between supervised and unsupervised learning.

Medium

Approach

1. State your assumptions explicitly before working the problem.
2. Say what you would check first and why it is the highest-information step.
3. Clarify what is being asked and what a complete answer would contain.

Follow-up

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

Tell a novelty effect apart from a durable one

Medium · novelty effect · exposure cohorts · composition

A redesigned navigation shows a 6% lift in weekly core actions in calendar week one, 3% in week two and 1% in week three, on a continuously enrolling user-randomised experiment. Enrolment ran throughout, so later exposure cohorts are younger accounts. Using fct_experiment_exposure (unit_id, variant, first_exposed_at_utc), dim_user.account_created_at_utc and fct_event, determine whether this is novelty decay, a composition artefact, or a real effect that shrank, and state what evidence would settle it.

Approach

1. Separate calendar time from exposure time first. With continuous enrolment, calendar week two mixes units in their second week of exposure with units in their first, and the tenure mix changes daily, so a calendar trend is not evidence of decay.
2. Rebuild the readout as a triangle indexed on days since first_exposed_at_utc. For each exposure cohort compute the treatment effect at exposure-day k, then average across cohorts at fixed k using only cohorts that have reached k. Real decay shows as a falling curve in k; a composition artefact flattens out.
3. Use the one clean discriminator novelty offers: it requires a prior version to be novel against. Split on whether the unit existed before experiment start. Users who signed up afterwards never saw the old navigation, so a stable effect among them is not novelty, and an effect confined to pre-existing users is.
4. Check the primacy direction as well. An early negative effect that recovers is the mirror image and argues for shipping rather than against it, and it is invisible without the same exposure-indexed curve.
5. Say what would settle it. A long-run holdback, a few percent of traffic kept on control for eight weeks or more and read from exposure-week six onward. No three-week test can distinguish a decaying effect from one that has reached a lower plateau.

Worked solution (30 min)

1. Assign each exposed unit an exposure cohort from date(first_exposed_at_utc) and compute treated and control means per cohort per exposure-day k.
2. Average the per-cohort effects at each k only across cohorts that have observed day k, leaving later k blank rather than averaging over whoever has reached it.
3. Split the same triangle on pre-launch versus post-launch account_created_at_utc from dim_user.
4. Compare the curves: decay confined to the pre-launch split is novelty, decay in both is a real effect shrinking, and a flat exposure-time curve under a falling calendar curve is composition.
5. Recommend the holdback and state the earliest exposure-week at which a plateau claim becomes supportable.

Expected result: The decision rests on the exposure-indexed curve, not the calendar one. Novelty is indicated only when decay is present among units that existed before launch and absent among units created after it. Identical decay in both splits means a real effect shrinking; no decay in exposure time means the calendar trend was composition. A three-week test cannot separate decay from a plateau, and a long-run holdback can.

Verify it yourself

• Within a cohort, denominators must not fall as k grows. If they do, the population is being conditioned on survival rather than held fixed at exposure.
• Re-aggregating the exposure-indexed effects with the observed enrolment weights must reproduce the reported calendar-week effects, which proves the two views are the same data.
• Verify with a query that no unit in the post-launch split has a pre-launch session, otherwise that split is not clean and the discriminator is worthless.

Follow-up

• The holdback costs a few percent of traffic on the winning arm. How do you justify that, and when would you retire it?
• Weekly core actions per user is a ratio whose numerator and denominator live at different grains. Which standard error are you computing, and why is the naive one wrong?
• What would you conclude if the effect were 6%, 3%, 1% in exposure time as well, among post-launch signups only?

Decide whether a one-day core-action drop is real

Easy · seasonality · day-of-week · late-arriving-data

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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?

Suggested practice checklist

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

Day 1 — Diagnostic, scored before you study anything

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

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

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

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

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

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

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

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

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

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

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

Day 5 — The gap that is not a skill

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

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

Day 6 — Retest under day-one conditions

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

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

Day 7 — Full loop under interview conditions

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

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

Your part, not the team's

Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.

Describe a time when you had to work with a difficult team member. How did you handle it?

Medium · behavioural and stakeholder questions

Approach

1. Pick a story where you drove the decision, not one where you observed it.
2. Close with what you would do differently, concretely.
3. State the situation in two sentences and spend the rest on your reasoning.

Follow-up

• What did you decide not to do, and why?
• How did you know the outcome was caused by your change?

Give an example of a project where you had to take the lead. What was the outcome?

Medium · behavioural and stakeholder questions

Approach

1. Quantify the outcome, including what you would not claim credit for.
2. Close with what you would do differently, concretely.
3. Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that project again?
• How did you know the outcome was caused by your change?

Quantify your own impact without claiming the topline you touched

Hard · self-assessment · attribution · communication

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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?