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Zeta Data Scientist Interview Questions & Guide 2026

Zeta data scientist prep on data pitfalls: selection bias, target leakage, and checking an outlier's provenance before you drop it.

The 60-second brief

The Data Scientist role at Zeta is a high-impact position that sits at the intersection of complex data engineering and strategic product decision-making. You will be responsible for building robust models, designing experiments that drive product growth, and translating ambiguous business problems into actionable technical roadmaps. Because Zeta operates at significant scale, your work directly influences the performance of internal platforms and the end-user experience, making your ability to bridge the gap between technical rigor and business outcomes essential.

Coding rounds for this role are usually data-manipulation shaped rather than data-structure shaped: group-bys, joins, time windows, ranking within a partition. Confirm the format before spending a week on graph traversal.

Focus areas

Power experiments for heavy-tailed account revenue
Read NRR on a fixed account cohort
Strip CI, retry and synthetic traffic first

Overview

You will work within cross-functional squads, collaborating closely with engineering and product management teams. Whether you are optimizing recommendation systems or diagnosing sudden shifts in product metrics, your contribution is expected to be foundational. The environment is fast-paced and demands a high degree of autonomy; you will not just be analyzing data, but actively shaping the product strategy through rigorous experimentation and statistical analysis.

The Zeta interview process

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

Technical Screening

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

Model Design Round

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

What to demonstrate

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

How to prepare

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

Statistical Theory Round

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

Team Fit Interview

This conversation decides whether you can be handed a problem nobody has finished defining and left alone with it for a few weeks. The manager is listening for how you behave when the brief is thin: what you clarify before starting, and what you settle on your own rather than escalating. Most candidates over-index on technical depth here and under-describe the decisions they actually owned. Say who wanted the work, what you chose not to do, and where you would have stopped and asked. A clean account of your own judgement carries this round further than a longer project list.

What to demonstrate

Whether you can name a decision that was yours alone, as opposed to one the team arrived at
How you respond to a request that arrives with no success metric attached to it
Whether the effort you estimate for a piece of work matches the work you just described doing
What you escalate, and how long you sit on a problem before you do

How to prepare

For each project you plan to raise, write one sentence saying what would not have happened if you had not been on it, and check that the sentence is about an outcome rather than an artefact
List the decisions in your last project that were genuinely yours, and for each one write down the option you rejected and why
Prepare the project that went badly: the point at which you knew, who you told, and what it cost before it was caught

Final Leadership Interviews

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What to demonstrate

Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
Whether you ask what decision is actually on the table before explaining anything

How to prepare

Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else

Where candidates lose points

Reporting a mean over accounts when account revenue is heavy-tailed

When a small number of accounts hold most of the revenue, the sample mean is dominated by whichever of them happens to be in the sample, and the sample variance keeps growing as more data arrives instead of stabilising. In that regime the usual central-limit-based confidence interval understates uncertainty, and a single renewal or a single large account's batch job can flip the sign of a measured effect. The fixes are to pre-register a winsorisation or capping rule before looking at the outcome, to report account counts crossing a threshold alongside the revenue figure, or to define the estimand on a bounded transform. Choosing the cap after seeing the result is a separate and worse problem, because the cap then encodes the answer.

Treating raw request or usage volume as engagement

Most traffic in this domain is emitted by machines. Continuous-integration pipelines, scheduled batch jobs, synthetic monitors, backfills and client retries can all grow by an order of magnitude from one configuration change made by one engineer, and none of it represents a new decision to use the product. The inversion is what makes it dangerous: when the platform degrades, clients retry, so error-driven retry volume rises at the exact moment the customer is most likely to leave, and an engagement dashboard built on raw counts shows growth immediately before a churn. Filter on traffic_class and on successful status before anything else, and keep failed-request volume as its own separate series.

Analysing at a different unit than the one randomised

Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.

Generalising beyond the population the sample actually supports

State the frame the sample was drawn from and where it diverges from the population you want to talk about: time window, platform, geography, opt-in. If a group is excluded from the frame, either weight to known margins or narrow the claim rather than quietly extending it.

Questions & practice

How would you handle null values when joining large datasets for model training?

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.

Follow-up

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

Simulate how the renewal calendar distorts monthly churn rates

Medium · simulation · churn · numpy · cohorts

Simulate 1,200 accounts on annual contracts. Draw each account's renewal month from a deliberately lumpy calendar: 30% renew in January and the remaining 70% are spread evenly over the other eleven months. At each renewal an account churns with probability 0.18, independent of month; survivors renew and come back twelve months later. Run 24 simulated months. For each month compute two rates: churned accounts over all live accounts, and churned accounts over accounts whose term ended that month. Report the mean and the month-to-month standard deviation of each series, and state which one belongs in an executive summary.

Approach

Build the panel with numpy state arrays rather than a per-account loop: a next_renewal_month vector, an alive boolean vector, and a loop over the 24 months only. Looping over 24 months is fine; looping over 1,200 accounts inside it is what makes the simulation too slow to iterate on.
Maintain the live set honestly. An account that churned in month m must leave the denominator from m+1 onward and can never be renewal-eligible again; if it stays, the naive rate drifts downward for reasons that have nothing to do with churn and the calendar effect gets buried.
Compute both series over the same months and compare dispersion, not only level. The eligible-base rate should sit near 0.18 with binomial noise scaled by that month's renewal count; the naive rate spikes in January and collapses in thin months.
Quantify the gap instead of describing it: the ratio of the two means is roughly the reciprocal of the average monthly renewal-eligible fraction, and the naive series' standard deviation is driven by the signing calendar rather than by customer behaviour.
Check against the closed form before trusting the output. With the live set maintained correctly the eligible-base rate is an unbiased estimator of 0.18 in every month, so a systematic offset means the bookkeeping is wrong, not that the simulation found something.

Follow-up

Compounded over twelve months the naive rate lands close to the true annual churn. Does that rescue it?
How would you report churn in a month where only nine accounts were renewal-eligible?
Eighteen-month terms are now being sold alongside annual ones. What breaks?

Audit daily usage rows for grain and arithmetic violations

Easy · data-quality · pandas · grain

You are handed fct_usage_daily as a pandas DataFrame with account_id, workspace_id, sku_code, usage_date, billable_quantity, included_quantity_applied, overage_quantity, list_amount_cents, discount_amount_cents, net_amount_cents, cogs_cents, is_restated, first_written_at and restated_at. The declared grain is one row per (account_id, workspace_id, sku_code, usage_date). Write audit(df) returning a DataFrame with one row per failing check: check name, failing row count, and one example key. Cover at minimum grain duplication, negative quantities or amounts, the identity net = list - discount, billable = included + overage, and rows where is_restated is true but restated_at is null.

Approach

Check the grain before anything else with df.duplicated(subset=key, keep=False), and count rows rather than groups so a key appearing twice contributes 2 — if the grain is broken every arithmetic count below it is uninterpretable.
Express each invariant as a boolean Series over the whole frame. The cent columns are integers and compare exactly, so use !=; the numeric(18,6) quantity columns need np.isclose with atol=1e-6 because included + overage is a decimal sum.
Handle null as its own failure mode. Comparisons against NaN return False, so a check written as rows_that_pass = (a == b - c) silently files every null-amount row wherever the negation happens to land; build each check as violations = ~condition | column.isna().
Collect the checks as a list of (name, mask) pairs and assemble the output in one pass, so adding a check is one line and every check reports in the same shape.
Order the output with structural failures (grain, null keys) above arithmetic failures, and report zero-count checks too — a check that silently disappears when it passes is indistinguishable from a check that was never run.

Worked solution (20 min)

key = ['account_id','workspace_id','sku_code','usage_date']; dup = df.duplicated(key, keep=False); record dup.sum() and df.loc[dup, key].iloc[0].to_dict() as the example.
neg = (df[['billable_quantity','included_quantity_applied','overage_quantity','list_amount_cents','net_amount_cents','cogs_cents']] < 0).any(axis=1); net_bad = df.net_amount_cents.isna() | (df.net_amount_cents != df.list_amount_cents - df.discount_amount_cents).
qty_bad = ~np.isclose(df.billable_quantity, df.included_quantity_applied + df.overage_quantity, atol=1e-6) | df.billable_quantity.isna(); restated_bad = df.is_restated & df.restated_at.isna().
Assemble pd.DataFrame([{'check': n, 'failing_rows': int(m.sum()), 'example': first_key(m)} for n, m in checks]) with the grain and null checks listed first.

Expected result: One row per check with failing_rows as an integer count of rows rather than groups, and a JSON-serialisable example key for any check with a non-zero count; a single doubled key contributes 2 to the grain check.

Verify it yourself

df.groupby(key).size().max() == 1 if and only if the grain check reports zero.
When the net check reports zero, sum(net_amount_cents) == sum(list_amount_cents) - sum(discount_amount_cents) exactly.
Running audit on df.sample(frac=1, random_state=0) gives identical counts; the checks must not depend on row order.
Corrupting one net_amount_cents by a single cent raises exactly one check's count by exactly 1.

Follow-up

Which of these should block a dashboard refresh and which should only warn?
Rows with is_restated = true legitimately change value after first write. How do you make yesterday's audit result reproducible?
How would you extend this to catch a partition that is missing entirely rather than wrong?

Use SQL window functions to calculate rolling averages for user activity.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which table is the grain you start from, and join outward from it.

Follow-up

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

Explain how you would optimize a slow-running query on a massive database table.

Medium · sql and data manipulation

Approach

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

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?

Net revenue retention on a cohort frozen twelve months back

Hard · retention · as-of join · cohort arithmetic

fct_subscription_period carries subscription_period_id, account_id, arr_cents, plan_code, term_start_date, term_end_date, booked_at, amendment_type, superseded_by_id (the subscription_period_id of the version that replaced this one, null on the live version of a lineage) and is_current. Compute net revenue retention for month M: the summed arr_cents at M for the set of accounts holding arr_cents > 0 at M-12, divided by that same set's arr_cents at M-12. An account can hold more than one live subscription, a churned account contributes zero rather than dropping out, and nothing signed after M-12 may enter either side. Return the ratio plus the expansion, contraction and churn components in cents.

Approach

Write one reusable as-of ARR snapshot parameterised by a date: rows whose term brackets the date AND whose booked_at is at or before the date, then only the version of each lineage that is still live at that date, then sum arr_cents per account. The booked_at guard matters because an amendment signed in advance otherwise co-exists with the term it replaces and double counts the account.
Collapse the lineage on superseded_by_id, not on any attribute of the contract. Keep a row when superseded_by_id IS NULL, or when the successor it points at was booked after the date. Ranking with ROW_NUMBER() OVER (PARTITION BY account_id, plan_code ...) instead is wrong in both directions: an amendment that moves the account from one plan_code to another puts the old and new versions in different partitions, so both are rank 1, both bracket the date, and the account's arr_cents is counted twice; and two genuinely concurrent subscriptions that happen to share a plan_code land in one partition, so one of them is deleted.
Resolve the successor with a LEFT JOIN back to fct_subscription_period on subscription_period_id, and treat a missing successor as not superseded. An inner join would silently delete an account's ARR on a dangling pointer, which is a data-quality bug in the source, not a retention movement.
Never use is_current for the M-12 side. is_current describes today; using it at the historical snapshot backdates the present contract onto last year's cohort and makes retention look like 100 percent by construction.
Freeze the cohort from the M-12 snapshot where arr_cents > 0, then LEFT JOIN the M snapshot onto it and COALESCE the missing side to zero. An inner join deletes exactly the churned accounts, which is the single largest way this number gets overstated.
Return a ratio of sums, not a mean of per-account ratios. The two are different estimands: contraction is floored at zero while expansion is unbounded, so the mean of ratios is both biased relative to the aggregate and far noisier on a skewed revenue base.
Decompose per account on the delta: positive delta is expansion, negative delta with a non-zero M value is contraction, a zero M value is churn. The three components must reconcile to numerator minus denominator.
Prove no leakage: any account whose first contract began after M-12 must be absent from both sides, and the cohort row count must be identical in the numerator and denominator.

Worked solution (45 min)

Write arr_asof(d) as a CTE or lateral: from fct_subscription_period s take rows with term_start_date <= d AND term_end_date >= d AND booked_at <= d, LEFT JOIN fct_subscription_period succ ON succ.subscription_period_id = s.superseded_by_id, keep the row when s.superseded_by_id IS NULL OR succ.subscription_period_id IS NULL OR succ.booked_at > d, then sum arr_cents per account_id across every surviving version.
Sanity-check the lineage rule on one amended account before going further: at a date after the amendment, the account must contribute exactly one version per lineage even when the amendment changed plan_code, term dates or both.
Materialise base = arr_asof(M-12) filtered to arr_cents > 0, and curr = arr_asof(M).
LEFT JOIN curr onto base on account_id and COALESCE(curr.arr_cents, 0) AS arr_now.
Compute nrr = sum(arr_now)::numeric / NULLIF(sum(base.arr_cents), 0), and the three components with SUM(...) FILTER on the sign of arr_now - base.arr_cents and on arr_now = 0.
Reconcile: assert sum(arr_now) - sum(base.arr_cents) = expansion - contraction - churn, and assert the cohort account count is identical on both sides.

Expected result: A single row with nrr as a ratio, plus expansion_cents, contraction_cents and churn_cents as positive magnitudes satisfying numerator minus denominator equals expansion minus contraction minus churn, over a cohort whose membership is fixed at M-12.

Verify it yourself

At each snapshot date, the surviving row count per account equals the number of distinct live subscription lineages on that account, not the number of contract versions bracketing the date.
For an account whose amendment changed plan_code, the snapshot returns one version and its snapshot ARR equals the amended arr_cents; swapping in a ROW_NUMBER partition on (account_id, plan_code) returns two versions for that account and inflates the side it sits on, which is the fastest way to see the bug.
The three components reconcile to the numerator-denominator delta to the cent.
The distinct account count is the same in the numerator and the denominator.
No account with a first term_start_date after M-12 appears anywhere in the output.
Re-running after new accounts sign leaves the ratio unchanged, which is the defining property of a frozen cohort.

Follow-up

Net revenue retention can rise while the business shrinks. Show one mechanism and name the guardrail that catches it.
How do you handle an account that co-terms two subscriptions into one mid-window, so the subscription count changes but the money does not?
Finance computes this from invoiced amounts and gets a different number. Which is right for which question?

If we notice a sudden drop in a key product metric, how would you go about diagnosing the root cause?

Medium · product sense and metrics

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.
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?

How would you design a recommendation system to increase user engagement on our platform?

Medium · product sense and metrics

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.
Restate the decision this analysis has to support, and who acts on the answer.

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?

Given a specific product goal, how do you prioritize which metrics to track?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
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.

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?

How would you define the success metrics for a new feature launch?

Medium · product sense and metrics

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.
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?

How do you determine the required sample size for statistical significance in an A/B test?

Medium · a/b testing and experimentation

Approach

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.
State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

What would you conclude if the result is positive but the test is underpowered?
What would you do if you could not randomise at all?

Explain the difference between frequentist and Bayesian approaches to A/B testing.

Medium · a/b testing and experimentation

Approach

Say whether units interfere with each other, and switch design if they do.
State the primary metric and the minimum effect worth shipping, then size the test.
Name the guardrails that would stop a launch even on a positive primary result.

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?

Design the retention metric suite for a quarterly board pack

Hard · net revenue retention · cohort arithmetic · as-of joins · gaming resistance

You own the retention numbers in the quarterly board pack. Build them from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, booked_at, is_current, superseded_by_id) rather than from invoices. Deliver net revenue retention over a trailing twelve months, the guardrails that stop it being satisfied by shrinking the business, and the reporting lag you will enforce. Specify the cohort rule, state whether you report a ratio of sums or a mean of per-account ratios and why, and name two ways the headline number rises while the business gets worse.

Approach

Freeze the cohort at month M-12: the set of accounts with ARR above zero on that date. No account acquired after M-12 enters either side of the ratio. That constraint is the entire point, because it makes the number a statement about the installed base rather than about how well sales did last quarter.
Read arr_cents from the fct_subscription_period version that was live on each of the two dates, selected by term_start_date and term_end_date bracketing the date, never by is_current, which silently rates last year's revenue at this year's plan. Invoiced amounts move with billing_frequency and prepayment and will not reconcile to a contract-derived figure, so pick one source and say which.
Report the ratio of sums: total arr_cents at M over the frozen cohort divided by total at M-12. The mean of per-account ratios is a different estimand and much noisier on a few thousand accounts, because contraction is bounded below at zero while expansion is unbounded, so a handful of large expansions dominate the mean.
Attach the two guardrails that close the two gaming routes. Gross logo retention on the renewal-eligible base catches raising the ratio by declining to serve the churn-prone segment, because it is computed only over accounts that actually had an opportunity to leave. New-logo ARR reported beside it exposes a contracting top of funnel that the ratio is structurally incapable of seeing.
Enforce the lag and the restatement policy. A 45-day grace on term_end_date for late paperwork means the most recent 45 days are never reportable, and a month is openly restated when the grace closes rather than quietly corrected between board packs.

Worked solution (40 min)

Build an as-of ARR resolver: for a date D and an account, select the fct_subscription_period row where term_start_date <= D and term_end_date >= D, breaking ties on the latest booked_at so a superseded version never wins.
Form the cohort at M-12 as accounts with as-of ARR above zero, then compute both sums with that account set held fixed.
Compute the same quantity as a mean of per-account ratios and record the gap between the two.
Build gross logo retention over accounts with term_end_date in each month, with the 45-day grace applied, and pull new-logo ARR from amendment_type = 'new' by booked_at.
Recompute net revenue retention with the five largest accounts removed and report it beside the headline.

Expected result: Net revenue retention as a ratio of sums on a cohort frozen at M-12, reported beside gross logo retention on the renewal-eligible base and new-logo ARR, with the most recent 45 days marked not reportable. The answer names at least two ways the headline rises while the business worsens: ceasing to sell into the churn-prone segment, and expansion concentrated in a few accounts masking broad contraction underneath.

Verify it yourself

The cohort account count must be identical at M and at M-12; a larger count at M means accounts acquired inside the window leaked into the numerator.
A churned account must contribute zero to the numerator and its full M-12 ARR to the denominator, not be dropped from both, which would silently raise the ratio.
Net revenue retention recomputed without the five largest accounts must be reported; a headline that moves by more than a few points under that removal is a statement about those accounts rather than about the base.
Summed as-of ARR must not exceed the sum over is_current rows by more than the amendment rate; a large excess means the as-of join is returning several contract versions per account.

Follow-up

Compute net revenue retention both ways on the same cohort. What does a large gap between the ratio of sums and the mean of per-account ratios tell you about the shape of the expansion distribution?
A ramp deal is signed in March and starts in July. Which month does it belong to on the board's sales-effectiveness page, and which on this one?

Net revenue retention jumps sixteen points in one month

Medium · versioned tables · fan-out · metric definition

Trailing-twelve-month net revenue retention printed around 108 percent for months and now reads 124 percent, with no unusual deals closed. The query sums arr_cents from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, superseded_by_id, is_current, booked_at) filtered on is_current = true at month M, across accounts holding arr_cents > 0 at month M-12. Find the defect, correct the number, and rewrite the definition so the next person cannot reintroduce it.

Approach

Audit the grain before the arithmetic: count account_ids holding more than one row with is_current = true and superseded_by_id null. A versioned contract table that double counts one amendment batch inflates the numerator while leaving the denominator untouched.
Replace is_current with an as-of selection on both dates, taking the version whose term_start_date and term_end_date bracket the reporting date and tie-breaking on latest booked_at. The numerator is read as of M and the denominator as of M-12; neither uses today's live version.
Verify the cohort is frozen. The account set is fixed at M-12 and nothing acquired since may enter the numerator, so check that no join to a current-period table quietly re-admits new accounts.
Confirm the estimand is a ratio of sums rather than a mean of per-account ratios. Contraction is floored at zero while expansion is unbounded, so the two constructions differ systematically and the second is far noisier.
Reissue the definition with the failure modes written into it: exactly one row per account per date by construction, cohort frozen at M-12, churned accounts contributing zero rather than dropping out of the numerator.

Follow-up

A churned account should contribute zero rather than disappear. What does the ratio do under each treatment, and which one is correct?
How would you unit-test this metric so a future amendment batch with the same defect fails a check instead of reaching a board slide?

Suggested practice checklist

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.

Day 1 — Design 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.

Day 2 — Power 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.

Day 3 — Variance 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.

Day 4 — Validity 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.

Day 5 — When 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.

Day 6 — The 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.

Day 7 — Present 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.

Bring numbers, including the ones you are unsure of

An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.

How do you handle situations where your experiment results are inconclusive?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Defend a churn number twelve times the one in the board deck

Hard · stakeholder management · retention metrics · definitions

You recompute logo churn on the renewal-eligible base from fct_subscription_period, counting only accounts whose term_end_date fell in the month and allowing a 45-day grace for late paperwork. Annualised, about 14 percent of accounts that reach a renewal date do not renew. A revenue leader has been quoting 1.2 percent to the board for two quarters, computed by dividing non-renewed accounts by the entire customer base in each month and printing that monthly figure with no period attached. You have 20 minutes with that leader and the finance lead. Decide which number is reported from now on, and what happens to the two quarters already published.

Approach

The interviewer is probing whether you can hold a correct definition under social pressure without turning it into a competence dispute. Open by reproducing their 1.2 percent exactly, with their denominator and their months, so the disagreement is arithmetic both sides can see rather than a claim about who was careless.
Separate the two defects, because they are different in kind. The denominator is wrong: on annual contracts only about one twelfth of the base reaches a renewal date in any month, so an account eleven months from renewal sits in the denominator while being structurally incapable of entering the numerator, which suppresses the rate by a factor near twelve. The period is merely unstated: a monthly figure printed beside annual revenue targets gets read as an annual rate.
Say out loud that those two defects nearly cancel in the level, before the leader finds it. Twelve times 1.2 percent is about 14 percent, which is your number. That is the strongest thing you can say in the room, because it proves both figures rest on the same non-renewal count and moves the meeting onto which denominator and which period get published rather than onto whose query is right.
The level is recoverable; the series is not. Non-renewals in a month are the eligible base for that month times the churn rate, so dividing by a fixed whole base makes the published line proportional to how many contracts happen to come up that month. Where signings cluster at quarter ends, the eligible base in a quarter-end month can be several times a quiet month's, and the month-over-month moves the board has been reading as satisfaction are the signing calendar.
Separate the measurement change from a business change. Nothing got worse this week; the loss rate was always this. Bring net revenue retention over the same period as a ratio of sums on a cohort frozen twelve months earlier, because logo churn concentrated in small accounts can sit beside healthy revenue retention, and that combination is the actual story.
Offer a migration path rather than a correction. Report both rates for one quarter with a written bridge, restate the prior two quarters in an appendix instead of silently, and pin the definition, including the period it is stated over, somewhere finance and product both read it. Concede the limits of your own number: the 45-day grace means the most recent 45 days are not reportable, and churn must be dated on term_end_date rather than on updated_at. A strong answer volunteers this; a generic one only defends.

Follow-up

The leader multiplies their monthly figure by twelve, lands on your annual number, and concludes nothing was ever wrong. What do you say?
The leader says publishing the corrected rate costs the team its credibility with the board this quarter. What do you do?
Gross logo retention worsened while net revenue retention improved. Which do you lead with, and what does the combination tell you about who is leaving?

Report an underpowered consumption test to a non-technical executive

Medium · communication · uncertainty · experimentation

An account-randomised packaging change ran six weeks across 900 paying accounts. The effect on billable units per account per month is plus 4.1 percent, with a 95 percent interval from minus 3.2 to plus 11.8 after clustering standard errors at the account and applying the pre-registered winsorisation at the 99th percentile. An executive with no statistical background wants one number this week to decide a full rollout. Produce a three-sentence spoken answer, one chart, and an explicit recommendation of ship, stop or keep running, with the cost of each option stated.

Approach

The interviewer is probing whether you can be decision-useful without either hiding the uncertainty or hiding behind it. Start from the decision rather than the statistics: establish what the executive would do differently at plus 4 percent versus zero, because if the action is identical the interval does not matter.
Translate the interval into consequences in units the executive already reasons about. Multiply both endpoints by the cohort's baseline consumption and contracted rates to give an annualised revenue range, so the answer is a range of dollars rather than a range of percentages.
Price the option to wait. Using the observed variance, state roughly how many additional account-weeks halve the interval width, so keep running becomes a quantified choice instead of a stall.
Offer a cheaper path to the same decision: a lower-variance proximate outcome such as successful billable units on the new SKU, or CUPED using each account's pre-period consumption, quoting the expected variance reduction as one minus the squared pre-post correlation.
Give a recommendation and name the single observation that would reverse it. A strong answer commits; a generic one recites the interval and leaves the decision on the table.

Follow-up

The executive says it clearly works and is just not provable, so ship it. What is your answer?
How much of the interval width comes from clustering and how much from the revenue tail, and what would you do about each?
If you had to ship this week with no more data, which guardrail would you watch for the first fortnight and at what threshold would you roll back?