Interview guides · Praescient Analytics

Praescient Analytics Data Scientist Interview Questions & Guide 2026

Timed data scientist practice for Praescient Analytics: short SQL and probability sets run end to end, then scored against a rubric.

The 60-second brief

A Data Scientist at Praescient Analytics serves as a vital bridge between complex, often unstructured data and actionable intelligence. You are not just building models; you are solving critical investigative and operational challenges for clients who operate in high-stakes environments. Your work directly impacts how organizations detect fraud, uncover patterns in massive datasets, and make informed decisions that have real-world consequences.

How much statistics you need depends on the flavour of the seat. Experiment-facing work wants you deep enough to notice that repeated looks at accumulating data inflate the false positive rate of a fixed-sample test; modelling-facing work wants estimation and honest uncertainty intervals.

Focus areas

Power experiments for heavy-tailed account revenue
Read NRR on a fixed account cohort
Analyse at the account grain, cluster errors

Overview

The role requires a rare combination of technical rigor and mission-focused pragmatism. Whether you are working on graph analytics for fraud detection or developing predictive models for public trust initiatives, you will be expected to translate ambiguous problems into structured, data-driven solutions. At Praescient Analytics, your success is defined by your ability to deliver high-quality analytical products that empower users to act with speed and confidence.

The Praescient Analytics interview process

Praescient Analytics candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
Whether you name what you have not done instead of stretching to cover every line of the posting
Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

Technical Assessments

Much 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

Interviews with Leaders

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.

Where candidates lose points

Computing monthly churn against the entire customer base when contracts are annual

An annual contract has no opportunity to churn except at its renewal date, so an account that is eleven months from renewal is in the denominator while being incapable of appearing in the numerator. The resulting rate is smaller than the real one by roughly the ratio of the base to the renewal-eligible base, and it oscillates with the seasonality of when deals were originally signed rather than with anything about the customers. The corresponding trap on the other side is counting a churn on the date the record was updated rather than on term_end_date, which shifts losses into whichever month the operations team did its paperwork.

Reading consumption metrics before the metering lag window has closed

Usage pipelines land late and correct themselves, which is exactly what is_restated and restated_at record. A dashboard queried on day T sees a partially populated tail for the last several days, so the most recent points always slope downward and always look like a regression. Analysts then explain the artefact, and sometimes ship a change to fix it. Establish the empirical settling time by measuring how much a given usage_date's total moves between first_written_at and its final value, exclude that many trailing days from every reportable figure, and never compare a fresh period against a settled one.

Reaching for a model before the target metric exists

Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.

Ignoring interference between units in a marketplace experiment

Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.

Questions & practice

How do you balance the need for high recall versus high precision in an investigative support system?

Medium · machine learning and modelling

Approach

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

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?

Walk me through the process of designing an A/B test for a new algorithmic recommendation engine.

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

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

Quantify billable volume created by retries after server errors

Hard · telemetry-hygiene · pandas · unit-economics

From fct_api_request (request_id, account_id, endpoint, idempotency_key, is_retry, http_status, request_at, billable_units), measure how much billable volume in a 28-day window is retry traffic that followed a 5xx. Group requests into attempt chains by (account_id, endpoint, idempotency_key) ordered by request_at; a request is error-driven if any earlier attempt in its chain returned 5xx. Requests with a null idempotency_key cannot be chained, so report them as their own class rather than assuming each is unique. Return billable_units split into first-attempt, error-driven retry, other retry and unchainable, per account.

Approach

Split the population before measuring anything. A null idempotency_key is not a chain of one, it is an unknown; report its share of billable_units first, because if it is 40% of volume then the headline estimate is a lower bound and the deliverable has to say so.
Within chainable rows, sort by (account_id, endpoint, idempotency_key, request_at) and derive 'any earlier attempt failed' with arithmetic rather than a per-group lambda: with is5 = (http_status >= 500), the per-chain cumsum minus the row's own value is positive exactly when an earlier attempt in that chain returned 5xx. A groupby-apply gives the same answer and is unusable at five million rows.
Do not take is_retry as the definition. It is set by the client whenever an idempotency_key is resent, which covers retries after client-side timeouts and after 4xx as well; compute the flag yourself and then cross-tabulate it against is_retry, because the disagreement is a finding in its own right.
Aggregate billable_units by (account_id, class) and assert the classes sum to each account's total. The spine sets billable_units to zero on 5xx responses, so the failed attempt contributes nothing and the whole inflation sits in the successful retry that follows it.
Report the per-account share and look at its distribution, not the fleet total. One account in a retry storm dominates any blended figure, which is the same failure that makes a fleet-wide error rate useless.

Worked solution (40 min)

unchainable = df.idempotency_key.isna(); report df.loc[unchainable].groupby('account_id').billable_units.sum() before proceeding.
keys = ['account_id','endpoint','idempotency_key']; c = df[~unchainable].sort_values(keys + ['request_at'], kind='mergesort'); c['is5'] = (c.http_status >= 500).astype(int)
c['attempt_no'] = c.groupby(keys, sort=False).cumcount(); c['prior_5xx'] = (c.groupby(keys, sort=False).is5.cumsum() - c.is5) > 0
c['cls'] = np.where(c.attempt_no == 0, 'first_attempt', np.where(c.prior_5xx, 'error_driven_retry', 'other_retry')); out = pd.concat([c, df[unchainable].assign(cls='unchainable')]).groupby(['account_id','cls']).billable_units.sum().unstack(fill_value=0)

Expected result: A per-account table of four columns summing exactly to that account's total billable_units in the window. error_driven_retry is zero for any account with no 5xx; the fleet-level error-driven share is typically low single digits while a small number of accounts sit far above it.

Verify it yourself

out.sum(axis=1) equals df.groupby('account_id').billable_units.sum() for every account.
Every row classed error_driven_retry has attempt_no >= 1.
An account with no http_status >= 500 in the window has error_driven_retry == 0.
Cross-tabulating cls against is_retry: no first_attempt row should carry is_retry True, and any that do mark chains truncated by the window boundary.

Follow-up

An account's error-driven share is 22%. Is that the platform's fault or the client's, and what do you look at next?
How would you define a consumption-based north-star metric that an outage cannot inflate?
Chains straddle the 28-day boundary. How large is that bias and in which direction?

How would you clean and join disparate datasets to prepare them for a graph-based analysis?

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.
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?
What breaks if events arrive late or out of order?

Write a query to identify top-performing users over a rolling 30-day window using SQL window functions.

Medium · sql and data manipulation

Approach

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.
Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

What breaks if events arrive late or out of order?
How would you verify this result without re-running the same query?

Running commitment burn-down and the date consumption crosses it

Medium · window functions · running totals · committed consumption

fct_subscription_period gives committed_amount_cents, term_start_date and term_end_date for each account's current version where pricing_model = 'committed_consumption'. fct_usage_daily gives account_id, workspace_id, sku_code, usage_date and net_amount_cents, at one row per workspace and SKU per day. Inside each account's current term, return the running total of net_amount_cents by usage_date, the first usage_date on which that running total reaches committed_amount_cents, and the fraction of the term elapsed at that point. Accounts that have not reached their commitment must still appear, with a null crossing date.

Approach

Collapse usage to one row per (account_id, usage_date) first. The fact is grained by workspace and SKU, so a raw running total leaves several rows per date and the first-crossing date becomes dependent on the arbitrary order of rows inside that day.
Restrict to the term with usage_date BETWEEN term_start_date AND term_end_date on the account's current row, so no prior term's consumption leaks into this term's burn-down.
Compute SUM(net_cents) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). Name ROWS explicitly: the default frame is RANGE, which includes all peer rows at the same ORDER BY value and would hand back the whole day's total on each of that day's rows.
Extract the crossing with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) grouped per account, which returns NULL for accounts still under commitment instead of dropping them.
Elapsed fraction is (crossing_date - term_start_date)::numeric / NULLIF(term_end_date - term_start_date, 0); Postgres date subtraction yields whole days, so the guard matters for same-day terms.
Exclude the trailing days still inside the metering settling window, measured from first_written_at against restated_at, and say how many days you cut and why.

Worked solution (30 min)

Build terms: SELECT account_id, committed_amount_cents, term_start_date, term_end_date FROM fct_subscription_period WHERE is_current AND pricing_model = 'committed_consumption' AND committed_amount_cents IS NOT NULL.
Build daily: join fct_usage_daily to terms on account_id with usage_date inside the term, then GROUP BY account_id, usage_date summing net_amount_cents.
Add running_cents with SUM(...) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).
Aggregate per account with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) AS crossing_date, then compute the elapsed fraction with the NULLIF guard.
LEFT JOIN that summary back onto terms so every committed account appears, and verify the last running_cents per account equals a plain SUM over the term.

Expected result: A per-account-per-day series of running_cents inside the current term, plus one summary row per committed account carrying crossing_date and term_elapsed_fraction, with crossing_date NULL exactly for accounts whose final running total is below committed_amount_cents.

Verify it yourself

The maximum running_cents per account equals SUM(net_amount_cents) over that account's term computed independently.
crossing_date is NULL if and only if the final running total is below the commitment.
term_elapsed_fraction falls in [0, 1] for every non-null crossing date.
running_cents is non-decreasing within each account, which fails immediately if a negative credit row was included without comment.

Follow-up

Turn this into an end-of-term overage forecast. What breaks if you extrapolate a linear run rate on a consumption product?
An account crosses its commitment at 40 percent of the term. Is that an expansion signal or a billing-surprise risk, and what would you check to tell them apart?

How would you design a metric to measure the effectiveness of a new fraud detection feature?

Medium · product sense and metrics

Approach

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

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

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

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

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?

How do you determine if your results are reaching statistical significance when dealing with low-volume data?

Medium · a/b testing and experimentation

Approach

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

How would you handle interference between treated and control units?
What would you conclude if the result is positive but the test is underpowered?

What are the most common experimentation pitfalls that lead to false positives?

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
State the primary metric and the minimum effect worth shipping, then size the test.
Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

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

Size an account-randomised test of a consumption feature

Easy · power · mde · heavy-tailed-outcomes

You have 2,800 non-internal paying accounts. The outcome is the monthly sum of billable_quantity for sku_code = 'compute_hours' from fct_usage_daily, with a mean of 640 and a standard deviation of 2,100 across accounts. Product wants to detect a 5% lift from a scheduling feature, randomised 50/50 at the account level, at 80% power and two-sided alpha 0.05. Give the minimum detectable effect the available sample actually supports, say whether the 5% target is reachable, and state what you would change in the design or the metric before agreeing to run it.

Approach

Write the two-sample formula and plug it before discussing anything else: MDE = (z_0.975 + z_0.80) * sd * sqrt(1/n1 + 1/n2) = 2.80 * 2100 * sqrt(2/1400) = 222 compute-hours, which is 34.7% of the 640 mean. The driver is the coefficient of variation of 3.3, not the account count, so quoting a sample size without it is not a power calculation.
Invert for the stated target. A 5% lift is 32 compute-hours, needing N = 4 * (2.80 * 2100 / 32)^2 = about 135,000 accounts, roughly 48 times the eligible population. Say the test is not powerable on this metric, and do not offer to run it longer: the account population is fixed, and extra weeks add repeated observations on the same clusters rather than new clusters.
Attack the variance instead of the sample. Pre-register winsorisation at the 99th percentile of the pre-period distribution, or move the estimand to a bounded one such as the share of accounts exceeding a usage floor. State explicitly that this changes the estimand rather than cleaning the data, because a cap chosen after seeing the result encodes the answer.
Add CUPED on the account's pre-period compute_hours. At a pre/post correlation of 0.80 the residual variance multiplier is 1 - 0.80^2 = 0.36, the standard error falls to 0.60 of its unadjusted value, and the MDE drops from 222 to 133 compute-hours (20.8% relative). That is still four times the requested 5%.
Fix the randomisation unit and the balance in the same breath: randomise account_id because colleagues share workspaces, and stratify assignment on pre-period usage decile so that a handful of very large accounts cannot land disproportionately in one arm and dominate the difference in means.
Close with an operating decision rather than a number. Run only if the team will act on a roughly 20% MDE after CUPED and winsorisation; otherwise re-specify the primary outcome as a bounded indicator and treat compute-hours as a secondary reported with intervals only.

Worked solution (25 min)

Compute the coefficient of variation, 2100 / 640 = 3.28, and check the top-decile share of total compute_hours before treating the mean as a summary of anything.
Compute MDE = 2.80 * 2100 * sqrt(1/1400 + 1/1400) = 2.80 * 2100 * 0.0378 = 222 compute-hours, 34.7% relative.
Invert to the required N for a 32-hour effect: 4 * (5883 / 32)^2 = about 135,000 accounts, 48x the population.
Recompute under CUPED at rho = 0.80: variance multiplier 0.36, SE multiplier 0.60, MDE 133 compute-hours, 20.8% relative.
Recompute on the binary variant at base rate 0.46 and write the recommendation as one decision with one operating point.

Expected result: MDE is about 222 compute-hours (34.7% relative) as specified and about 133 (20.8%) with CUPED at rho = 0.80. A 5% lift would require roughly 135,000 accounts, about 48 times the eligible population, so the test is declined on this metric and re-specified on a bounded outcome where the MDE falls to about 5.3 percentage points on a 0.46 base.

Verify it yourself

sqrt(1/1400 + 1/1400) = 0.0378, so the MDE must land within a few percent of 2.80 * 2100 * 0.0378 = 222. A materially different number means a one-sample or one-sided formula was used.
Required N scales with the inverse square of the effect size: halving the target MDE must quadruple N. If it does not, the formula is wrong.
The CUPED variance multiplier is 1 - rho^2, never 1 - rho. At rho = 0.80 the standard error falls by 40%, not by 20%.

Follow-up

The feature only applies to the 310 accounts on committed_consumption contracts. What is the MDE on that subset, and does it change the recommendation?
Suppose the primary outcome becomes a binary indicator, account exceeded 100 compute-hours in the month, at a base rate of 0.46. What is the MDE now, and why did it improve so much?
What would make you stop this test early, and how do you write that rule so it is not peeking?

Error rate halves while severe support tickets double

Hard · missing data · aggregation bias · reconciliation

The fleet-wide customer-visible error rate fell from 1.8 percent to 0.9 percent, while sev1 and sev2 tickets and reopened_count rose across the same fortnight. Using fct_api_request (account_id, environment, sdk_name, traffic_class, http_status, request_at, billable_units), fct_usage_daily (account_id, sku_code, usage_date, billable_quantity) and fct_support_ticket (account_id, severity, opened_at, reopened_count, linked_incident_id), determine whether reliability improved, and if not, identify precisely which rows are missing and from when. Deliverable: a diagnosis backed by an independent corroborating source.

Approach

Distrust an improvement that contradicts an independent operational signal. Two sources disagreeing is itself the finding; decide which one is more likely to be broken before explaining either.
Recompute the rate as the metric tree defines it, per account first and then as the share of accounts above the reliability target. A single global average is dominated by whichever account sends the most traffic, so a fleet number can fall while a quarter of accounts get worse.
Audit for missingness rather than for badness: count fct_api_request rows per hour split by environment, sdk_name and status class, indexed against the trailing same-hour baseline. A partial ingestion failure shows as a step drop confined to one slice, not a uniform decline.
Reconcile against a source the request pipeline does not feed, such as implied request volume from fct_usage_daily for the same accounts and dates. If the usage table is flat while request rows fell, rows are missing rather than traffic.
Test whether the missingness is differential by status, which is the mechanism that fakes an improvement: if 5xx rows are written on a path that stopped while 2xx rows were unaffected, the numerator falls faster than the denominator and the rate drops with nothing improving.
Close with the affected window, the affected slice and a restated series marked unreliable across that window, rather than a silently patched number.

Follow-up

The missing rows are unrecoverable. How do you present that fortnight in a series people compare week over week?
What monitor would have caught this within an hour, and what is its false-positive cost on a normal quiet weekend?

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

Rigour against a deadline

Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.

Describe a situation where you had to pivot your approach due to shifting project requirements.

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
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.

Follow-up

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

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?

State the measured impact of your own work honestly

Hard · impact · causal inference · self-assessment

You are asked for the business impact of a renewal-risk worklist you shipped nine months ago. Customer success used it, and renewals in the covered segment came in 6 points above the prior year. Coverage was assigned by the team itself: they worked the top of your list and also the accounts they were already worried about. Produce the impact claim you are willing to defend, the number you refuse to claim, and the design you would have asked for at the start.

Approach

The interviewer is probing whether you can separate what you shipped from what you caused, and whether you would have built the measurement in rather than reconstructing it afterwards. Both halves are being scored.
Name the confound precisely. Coverage assignment is doubly selected: the largest accounts get an owner because they are valuable, and distressed accounts get one because they are at risk. The naive covered versus uncovered comparison mixes a strong positive selection with a strong negative one and can come out with either sign depending on which rule dominated. Matching on account size does not fix it, because the risk signal that triggered coverage is the same signal that predicts the outcome.
Split the claims by what each needs to be true. Ranking quality is defensible from precision at k on out-of-time renewals. Adoption is defensible from timestamps showing what share of listed accounts were contacted. The outcome claim is not defensible without a design, and saying so is the point of the exercise.
Look for identification before giving up on it. A capacity cut-off, a territory boundary, or a period in which the list existed but was unstaffed can assign coverage for reasons unrelated to account health, and any of those supports a bounded estimate.
State the design you would ask for now and its price: a randomly withheld slice of the list, held for two renewal quarters, with the expected cost in renewals stated openly. That cost is what it takes to be able to answer this question at all.
Give a bounded number rather than none. Six points with an explicit statement of how much of it you can attribute is more useful than either claiming the whole figure or declining to quantify anything.

Follow-up

Your manager wants the 6 points in a promotion packet. What wording do you accept, and what do you strike?
What would have had to be true for the naive covered versus uncovered comparison to be valid?
If the holdout costs the team real renewals, how do you justify asking for it, and to whom?