Interview guides · ADP

ADP Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

As a Data Scientist at ADP, you operate at the intersection of massive-scale human capital management and cutting-edge artificial intelligence. You are tasked with analyzing millions of employee records, payroll transactions, and HR data points to build predictive models, recommendation systems, and user-facing features that impact millions of workers daily. Your work directly drives product transformation, optimizes client experiences, and shapes business strategy across global markets.

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
Separate contracted seats from actively used seats
Measure churn only on renewal-eligible accounts

Overview

This role requires a unique blend of technical depth and product intuition. Whether you are building Large Language Model architectures for natural language understanding, refining machine learning pipelines, or analyzing consumer conversion funnels, your insights guide executive decision-making. You will collaborate closely with product managers, data engineers, and software developers to take models from concept to robust, production-ready environments.

Working at ADP means tackling complex challenges with high visibility and enterprise scale. You will find an environment that values continuous learning, technical rigor, and courageous collaboration. While the pace can be demanding, the scale of data and the opportunity to influence the future of work make this position exceptionally rewarding for ambitious data professionals.

The ADP interview process

ADP candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screening Call

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 Assessments

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Senior Interviews

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.

Behavioral Evaluations

This round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.

What to demonstrate

Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
The counterfactual: what would have been done without your analysis, and why that default was worse
How far your involvement ran past the handoff, and whether you checked that the change did what you predicted

How to prepare

Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.

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.

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

Questions & practice

How would you define core health metrics for a newly deployed machine learning model in production?

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.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Implement seven-day activation rate from its written definition

Easy · metrics · pandas · activation

Given dim_account (account_id, created_at, is_internal, is_current) and fct_api_request (account_id, request_at, http_status, api_key_id, traffic_class), implement activation_rate(accounts, requests, week_start). Definition: the numerator is accounts whose first request with http_status < 400, api_key_id not null and traffic_class != 'synthetic_monitor' occurs no later than 168 hours after created_at; the denominator is non-internal accounts created during the ISO week starting week_start. All timestamps are tz-aware UTC. Return the rate and both counts, and refuse to report a week until every account in it has had its full 168 hours.

Approach

Reduce dim_account to one row per account_id before joining anything. It is a type 2 dimension, so several versions of the same account exist; joining the versioned table to requests multiplies the denominator by the number of plan changes an account happened to make.
Build the denominator first and freeze it: is_internal == False and week_start <= created_at < week_start + 7 days. Everything after this is a filter on the numerator only, because an account that never sent a request must still sit in the bottom of the fraction.
Filter requests to qualifying rows and only then take groupby('account_id').request_at.min(). The first qualifying request is not the same object as the global first request filtered afterwards, and the two answers differ for every account whose first call was a 4xx.
Left-join the first qualifying timestamp onto the cohort and test (first_ok - created_at) <= Timedelta(hours=168). NaT propagates to False in that comparison, which is the behaviour you want, but assert it rather than assume it.
Guard reportability explicitly: if week_start + 7 days + 168 hours exceeds the maximum request_at in the data, the week is censored and will read as a drop, so return None or raise rather than emit a number.

Worked solution (20 min)

cohort = accounts.loc[~accounts.is_internal & accounts.created_at.between(week_start, week_start + pd.Timedelta(days=7), inclusive='left'), ['account_id','created_at']].drop_duplicates('account_id')
ok = requests[(requests.http_status < 400) & requests.api_key_id.notna() & (requests.traffic_class != 'synthetic_monitor')]
first_ok = ok.groupby('account_id', as_index=False).request_at.min(); m = cohort.merge(first_ok, on='account_id', how='left'); assert len(m) == len(cohort)
activated = (m.request_at - m.created_at) <= pd.Timedelta(hours=168); return {'rate': float(activated.mean()), 'numerator': int(activated.sum()), 'denominator': len(m)}

Expected result: A dict whose denominator is the count of distinct non-internal accounts created in that ISO week, numerator <= denominator, and rate = numerator / denominator; accounts with no qualifying request sit in the denominator with NaT and evaluate to False.

Verify it yourself

len(m) == cohort.account_id.nunique(); the merge must not add rows.
Dropping the synthetic_monitor filter can only raise the numerator, never lower it.
Widening the window from 168h to 336h is monotone: the numerator must not decrease.
An account whose only qualifying request falls at exactly created_at + 168h counts as activated, since the comparison is inclusive.

Follow-up

Median time-to-first-call is more informative. What breaks if you take the median over activated accounts only, and what estimator fixes it?
How would you decide whether 168 hours is the right window rather than 72 or 336?
An account signs up, does nothing for 20 days, then integrates heavily. Where does it land in this metric, and is that what you want?

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.

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?

Write a query using SQL window functions to calculate rolling averages of client payroll transactions over a twelve-month period.

Medium · sql and data manipulation

Approach

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

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?

Given a large database of user interactions, write a query to identify drop-off points in the client onboarding journey.

Medium · sql and data manipulation

Approach

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

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?

Seven-day activation rate by signup cohort week

Easy · funnel · cohorts · censoring

dim_account carries account_id, created_at, is_internal and is_current; fct_api_request carries account_id, request_at, http_status, api_key_id and traffic_class. Build a signup cohort by the ISO week of created_at over accounts with is_internal = false. An account counts as activated when it issues a request with http_status < 400, a non-null api_key_id and traffic_class <> 'synthetic_monitor' within 168 hours of its own created_at. Return cohort accounts, activated accounts and the rate per week, and exclude any week that has not yet fully elapsed its 168-hour window.

Approach

Collapse dim_account to one row per account_id before anything else. It is a type 2 dimension, so a plan or status change gives the same account several rows; filtering to is_current = true is the cheapest correct choice here because created_at does not change across versions.
Express the window as interval arithmetic on the timestamptz column: request_at >= created_at AND request_at < created_at + interval '168 hours'. A date-difference of 7 days is a different and wrong condition for accounts created mid-day.
Test activation with EXISTS rather than a join to MIN(request_at). EXISTS short-circuits, keeps the cohort at one row per account, and cannot fan out.
Aggregate by date_trunc('week', created_at AT TIME ZONE 'UTC'), counting accounts and activated accounts, and divide as a ratio of counts.
Drop unreportable weeks: the last account in a cohort week is created just under week_start + 7 days, so the week is only complete once now() >= week_start + interval '14 days'. Without that filter the newest week always looks like a regression.

Worked solution (20 min)

Write the cohort CTE: SELECT account_id, created_at FROM dim_account WHERE is_current AND NOT is_internal AND created_at >= <window start>.
Add the activation predicate as a correlated EXISTS over fct_api_request on account_id with the four conditions: http_status < 400, api_key_id IS NOT NULL, traffic_class <> 'synthetic_monitor', and the 168-hour bracket.
Group by date_trunc('week', created_at AT TIME ZONE 'UTC'); select count() AS cohort_accounts, count() FILTER (WHERE activated) AS activated_accounts, and the ratio cast to numeric.
Add HAVING or an outer WHERE that keeps only weeks where week_start + interval '14 days' <= now().
Spot-check one account that activated on hour 167 and one that activated on hour 169 to confirm the boundary is exclusive at the top.

Expected result: One row per ISO cohort week with cohort_accounts, activated_accounts and activation_rate between 0 and 1; the newest row is at least 14 days old, and accounts with no rows at all in fct_api_request appear in the denominator with activated = 0 rather than disappearing.

Verify it yourself

Summing cohort_accounts across weeks equals the count of non-internal current-version accounts created in the same window.
Re-running the query a day later leaves every previously reported week byte-identical; any movement means the censoring filter is wrong.
activated_accounts is never greater than cohort_accounts, and the count of distinct account_id in the activated set matches a separate COUNT DISTINCT over the fact table.

Follow-up

The median time-to-first-successful-call is more informative than a fixed-window rate. Why can you not compute it from this query, and what estimator does it need?
How would you separate accounts that never called from accounts that called and got only 4xx responses, and which of those is a product problem?

What are the trade-offs between using complex tree-based models versus generalized linear models for credit or risk prediction?

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.
Fix the population and the time window before naming any metric.

Follow-up

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

Walk through a scenario where a key metric drops unexpectedly; how do you diagnose the root cause systematically?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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

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?

What product metrics would you introduce to measure the success of an AI-enhanced payroll feature?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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

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

If user engagement drops by fifteen percent week-over-week, what framework do you use to investigate?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.
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?
Which segment would you cut first, and what would that rule out?

How do you determine sample size and power for an experiment with high baseline variance?

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.
Say whether units interfere with each other, and switch design if they do.

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?

How would you set up an A/B test for a new interface layout on a high-traffic client portal?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
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.

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?

How would you identify new product opportunities using large-scale historical human capital management data?

Medium

Approach

Work from the decision backwards to the evidence you would need.
Clarify what is being asked and what a complete answer would contain.
State your assumptions explicitly before working the problem.

Follow-up

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

Separate novelty from durable lift in a console redesign

Medium · novelty-effect · time-heterogeneity · holdout

A redesigned request console was tested for four weeks, randomised at the account level. The lift in weekly successful interactive requests per account was +9.2%, +5.1%, +1.8% and +0.4% in experiment weeks one through four, and the week-four 95% interval spans plus or minus 3.1 percentage points. Accounts entered on a rolling basis as they next signed in, so week one is a different calendar week for different accounts. Say what the decaying series can and cannot establish, specify the readout that would settle it, and state what you would ship on Monday.

Approach

Name the distinct explanations the same curve is consistent with, because the shipping decision differs across them: a novelty effect decaying to zero; a novelty effect decaying to a small positive plateau the test has no power to see; and a genuine effect whose measured size shrinks as control accounts learn about the change from colleagues or release notes.
Fix the time axis before interpreting anything. With rolling entry, calendar week and weeks-since-first-exposure are different variables, and pooling them mixes the decay curve with a change in cohort composition, since the accounts that signed in on day one are systematically the most engaged. Recut on weeks-since-first-exposure and verify that plan_tier and pre-period usage mix are stable across entry cohorts.
Read the interval width honestly. Plus or minus 3.1 percentage points at week four cannot separate a durable +1% from zero. The defensible statement is that no durable effect larger than roughly 3.5% was detected, not that there is no durable effect, and those two sentences lead to different decisions.
Separate novelty from primacy using account tenure. Accounts created after launch have no prior console to be surprised by, so they cannot show novelty; if the redesign is genuinely better their curve should be flat or rising, while established accounts show the spike and decay. If both cohorts decay to zero, the effect really was novelty.
Specify the settling readout rather than arguing about the four weeks you have. Hold back 5% of accounts as a long-run holdout for 90 days, and compare whole ISO weeks only: usage in this domain follows a hard five-to-two weekday cycle, and a window containing four business days instead of five moves this metric by several percent with no product change.
Give the Monday answer. Ship if guardrails are clean and maintenance cost is low, because a decayed-to-zero effect with no harm is a neutral trade; but book none of the +9.2% in any forecast, and do not run a follow-up test on the same accounts inside the novelty window, because their baseline has not returned to steady state.

Worked solution (30 min)

Recut the four weekly estimates on weeks-since-first-exposure and confirm entry-week cohorts are comparable on plan_tier and pre-period usage.
Split each week's estimate by account tenure, created before versus after launch, and compare the shapes of the two curves.
Compute the horizon needed to halve the week-four interval: precision scales with the square root of exposure, so about four times the account-weeks are required.
Write the ship note with the 5% holdout design, the 90-day re-read date, and an explicit statement of the effect sizes that remain unexcluded.

Expected result: The series is consistent with pure novelty, with a durable plateau anywhere below about 3.5% left unexcluded. Separating the explanations requires the tenure split plus a 5% holdout re-read at 90 days, and halving the week-four interval to plus or minus 1.55 points needs roughly four times the account-weeks.

Verify it yourself

Interval width scales with one over the square root of the sample, so quadrupling exposure should roughly halve 3.1 points. A plan claiming a larger gain is double-counting repeated weeks on the same accounts, which are correlated rather than independent.
Accounts created after launch cannot exhibit novelty against an experience they never had, so a week-one spike in that cohort points at an assignment or instrumentation problem rather than at novelty.
Every weekly comparison must be a whole ISO week. A window containing a different number of business days than its comparator is not a like-for-like contrast in this domain.

Follow-up

What sample or horizon would you need to rule out a durable +1.5% at 80% power, given the week-four interval you have?
How would you distinguish a novelty effect from a control arm that gradually learned about the change?
The metric that decides renewal is up to eleven months away on annual contracts. What proxy do you use in the meantime, and how do you validate it once renewals land?

Blended gross margin fell while every segment improved

Medium · mix shift · simpsons paradox · as-of join

Blended gross margin across paying accounts fell from 71 percent to 66 percent over two quarters, yet margin improved inside every plan_tier and every deployment_model. You have fct_usage_daily (account_id, sku_code, usage_date, net_amount_cents, cogs_cents), fct_subscription_period (account_id, plan_tier, term_start_date, term_end_date) and dim_account (account_id, deployment_model, is_internal, effective_from, effective_to). Produce an exact decomposition of the five-point move into mix, rate and interaction terms, and name the segment shift that carries it.

Approach

Write blended margin as a revenue-weighted average of segment margins, m = sum over i of w_i * m_i, where w_i is the segment's share of net_amount_cents. Margin is a ratio of sums, so only revenue weights reproduce the blended figure.
Apply the exact three-term decomposition: delta_m = sum(delta_w_i * m_i0) + sum(w_i0 * delta_m_i) + sum(delta_w_i * delta_m_i), read as mix, rate and interaction. It is exact by construction, so the three terms must sum to the observed change with no residual.
Build segments from as-of joins: take the fct_subscription_period version whose term brackets each usage_date and the dim_account version whose effective_from and effective_to bracket it, rather than joining on is_current and backdating today's attributes over last year's usage.
Run the decomposition on more than one segmentation, at minimum plan_tier, deployment_model and sku_code, since the mix that actually moved may not be the one anybody already suspected.
Once the driving segment is identified, determine whether the shift came from acquisition landing new accounts in a lower-margin segment or from existing accounts migrating, because those have different owners and different remedies.

Follow-up

The interaction term is sometimes large. What does it mean operationally, and when would you prefer a log-mean decomposition that distributes it across the other two terms?
Revenue-weighted average margin is one construction. What changes if leadership wants margin weighted by account count, and which question does each version answer?

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 situation where you faced a significant disagreement with a cross-functional partner on project direction.

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Close with what you would do differently, concretely.
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?

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?