Interview guides · Audi

Audi Data Scientist Interview Questions & Guide 2026

Data scientist prep for Audi: practice defining a payments and lending metric, picking its guardrails and defending the tradeoff.

The 60-second brief

As a Data Scientist at Audi, you sit at the intersection of traditional automotive engineering and the future of digital mobility. Your work is fundamental to transforming the way Audi approaches vehicle intelligence, customer experience, and operational efficiency. By leveraging large-scale data, you help the company transition from a hardware-focused manufacturer to a data-driven service provider.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Focus areas

Report only matured cohorts for loss metrics
Reconcile amounts in minor units and currency
Read vintage curves, not blended portfolio averages

Overview

Your impact will be felt across various domains, ranging from predictive maintenance for vehicle fleets to optimizing digital sales funnels and personalized customer journeys. You will be expected to translate complex business problems into actionable models, ensuring that Audi maintains its competitive edge in a rapidly evolving technological landscape. This role is highly collaborative, requiring you to bridge the gap between technical data solutions and stakeholder requirements across global teams.

The Audi interview process

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

High-Level Screening

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Technical Assessment

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

Final Assessment Rounds

A loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.

What to demonstrate

Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience

How to prepare

Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story

Where candidates lose points

Using written premium as the denominator of a loss ratio

Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.

Recalibrating an underwriting cutoff on approved and funded applicants only

Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.

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.

Averaging per-user rates to produce a population rate

Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.

Questions & practice

Build a vintage delinquency table without pivot or unstack

Easy · vintage analysis · cohorts · groupby · cumulative max

fct_loan_performance_monthly gives loan_id, origination_month, months_on_book, days_past_due, charge_off_flag and restructured_flag. Produce a DataFrame with one row per origination_month and columns for months_on_book 0 through 12, each cell holding the share of that vintage's funded loans that had ever reached 90 or more days past due, or charge-off, by that age. You may not use pivot, pivot_table, crosstab or unstack. Cells for ages a cohort has not yet reached must be NaN rather than zero.

Approach

Define the per-row indicator as days_past_due >= 90 or charge_off_flag, then take a cumulative maximum of it per loan ordered by months_on_book, because the metric is reached-by-age-m, not in-that-state-at-age-m.
Deal with restructuring before the cumulative max. Restructuring resets days_past_due, so a restructured loan re-enters at current and, without the cumulative maximum carrying its pre-restructure worst state, reads as a cure.
Fix the denominator once as the count of distinct loan_id per origination_month across the whole cohort. Prepaid and charged-off loans stop producing rows, so a denominator recomputed at each age silently shrinks exactly where losses land.
Aggregate with groupby(['origination_month','months_on_book'])['ever_90'].sum(), then pre-build the output frame indexed by sorted origination months with integer columns 0 to 12 and assign from the grouped Series by .loc on its index.
Mask cells beyond each cohort's maximum observed months_on_book so an immature cell reads NaN instead of an artificially low rate.

Worked solution (30 min)

Sort by loan_id and months_on_book, build the ever_90 indicator, then apply groupby('loan_id')['ever_90'].cummax().
Compute cohort_size as the distinct loan_id count per origination_month, before any filtering on age.
Group the cumulative indicator by origination_month and months_on_book and sum it to get the numerator per cell.
Create the output frame with the sorted origination months as index and range(0, 13) as columns, fill it from the grouped Series, and divide each row by its cohort_size.
Compute each cohort's maximum observed months_on_book and set every cell to the right of it to NaN.

Expected result: A DataFrame with one row per origination_month and columns 0 to 12, values non-decreasing left to right within every row, near zero in column 0, and NaN forming an upper-right triangle where cohorts have not yet aged.

Verify it yourself

Every row is non-decreasing across columns; a decrease means the cumulative maximum was not applied or a restructure reset leaked through.
The denominator per row equals the cohort's distinct loan count and does not change across columns.
Column 0 is at or near zero for every vintage, since a loan cannot be 90 days past due at its first month end.
origination_month plus months_on_book is constant along an anti-diagonal, so one anti-diagonal should reproduce a direct calendar-month filter.

Follow-up

Two adjacent vintages diverge at months_on_book 6. How would you separate seasoning, mix shift and a genuine credit-quality change?
The three most recent vintages look best on this table. What do you check before saying so?
How does the table change if charge-off policy moved from 180 to 120 days past due partway through the series?

Estimate a delinquency roll-rate matrix and project twelve months

Hard · roll rates · markov chain · survivorship

fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.

Approach

Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.

Follow-up

How would you validate the projection against what actually happened, and over what window?
The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
When would you prefer a vintage curve to a roll-rate projection, and why?

Bootstrap a fraud loss rate that clusters within merchant

Medium · bootstrap · clustered resampling · heavy tails

You have a per-transaction frame with auth_id, merchant_id, settled_amount_reporting and net_loss_reporting, both already in one reporting currency. Most rows carry zero loss, a few carry large ones, and losses cluster within merchant. Using only numpy's random generator and no resampling helper from any library, write a bootstrap that returns a 95 percent interval for net fraud loss in basis points of settled volume, resampling merchants with replacement and taking all rows belonging to each drawn merchant. Also produce the naive row-level interval and state which you would report.

Approach

State the estimator before writing it: total net loss divided by total settled volume, times 10,000. It is a ratio of sums, so each replicate recomputes both sums. Averaging per-transaction loss rates instead would weight a five-unit transaction like a five-thousand-unit one.
Pre-aggregate loss and volume to merchant level once. For a ratio of sums, drawing merchants and taking all their rows is arithmetically identical to drawing merchant-level (loss_sum, volume_sum) pairs, so a replicate becomes one integer draw plus two vectorised sums rather than a groupby inside the loop.
Draw B replicates of M merchant indices with replacement, where M is the observed merchant count, compute the ratio per replicate, and take the 2.5th and 97.5th percentiles. Say explicitly that this is a percentile interval and that BCa would correct the skew-induced bias if the decision is close.
Repeat with independent row draws for the naive interval and compare widths on the same replicate count.
Report the clustered interval. Rows within a merchant share an acceptance profile, a category code and a fraud exposure, so they are not independent, and the row-level interval understates variance by roughly the design effect.

Follow-up

Your clustered interval is three times wider. How do you explain that to someone who wanted a tighter number?
One merchant accounts for 40 percent of losses. What does that do to the interval, and what would you do about it?
How does this change if the question is whether two months differ rather than what this month's rate is?

Explain how you would join multiple disparate tables to build a unified customer profile.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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?

Write a query using SQL window functions to calculate a rolling average of vehicle diagnostics.

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?
How would you verify this result without re-running the same query?

How would you handle missing data or outliers in a large telematics dataset?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
State the window function and its partition and ordering out loud before writing it.

Follow-up

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

Attribute chargebacks to transaction month without fanning out

Medium · fan-out · cohort attribution · maturity

Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.

Approach

Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.

Worked solution (30 min)

CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.

Expected result: One row per transaction month with settled_txns, chargeback_cases, a rate in basis points, and a status of 'final' or 'incomplete'. The final months form a roughly flat series; the incomplete months at the right edge are visibly and correctly labelled rather than sloping to zero.

Verify it yourself

settled_txns for a given month is identical whether or not the dispute CTE is joined, which is the direct test that no fan-out occurred.
COUNT(DISTINCT auth_id) in the numerator is at or below the dispute row count; if they are equal, no authorization in the data carries two cases and the fan-out risk is latent rather than absent.
Every month flagged 'final' has its last day at least 120 days before the current date.
Attributing by opened_at instead shifts the series later and flattens the right edge, which demonstrates why the attribution key matters.

Follow-up

Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?

How would you design the key performance indicators for a new Audi connected-car feature?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
How would you detect that the metric is being gamed rather than genuinely improving?

What metrics would you use to measure the success of an A/B test on our vehicle configuration website?

Medium · product sense and metrics

Approach

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.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

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

If you noticed a sudden drop in user engagement on our digital platform, how would you diagnose the root cause?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.
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?

What are the most common experimentation pitfalls that could lead to a false positive?

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
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.

Follow-up

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

When is it appropriate to use a parametric versus a non-parametric test in our product analysis?

Medium · a/b testing and experimentation

Approach

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.
Name the randomisation unit first; it decides the variance and what the test can detect.

Follow-up

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

Monitor a rollout daily without inflating false positives

Medium · peeking · sequential testing · alpha spending

A risk-rule change is ramped to 50 percent and the team reads the dashboard every morning for twenty business days, intending to stop the first time the two-sided p-value on the count-weighted approval rate falls below 0.05. Quantify how much that inflates the false positive rate, propose a monitoring scheme that still permits an early stop for harm, and explain what dispute maturity does to the matured fraud basis points guardrail when it is read on day twenty.

Approach

Quantify rather than assert. Repeatedly applying a fixed-horizon test at nominal two-sided 0.05 inflates the family-wise type I error to roughly 8 percent at 2 looks, 14 percent at 5, 19 percent at 10 and about 25 percent at 20 equally spaced looks. Under continuous monitoring with no stopping rule the probability of crossing at some point tends to 1.
Pick a scheme matched to how the team actually behaves. If looks are on a fixed schedule, use a group-sequential design with an alpha-spending function: O'Brien-Fleming spends almost nothing early so the final boundary stays near nominal, which suits a team that mostly wants to ship at the end; Pocock spends evenly and buys genuine early-stopping power at the cost of a stricter final boundary. If looks are truly continuous and ad hoc, use an always-valid confidence sequence such as a mixture sequential probability ratio test, which is valid at every moment in exchange for a larger fixed-horizon sample at equal power.
Treat the harm stop as a separate, asymmetric decision. Stopping a change because it looks harmful costs one abandoned experiment; failing to stop costs real loss every day it runs. Run the guardrail as a one-sided monitor at a looser alpha with its own pre-registered stop rule, and do not spend the primary metric's alpha budget on it.
Fix the schedule before launch: the number of looks, their timing, the boundaries, and the maximum sample. Boundaries computed after the fact from however many times someone opened the dashboard are not a correction.
Separate peeking from immaturity on the fraud guardrail. Disputes on a transaction can be filed for roughly 120 days, and some reason codes run longer, so a day-twenty read covers transactions with at most twenty days of dispute exposure. The number is not low, it is incomplete. Either report only matured transaction months, or apply development factors estimated from completed months and label the result an estimate with its interval.

Worked solution (25 min)

Quote the inflation for the stated plan: twenty looks at nominal two-sided 0.05 gives a family-wise type I error of about 25 percent, so one rule change in four would appear significant with no true effect.
Specify the replacement: five pre-scheduled looks at 20, 40, 60, 80 and 100 percent of planned sample under an O'Brien-Fleming spending function, with z boundaries of approximately 4.56, 3.23, 2.63, 2.28 and 2.04. For comparison, a Pocock design at five looks uses a constant boundary of about 2.41.
Add a one-sided harm monitor on the fraud guardrail and the decline-rate guardrail with its own alpha and its own pre-registered rule, documented before launch.
For the fraud read, restrict to transaction months with at least 120 days of maturity; if none exist yet, present a development-factor estimate from completed months with its uncertainty and mark the recent months incomplete on the chart rather than plotting them as low.
If the boundary is crossed early, report a bias-adjusted effect estimate, because the estimate at a stopping boundary is systematically larger in magnitude than the truth.

Expected result: Twenty daily looks at nominal 0.05 give a family-wise type I error of roughly 25 percent. The recommendation is a pre-registered five-look O'Brien-Fleming group-sequential design with a final z boundary near 2.04, or an always-valid confidence sequence if looks cannot be scheduled, plus a separate one-sided harm monitor. The day-twenty fraud guardrail is reported as incomplete, not as a pass.

Verify it yourself

Simulate the stated stopping rule under the null a few thousand times; the crossing rate should reproduce the quoted inflation, and under the group-sequential boundaries it should land near 5 percent.
The alpha spent across the planned looks under the chosen spending function must sum to the total alpha; if it does not, the boundaries were not computed from a spending function.
Plot the fraud rate by transaction month against dispute maturity. If the curve slopes down at the right edge on historical data where nothing changed, that slope is maturity, not effect.
Compare the effect estimate at the crossing look with the estimate at the planned maximum sample on historical tests; the gap is the stopping bias you owe the sponsor.

Follow-up

Under an O'Brien-Fleming boundary you cross on day three. What do you say about the effect size, and why is the naive point estimate biased?
How would you set the stopping rule for the fraud guardrail given that its true value is not observable inside the test window?
The team argues that they are only looking, not deciding, so peeking is harmless. Under what precise condition is that true, and how would you verify it?

Approval rate fell but approved value did not

Easy · approval rate · retry chains · denominators

Over ten days the count-weighted 7-day approval rate on fct_payment_authorization fell from 91 to 86 percent, while captured value in the reporting currency is flat. Available columns: auth_id, card_token_id, merchant_id, mcc, channel, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, risk_score, is_reversal, parent_auth_id, captured_at, captured_amount_minor, issuer_country. In twenty minutes, decide whether approved value is actually at risk, and hand back a corrected rate together with its denominator and every exclusion written down.

Approach

Restate the denominator before querying anything. The current one counts every row, so reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and zero-amount verification attempts (amount_minor = 0) are all sitting in it.
Chart numerator and denominator separately by day. If approved counts are flat and total attempts rose, the rate moved because the denominator grew, which is a different investigation from a rule change and points at a different owner.
Group declines by decline_reason_code and merchant_id. Retry-driven inflation concentrates in a few soft decline codes at a few merchants; a genuine policy change spreads across merchants within one code family.
Collapse retry chains: partition by (card_token_id, merchant_id, amount_minor), keep one attempt per 15-minute window taking the best outcome, and recompute both count-weighted and dollar-weighted rates on the collapsed set.
Convert amount_minor to one reporting currency using each currency's ISO 4217 exponent before any dollar weighting, then cut by channel and issuer_country to confirm nothing is hiding underneath a flat total.
Report both rates side by side with the exclusion list attached, and state which definition the alert should have been built on.

Follow-up

How would you pick the retry-collapsing window when merchants retry on different schedules?
Flat captured value could itself be hiding a mix shift. How do you rule that out?
What monitor would have caught denominator inflation on the day it started, rather than ten days later?

Suggested practice checklist

For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.

Day 1 — Metric anatomy

For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.

Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.

Day 2 — Diagnosing a drop without guessing

Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.

Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.

Day 3 — Should we build it

Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
Write the counter-metric that would make you kill the feature even if it wins on the primary metric.

Deliverable: A one-page product memo ending in a decision rather than a list of considerations.

Day 4 — The places aggregate numbers lie

Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.

Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.

Day 5 — Technical maintenance, aimed at metrics

Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.

Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.

Day 6 — Turning engineering work into data science stories

Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.

Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.

Day 7 — Mock case and gap list

Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
Listen back and mark every moment you proposed a solution before the success metric existed.
Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.

Deliverable: A recorded case plus a rewritten opening 90 seconds.

What the behavioural round is actually scoring

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

Describe a time you had to explain a complex technical finding to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Disagree with a product manager over an approval-rate target

Medium · metric design · denominators · influence without authority

A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.

Approach

Separate the goal from the metric out loud and agree with the goal first, so the disagreement stays narrow and technical rather than becoming positional.
Demonstrate the failure rather than asserting it: compute the proposed metric and the dollar-weighted collapsed version over the same recent window, and find a period where they moved in opposite directions.
Propose the replacement precisely: sum of approved amount_minor over sum of attempted amount_minor, after collapsing retries to one attempt per card_token_id, merchant_id and amount_minor within a 15-minute window, excluding is_reversal rows and zero-amount verifications, with everything converted to one reporting currency before summing.
Attach the guardrail that makes the target honest: matured first-chargeback rate and net fraud loss in basis points of settled volume, read only on transaction months carrying at least 120 days of maturity.
Give the product manager something back: the replacement metric cuts cleanly by channel and issuer_country, which makes a roadmap of merchant-specific and authentication fixes legible in a way the blended rate never was.

Follow-up

How do you identify a retry chain when the merchant varies the amount slightly between attempts?
The product manager wants a weekly read on the guardrail. What is the earliest defensible signal, and how do you label it?

Recommend a decision whose true outcome matures a year later

Hard · decision under censoring · leading indicators · staged rollout

An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.

Approach

Fix the readout before the rollout, because a readout chosen after the data arrives is a story rather than a decision rule: indicator, window, threshold and reversal condition all go in writing first.
Choose the leading indicator on its measured relationship to the matured outcome in historical vintages rather than on availability. Early delinquency, typically the share reaching dpd_1_29 or missing a first scheduled payment by months_on_book 3, is the usual candidate, and you quantify how well it predicted the 12-month rate across past cohorts.
State the bias and its direction plainly: early delinquency under-represents default that emerges later and is contaminated by servicing and payment-date effects, so treat it as a floor on risk rather than an estimate of it.
Buy identification where full randomisation is unavailable: a narrow randomised approval band around the cutoff, or a staged rollout by channel or region read as a difference-in-differences, with the parallel-trends assumption stated and checked in the pre-period rather than assumed.
Give the executive the binary they asked for with the trigger attached in the same sentence: yes, conditional on the month-3 indicator staying inside a stated band, with an automatic hold if it breaches.

Follow-up

How would you validate that the month-3 indicator predicts the 12-month outcome, and what evidence would invalidate it mid-rollout?
Compliance refuses a randomised band. What is your next-best identification strategy, and what precision do you lose by taking it?