Interview guides · Ati

Ati Data Scientist Interview Questions & Guide 2026

Modeling drills for a Ati data scientist interview: baselines, regression interpretation, class imbalance, model defense.

The 60-second brief

The role of a Data Scientist at Ati is central to driving data-driven decision-making across the organization. As a Data Scientist, you will leverage advanced statistical techniques, machine learning algorithms, and data visualization tools to extract insights from complex datasets. Your analyses will inform product development, enhance user experiences, and optimize business strategies, making your contributions crucial to the success of Ati's offerings.

Nearly every loop contains a round whose deliverable is a recommendation to someone non-technical. Practise stating a conclusion, the confidence attached to it, and the cost of being wrong in each direction, because that triple is the artifact being graded.

Focus areas

Report only matured cohorts for loss metrics
Separate authorization, settlement and dispute outcomes cleanly
Reconcile amounts in minor units and currency

Overview

In this position, you will collaborate with cross-functional teams to tackle real-world problems, working on projects that impact various aspects of the business, from product recommendations to market analysis. The scale and complexity of the data you will be handling provide a unique opportunity to influence products that reach millions of users. You can expect to engage with cutting-edge technologies and methodologies, making this role both challenging and rewarding.

The Ati interview process

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

Initial Screening

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
Whether your reason for wanting the role points at the work itself rather than the company's reputation
Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Technical Assessment

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

What to demonstrate

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

How to prepare

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

Behavioral Interview

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

What to demonstrate

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

How to prepare

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

Case Studies

A case round is decided by whether you leave the interviewer with a recommendation, not by how much analysis you narrate on the way there. The prompt is open on purpose, so the first job is to convert it into a decision someone could act on: ask what would be done differently depending on the answer. From there name the quantity that would settle it, state the assumptions you need, and commit. Candidates who cover more ground than anyone expected and still end on "it depends" score below candidates who scoped narrowly and said what they would do.

What to demonstrate

Whether the version of the question you choose to answer is genuinely narrower than the prompt and still worth answering
Whether the recommendation arrives as an action with a number attached, rather than as a summary of what you looked at
Whether assumptions are stated at the moment you rely on them, instead of collected into a disclaimer at the end
Whether you notice when a branch you are exploring would not change the decision either way

How to prepare

Take six open prompts and write only the scoping move for each: the one-sentence question you would actually answer and the decision it feeds. Give yourself three minutes per prompt and stop there.
Put a five-minute warning into every practice case and force a closing statement that names the action, the result that would justify it, and the result that would reverse it.
Record one case and count how long you talked before naming a measurable quantity. Past roughly five minutes, what you are calling scoping is narration.

Final Interview

Where a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.

What to demonstrate

Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered

How to prepare

Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one

Where candidates lose points

Averaging delinquency across a book that is growing

A loan three months old cannot be 90 days past due, so a portfolio with many recent originations reports a low blended 90+ rate purely from age mix. The blended rate falls fastest exactly when originations grow fastest, which is precisely when credit quality most needs watching, so the metric moves in the reassuring direction during the riskiest period. Only comparisons at equal months on book are valid, which is what a vintage or roll-rate view enforces.

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

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.

Reporting a p-value with no effect size or interval

Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.

Questions & practice

Write a function to calculate the mean and standard deviation of a dataset.

Medium · statistics and probability

Approach

Write down the assumption the method needs before you use the method.
Sanity-check the answer against a simple bound or a simulated case.
Translate the result into the decision it informs, in one plain sentence.

Follow-up

What sample size would you need to detect an effect half this size?
How would you explain this result to someone who does not know statistics?

Walk us through your thought process for optimizing a machine learning model.

Medium · machine learning and modelling

Approach

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

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

How would you implement a decision tree algorithm from scratch?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Pick an evaluation metric that matches the cost of each error type, not a default.

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?

Collapse retry chains and compute a dollar-weighted approval rate

Medium · sessionisation · window functions · dollar-weighted rates

fct_payment_authorization gives auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, requested_at, auth_result, is_reversal, channel and issuer_country. Two reference frames give the minor-unit exponent per currency and a daily rate to one reporting currency. Collapse retry chains first: attempts sharing card_token_id, merchant_id and amount_minor whose consecutive gaps are under 15 minutes form a single attempt, whose outcome is its last row. Exclude reversals and zero-amount verifications. Return a 7-day rolling dollar-weighted approval rate by channel and issuer_country.

Approach

Filter before grouping: drop is_reversal rows and zero-amount verifications, since neither is a purchase attempt and both would otherwise sit in the denominator.
Sort by card_token_id, merchant_id, amount_minor and requested_at, take the gap to the previous row within that key, mark a chain start where the gap exceeds 15 minutes or the key changes, and label chains with a cumulative sum of that flag. This is a gap rule between consecutive attempts, not a fixed clock bucket, so a chain may span more than 15 minutes in total.
Keep each chain's terminal row by requested_at. If a retry was approved, the purchase was approved; keeping the first row reports the decline that caused the retry as the outcome.
Convert amounts exactly once: amount_minor divided by 10 to the power of the currency exponent, multiplied by the reference rate for the authorization date. Do not reach for settlement_fx_rate, which is null on precisely the declined rows the denominator needs.
Build the rolling window as a ratio of two rolling sums, approved value over total value, per channel and issuer_country. A rolling mean of daily ratios weights a quiet Sunday the same as a busy Friday.

Worked solution (35 min)

Filter out reversals and zero-amount rows, then sort by the chain key and requested_at.
Compute the within-key time difference, derive the chain start flag and the chain id, and take the last row per chain with groupby(chain_id).tail(1) after sorting.
Join the exponent and daily rate tables, compute value_reporting, and assert no nulls remain after the join.
Aggregate approved value and total value to a daily grain by channel and issuer_country, reindex to a complete date range per group so missing days are zero rather than absent.
Take 7-day rolling sums of both columns and divide, then confirm one hand-picked group-day against a direct filter.

Expected result: A DataFrame keyed by date, channel and issuer_country with approved_value, total_value and approval_rate. The collapsed attempt count is materially below the raw row count, with the gap concentrated in declined ecommerce rows, and ecommerce sits below card_present.

Verify it yourself

Within every chain, all consecutive requested_at gaps are under 15 minutes; across chains with the same key, at least one gap is not.
Chain count is strictly below raw attempt count, and the difference is concentrated in declined rows rather than spread evenly.
Total converted value on a single-currency single-day subset equals the raw amount_minor sum divided by that currency's exponent power, times that day's rate.
The rolling rate on one chosen day matches a direct filter-and-divide over the preceding seven days.

Follow-up

The count-weighted rate is flat while the dollar-weighted rate falls 80 basis points. What do you look at first?
How would you choose the 15-minute window rather than inheriting it?
A merchant moves from two retries to five. Which of your two rates moves, and is that a real change in approval quality?

Solve a coding challenge related to data manipulation.

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

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

Monthly delinquency roll rates with honest exiting-account denominators

Hard · roll rates · window functions · self joins

From fct_loan_performance_monthly, compute month-over-month roll rates: for each as_of_month_end and delinquency_bucket, the share of loans in that bucket that are in a worse state at the following month end. Use this disposition convention and apply it without exception: charge-off is the terminal state one rank past dpd_90_plus, so a loan that charges off has rolled forward, not exited; loans that leave the book any other way — prepaid in full, sold, matured, or simply absent at the next month end — are exited. Bucket order is current, dpd_1_29, dpd_30_59, dpd_60_89, dpd_90_plus, then charged_off. Return from_month, from_bucket, accounts, rolled_forward, cured, stayed, exited and roll_rate, and show that the four dispositions sum to accounts on every row.

Approach

Map the five delinquency_bucket values to ranks 0 through 4 in a small inline VALUES list, and reserve rank 5 for charged_off, because 'worse' is an ordering statement and enum text does not sort that way reliably. Read charge-off from charge_off_flag on the next month's row rather than from delinquency_bucket, which keeps carrying whatever delinquency value the loan held when it was written off.
Use LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) OVER (PARTITION BY loan_id ORDER BY as_of_month_end), then verify the leading month end is exactly one month after the current one, since a loan missing a month would otherwise appear to cure or roll across a gap it never traversed.
Classify each loan-month into exactly one of four labels off the adjacent next row, whose rank is 5 when its charge_off_flag is true and its mapped bucket rank otherwise. rolled_forward: the next rank is strictly greater. cured: strictly lower. stayed: equal. exited: there is no adjacent next row at all, which is where prepayment, sale, maturity and a gap in the monthly history land.
Define rolled_forward as 'any strictly worse rank', not 'rank plus one'. Under normal monthly aging the only reachable worse state is the next bucket, so the two definitions coincide — except at charge-off, where a loan can go from dpd_60_89 straight to rank 5. A strict plus-one test leaves those loan-months matching no branch and breaks the exhaustiveness invariant.
Be explicit about why charge-off is a roll rather than an exit: routed to exited, dpd_90_plus has no worse state left, so its roll_rate is identically zero by construction — arithmetic, not a credit finding. At rank 5 the dpd_90_plus row reports roll-to-loss, which is the number a loss forecast actually consumes.
Aggregate by from_month and from_bucket and require that rolled_forward + cured + stayed + exited equals accounts on every row, which is the invariant that proves the classification is exhaustive and mutually exclusive.

Worked solution (45 min)

CTE ranks: an inline VALUES mapping of the five delinquency_bucket values to ranks 0 through 4, joined onto the monthly rows. Rank 5 is not in the map; it is assigned during classification.
CTE leads: add LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) partitioned by loan_id ordered by as_of_month_end.
CTE classified: derive next_rank as CASE WHEN next_charge_off_flag THEN 5 ELSE next_bucket_rank END, then a CASE expression producing exactly one label per loan-month — exited when the next month end is null or is not the current month end plus one month, otherwise rolled_forward / cured / stayed by comparing next_rank to bucket_rank.
Aggregate to from_month and from_bucket with COUNT(*) and four FILTER counts, then compute roll_rate as rolled_forward over accounts with a numeric cast.
Assert the exhaustiveness invariant in a final HAVING or a separate verification query before trusting any number.

Expected result: One row per month and originating bucket, with the four disposition counts summing to accounts on every row and roll_rate between 0 and 1. Roll rates climb across current, dpd_1_29, dpd_30_59 and dpd_60_89, because each successive bucket holds loans that have already failed to cure. Do not expect that climb to continue into dpd_90_plus: that bucket is a catch-all holding several monthly cohorts at once until the charge-off policy retires them, so its roll-to-loss rate is set by the policy horizon — under a 180-day policy roughly a third of the bucket rolls in a given month — and usually sits below the dpd_60_89 rate. Cure out of dpd_90_plus is well under ten percent.

Verify it yourself

rolled_forward + cured + stayed + exited equals accounts for every row, with no exceptions.
SUM(accounts) for a given from_month equals the count of loan rows at that month end, so no loan-month was dropped or counted twice.
Removing the adjacency test changes the cure and roll counts, and the size of that change is exactly the number of loans with a gap in their monthly history.
The rolled_forward count for from_bucket dpd_90_plus in month M is at least the number of loans whose first charge_off_flag = true row falls at month end M+1. If it is materially lower, charge-off is being routed into exited.
roll_rate for dpd_90_plus is strictly greater than zero in any month with charge-offs. A flat zero on that bucket across every month is the signature of charge-off classified as an exit, not of an unusually stable book.
No loan-month is counted as both rolled_forward and exited: a loan with a charged-off row at M+1 has an adjacent next row, so it can never reach the exited branch.

Follow-up

Project the next three months of dpd_90_plus inflow from these roll rates. What assumption does that projection make, and when does it break?
A restructure resets days_past_due to zero. What does that do to the cure rate out of dpd_60_89, and how would you separate a real cure from a reset?
Argue the other convention: prepayment in exited but charge-off also in exited, with roll rates reported only for the four non-terminal buckets. What does that series answer better, and what does it lose?
The charge-off policy changed from 180 to 120 days. Show where that appears in this table and what it does to the dpd_90_plus roll rate specifically.

Given a dataset, how would you determine the most significant features?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.
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?

How would you approach a data analysis project to improve user engagement?

Medium · product sense and metrics

Approach

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

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 is the difference between supervised and unsupervised learning?

Medium

Approach

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

Follow-up

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

Read a segment-sliced result with a fading first-week lift

Medium · multiple comparisons · novelty effects · mix shift

A three-week test of an in-app credit-limit-increase offer had one pre-registered primary metric. The readout also slices 24 secondary segment-by-metric cells, of which three are significant at 0.05 with p-values 0.011, 0.028 and 0.041. The treatment effect on the primary was plus 8.1 percent in calendar week one, plus 3.4 percent in week two and plus 1.5 percent in week three. Say which claims survive, what the decay does and does not prove, and what you tell the sponsor.

Approach

Compute what chance alone produces before interpreting any cell. With 24 tests at 0.05 the expected number of false positives is 1.2, the probability of at least one is 1 - 0.95^24 = 0.708, and the probability of three or more under an independence approximation is about 0.12. Three hits is an unremarkable outcome, not a signal.
Apply a stated correction rather than a vibe, and be precise about which one the reported numbers can settle. Holm and Bonferroni both begin at 0.05 / 24 = 0.00208, so the smallest p-value alone decides them. Benjamini-Hochberg sorts all m p-values ascending and finds the largest k with p(k) <= k * q / m; at q = 0.10 and m = 24 the rank-k threshold k / 240 starts at 0.00417 and rises to 0.10 at k = 24, so it is a step-up test over the whole family and its verdict cannot be read off the three cells that happened to fall below 0.05. Ask for all 24 p-values, report the surviving set, which may be empty, and demote the rest to hypotheses for a future test.
Refuse to call the decay novelty until the competing explanations are ruled out. Three things produce a falling weekly effect: a genuine novelty response from existing users that reverts, a mix shift because week three's exposed population is not week one's, and an effect that is correctly one-shot because a limit increase is taken once per customer.
Separate them with concrete cuts. Plot the effect against time since first exposure instead of calendar week, which removes the mix shift. Compare users onboarded during the test, who never saw the previous experience and therefore cannot show novelty, against existing users. Hold back a small fraction of traffic for a long-run read.
Fix the estimand to match the mechanism. For a one-shot offer the meaningful quantity is cumulative take-up per exposed user over a fixed horizon, not a weekly rate, and under that estimand a declining weekly rate is exactly what a working feature looks like.
Give the sponsor one number with its interval, name the decision it supports, and list the exploratory cells separately under a heading that says they are not results.

Worked solution (25 min)

Compute the chance baseline: 0.95^24 = 0.292, so P(at least one false positive) = 0.708 and the expected count is 1.2.
Settle the corrections the reported numbers can settle. Holm and Bonferroni both require the smallest p-value to clear 0.05 / 24 = 0.00208; 0.011 does not, so neither rejects anything, and that conclusion needs only the one number. Benjamini-Hochberg is different: with m = 24 and q = 0.10 the rank-k threshold is k / 240, which runs 0.00417, 0.00833, 0.0125 for the three reported cells (all three fail their own thresholds) but passes 0.05 at k = 13 (0.05417) and reaches 0.10 at k = 24. Because BH rejects the k smallest hypotheses whenever any p(k) clears its threshold, one of the 21 unreported p-values sitting between 0.05 and its own rank threshold would make BH reject every cell down to rank 1, the three included. Demand the full ordered vector of 24 p-values before stating a BH verdict.
Rebuild the weekly series on time since first exposure and overlay new versus existing users, then state which of novelty, mix shift and one-shot mechanics the pattern is consistent with.
Recompute the primary as cumulative take-up per exposed user at a fixed 21-day horizon and report that with its confidence interval as the headline.
Write the sponsor summary: one confirmatory number, the exploratory cells listed as non-results pending the full p-value vector, and a proposed follow-up test for the single most plausible segment hypothesis.

Expected result: Holm and Bonferroni reject nothing, and that follows from the reported numbers alone: the smallest p-value, 0.011, exceeds 0.05 / 24 = 0.00208. Benjamini-Hochberg at q = 0.10 is not decidable from three p-values, because it is a step-up test over all 24 whose rank-k threshold k / 240 passes 0.05 at k = 13, so the correct move is to demand the full ordered vector and name the condition that would flip it rather than assert that nothing survives. Until that vector arrives the three cells are exploratory, not findings. Only the pre-registered primary is confirmatory, restated as cumulative take-up over a fixed horizon. The 8.1 to 1.5 percent decay is not established as novelty; the correct answer names mix shift and the one-shot nature of a limit increase as competing explanations and gives the cuts that separate them.

Verify it yourself

Binomial sanity check: P(3 or more hits out of 24 at 0.05) is about 0.12 under independence, so three hits is unremarkable; correlation among the cells makes the true probability higher still.
BH self-check: the rank-k thresholds k * 0.10 / 24 must run from 0.00417 at k = 1 to 0.10 at k = 24 and cross 0.05 between k = 12 and k = 13. Any BH conclusion that uses only the p-values below 0.05 is ignoring the ranks where the threshold is loosest.
The cumulative take-up curve per exposed user should be monotone increasing and flatten, while the weekly rate falls. Both are the same data, which is the point.
New users during the test should show no decay if the decay is novelty. If they decay the same way, novelty is not the explanation.
Re-run the segment slice on an A/A period: if it also produces two or three significant cells, the slicing procedure itself is the finding.

Follow-up

The three significant cells are all in the same segment. Does that change your reading, and how would you test whether it is a real interaction rather than three correlated slices of one population?
Design the long-term holdback: what size, for how long, and what does it cost you in foregone treatment?
If cumulative take-up is the estimand, what is the right horizon and how do you keep it from being chosen after seeing the data?

Settled volume jumps after a new acceptance corridor launches

Medium · minor units · currency conversion · reconciliation

Settled payment volume per active customer rose 12 percent in the month a new acceptance corridor went live. The pipeline sums settlement_amount_minor from fct_payment_authorization and divides by 100 to get major units. Columns available: amount_minor, transaction_currency, captured_amount_minor, settlement_amount_minor, settlement_currency, settlement_fx_rate, settled_at, is_reversal, issuer_country, acquirer_country. Confirm or refute the 12 percent, and specify exactly the conversion the metric should be using.

Approach

Split the month-over-month increase by settlement_currency. If one currency carries nearly all of it while its transaction count barely moved, the problem is arithmetic rather than demand, and that is a two-minute check.
Look up each currency's ISO 4217 exponent. Dividing a three-decimal currency by 100 instead of 1000 overstates it tenfold; dividing a zero-decimal currency by 100 understates it hundredfold. Neither is a rounding issue.
Rewrite the conversion to scale settlement_amount_minor by ten to the power of that currency's exponent, then convert to the reporting currency. Do not reuse settlement_fx_rate for this; it is the rate applied between transaction and settlement currency at settlement time, not a reporting-currency rate.
Check the other half of the series too. settlement_amount_minor is denominated in settlement_currency while amount_minor is in transaction_currency, so any series that mixes the two is uninterpretable regardless of the exponent fix.
Exclude reversals and refunds, recompute, then reconcile the corrected total to the settlement ledger for the same period and show the tie-out in the write-up.
Recompute the per-customer denominator on the corrected month, since a new corridor adds customers as well as volume and the ratio can move either way once the numerator is right.

Follow-up

Where else in the warehouse does a hardcoded divide-by-100 appear, and how would you find every instance?
What test would fail the build the next time a currency with a different exponent is onboarded?
The reporting-currency rate is itself a choice. Which rate, on which date, and why does finance care about the answer?

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

Repeat the depth protocol on the second-ranked area with the same six-problem structure.
Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

Re-run the six prompts from day one under the same clock and compare both correctness and time.
Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

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.

Give an example of how you have influenced a team decision.

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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

How do you handle feedback and criticism in your work?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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

Turn a one-line fraud-number request into a scoped brief

Easy · scoping · metric definition · denominators

A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.

Approach

Establish the decision behind the question first, because a risk-rule change, a board number and a merchant contract negotiation need different denominators, and asking which one is not stalling.
Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
Commit to a default so the reply is useful even if nobody answers, for example net fraud loss in basis points of settled volume, attributed to the requested_at month, matured months only.
State the exclusions in the same breath as the default: non-fraud dispute categories, transaction months with less than 120 days of maturity, and first-party abuse that arrives coded as consumer_dispute.
Give a delivery time for the default and a longer one for the fuller cut, so the choice between them carries a visible cost.

Follow-up

They come back wanting it by merchant for a contract negotiation. What changes in the definition and in the maturity rule?
How would you separate first-party abuse from third-party fraud in this data, and what would you refuse to conclude from the split?