Interview guides · A.S. Watson Group

A.S. Watson Group Data Scientist Interview Questions & Guide 2026

Modeling drills for a A.S. Watson Group data scientist interview: baselines, regression interpretation, class imbalance, model defense.

The 60-second brief

As a Data Scientist at A.S. Watson Group, you are positioned at the heart of the world’s largest international health and beauty retailer. Your work directly influences how millions of customers interact with iconic brands across thousands of stores and digital platforms. This role is not merely about building models; it is about extracting actionable intelligence from massive, diverse datasets to drive strategic decision-making in retail operations, customer loyalty, and supply chain efficiency.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

Decompose expected loss into PD, LGD, EAD
Report only matured cohorts for loss metrics
Reconcile amounts in minor units and currency

Overview

You will contribute to a high-impact environment where data serves as the backbone for product-led growth. Whether you are optimizing personalized marketing campaigns, designing experiments to test new retail features, or diagnosing shifts in key performance metrics, your insights will have tangible business consequences. The role requires a blend of rigorous statistical discipline and product-sense, ensuring that every technical solution is grounded in the practical realities of the retail sector.

Expect to work in a collaborative, fast-paced atmosphere where you will be challenged to bridge the gap between complex data methodology and clear, executive-level communication. Success here is defined by your ability to navigate ambiguity, demonstrate intellectual curiosity, and deliver solutions that improve the customer experience at scale.

The A.S. Watson Group interview process

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

Initial Screening

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

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

Meet Senior Leadership

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Where candidates lose points

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.

Reading the most recent months of fraud and dispute rates as final

Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.

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.

Crediting a treatment for regression to the mean

Selecting a group because it is extreme (lowest-engagement users, accounts having their worst month, the bottom decile of a score) moves that group's expected next-period value back toward the average even under no treatment, by exactly as much as the selecting measure is imperfectly correlated with its own later value. Compare against units that met the same selection rule and went untreated, or use two pre-periods so the bounce-back is visible before the intervention starts. A pre-post number on a group chosen for being extreme measures the selection rule, not the treatment.

Questions & practice

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.

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?

Implement accident-quarter loss ratio at twelve months development

Medium · loss ratio · development age · earned premium

fct_policy_period_monthly arrives as a stack of month-end snapshots: each row carries valuation_month alongside as_of_month, policy_id, product_line, written_premium_minor, earned_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor. Compute the accident-quarter loss ratio at exactly 12 months of development: incurred losses over earned premium, both taken from rows whose as_of_month falls in the accident quarter, read from the snapshot 12 months after that quarter closes. Report quarters that cannot reach that age as incomplete rather than dropping them.

Approach

Derive accident_quarter from as_of_month, then define the evaluation snapshot per quarter as valuation_month equal to the quarter's final month plus twelve months. Every figure in the ratio comes from that one snapshot, not from whichever snapshot happens to be newest.
Numerator is paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor over the accident quarter's rows in that snapshot. Loss adjustment expense may be included or not, but the choice applies to every quarter and is named in an output column.
Denominator is earned_premium_minor over the same rows. Written premium is booked in full at inception, so in a growing book it runs ahead of earned premium and drags the ratio down, with the error reversing when the book shrinks.
Left-join the full quarter list against available valuation months so a quarter with no 12-month snapshot yields status incomplete and a null ratio, instead of disappearing and shortening the series without saying so.
Split by product_line, since both the loss ratio level and the speed of development differ by line, and a blended series moves with mix as much as with experience.

Follow-up

The most recent complete quarter came in four points better than the one before. What do you check before calling it an improvement?
How would you estimate the 12-month figure for a quarter that is only 6 months developed, and how would you label the estimate?
Why can an expense ratio legitimately use a different denominator from the loss ratio in the same presentation?

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.

Worked solution (30 min)

Compute the point estimate directly on the full data and keep it for comparison.
Aggregate to merchant-level loss and volume arrays, record M, and set B to 2,000 with a seeded numpy Generator.
In a vectorised loop, draw integer indices of shape (B, M), index both arrays, sum along axis 1, and take the ratio times 10,000.
Repeat for the row-level version using the per-transaction arrays and N draws.
Take the 2.5 and 97.5 percentiles of each replicate array and report both intervals alongside the point estimate.

Expected result: Two intervals in basis points sharing the same point estimate. The clustered interval is materially wider, commonly two to four times, and is right-skewed because a heavy merchant can be drawn more than once in a replicate.

Verify it yourself

The point estimate equals the direct ratio on the full data and lies inside both intervals.
A synthetic input where every merchant holds exactly one transaction makes the clustered and naive intervals coincide; this is the test that the resampling unit is wired correctly.
Raising B from 2,000 to 10,000 shifts the endpoints by less than the reporting precision; if it does not, a handful of merchants is driving the tail and the estimate needs that caveat.
Replicate ratios never fall outside the range implied by drawing only the lowest-loss or only the highest-loss merchants.

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 handle data quality issues when pulling from multiple disparate retail databases.

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

Follow-up

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

How do you use SQL window functions to calculate rolling averages or identify trends over time?

Medium · sql and data manipulation

Approach

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

Follow-up

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

Vintage ninety-plus rate at twelve months on book

Medium · vintage analysis · window functions · right censoring

From fct_loan_performance_monthly, build a vintage table keyed on origination_month: the share of each cohort that ever reached days_past_due of 90 or more, or charge_off_flag = true, at or before months_on_book = 12. Restructuring resets days_past_due, so for any loan with restructured_flag true, evaluate only the month ends strictly before its first restructured month. Return origination_month, loans_funded, bad_loans and bad_rate. Exclude any cohort that does not yet have a months_on_book = 12 observation for every loan still on book.

Approach

Establish the cohort denominator from the first month end each loan appears at, months_on_book = 0, so a loan is counted once in its origination_month rather than once per monthly row.
Find each loan's first restructured month with MIN(as_of_month_end) FILTER (WHERE restructured_flag) OVER (PARTITION BY loan_id), or the equivalent grouped subquery, and keep it null for loans never restructured.
Flag a loan bad if any row with months_on_book <= 12 and as_of_month_end earlier than that first restructured month has days_past_due >= 90 or charge_off_flag = true, which is what 'pre-restructure worst state' means in practice.
Gate maturity by requiring the cohort's newest month end to be at least 12 months after origination_month, and report immature cohorts as incomplete rather than letting them appear at a flattering low rate.
Aggregate to one row per origination_month and read the column downward, not across calendar time, because the whole point is comparing cohorts at equal age.

Worked solution (35 min)

CTE 1: per loan_id, derive origination_month, the first restructured month end, and the maximum months_on_book observed.
CTE 2: join back to the monthly rows, filter to months_on_book <= 12 and to month ends before any restructure, then flag bad with a BOOL_OR.
CTE 3: aggregate to origination_month with COUNT() as loans_funded and COUNT() FILTER (WHERE is_bad) as bad_loans.
Apply the maturity gate and compute bad_rate with a numeric cast.

Expected result: One row per matured origination_month with loans_funded, bad_loans and a bad_rate between 0 and 1. The most recent immature cohorts are absent from the output entirely rather than present with a small bad_rate.

Verify it yourself

SUM(loans_funded) across reported cohorts equals COUNT(DISTINCT loan_id) restricted to those same origination months.
Recomputing without the months_on_book cap raises bad_rate monotonically for every cohort, and raises it most for the oldest, which is the seasoning effect made visible.
No cohort in the output has a maximum months_on_book below 12.

Follow-up

Should a restructure inside 12 months count as bad in its own right? Argue both sides and say what you would actually ship.
A loan that prepaid in full at month 4 never had a chance to go 90 days past due. In or out of the denominator, and why?
The 2025-11 cohort is two points worse at month 12 than its neighbours. What three queries do you run before you call it a credit-quality change?

How do you balance long-term user retention with short-term transaction volume?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.
Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

Propose a way to evaluate if a new personalized recommendation engine is actually helping the user.

Medium · product sense and metrics

Approach

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

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 you notice a sudden drop in a key product metric, what steps would you take to diagnose the root cause?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
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.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
Which segment would you cut first, and what would that rule out?

How would you design a metric to measure the success of a new loyalty program 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.
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?
How would you detect that the metric is being gamed rather than genuinely improving?

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

Medium · a/b testing and experimentation

Approach

State the primary metric and the minimum effect worth shipping, then size the test.
Name the randomisation unit first; it decides the variance and what the test can detect.
Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

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

Walk me through the process of setting up an A/B test for a new website checkout flow.

Medium · a/b testing and experimentation

Approach

State the primary metric and the minimum effect worth shipping, then size the test.
Name the randomisation unit first; it decides the variance and what the test can detect.
Say whether units interfere with each other, and switch design if they do.

Follow-up

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?

Success metrics for loosening a fraud decline threshold

Medium · cost asymmetry · guardrails · cohort maturity

A risk team proposes lowering the risk_score cutoff that produces auth_result = 'declined_risk_rule'. Settled volume per active customer is the north star; net fraud loss in basis points of settled volume is the guardrail. The two move in opposite directions by construction. Specify the readout: primary metric, guardrail, the maturity window each is read at, and the decision rule agreed before launch. Show the expected-cost arithmetic that sets the cutoff using an average ticket of 200 units, a 1.5 percent contribution margin, 35 percent recovery on fraud losses, and 12 units of downstream value lost per false decline.

Approach

Refuse the two-metric framing and convert both sides into one currency. Approving a fraudulent transaction costs the amount net of recovery; declining a good one costs the forgone margin plus the downstream value of the customer's reaction. Decline when p times C_FN exceeds (1 minus p) times C_FP, so the break-even probability is p* = C_FP / (C_FP + C_FN).
Put the numbers in. C_FN = 200 times (1 minus 0.35) = 130, C_FP = 200 times 0.015 plus 12 = 15, so p* = 15 / 145 = 10.3 percent. Then show the threshold is amount-dependent: at a 2,000 ticket C_FN = 1,300 and C_FP = 42, giving p* = 3.1 percent, so a single global cutoff is already the wrong shape before any tuning starts.
State the precondition that makes this arithmetic legal: risk_score has to be calibrated, so that a score of 0.10 corresponds to an observed 10 percent fraud rate. A score that only ranks makes p* meaningless. Check the reliability curve before quoting any cutoff to anyone.
Set the maturity windows separately. Volume is readable within days, fraud loss is not, so the guardrail is read only on transaction months with at least 120 days of dispute maturity and the decision stays open until then, or a leading indicator is agreed in advance with its bias written down.
Agree the stopping rule before launch in the right units: revert if matured net fraud loss per unit of incremental settled volume exceeds the figure implied by p*. Fraud loss is supposed to rise when the cutoff loosens, so a rule that triggers on any rise is a rule that was never going to allow the change.
Report the swap set rather than portfolio totals: the transactions the new cutoff approves that the old one declined, and their realised loss rate. Portfolio aggregates dilute the change into invisibility.

Worked solution (30 min)

Compute p* at ticket sizes of 50, 200 and 2,000 with the given margin, recovery and false-decline cost, and tabulate them.
Bucket historical declined_risk_rule authorizations by risk_score decile and, for each bucket, write down what outcome data exists and what does not.
Write the readout spec: primary metric, guardrail, the 120-day maturity rule, the swap-set table and the numeric stopping rule.
Write the calibration precondition in two sentences and say how you would test it.

Expected result: A threshold table giving p* = 28.2 percent at a 50 ticket, 10.3 percent at 200 and 3.1 percent at 2,000, plus a readout spec whose guardrail is explicitly unreadable for 120 days and whose stopping rule is expressed as loss per incremental unit of settled volume rather than as a loss level.

Verify it yourself

p* falls as ticket size rises, because fraud loss scales with the amount while the false-decline cost is mostly a fixed relationship term.
Setting C_FP equal to C_FN returns p* = 0.5. If your formula does not, the numerator and denominator are the wrong way round.
The declined population carries no fraud labels, so any realised fraud rate quoted for the region below the old cutoff has to come from a randomised approval sample rather than from the historical table.

Follow-up

Fraud loss in basis points falls after launch. Name two ways that happens without any improvement in decisioning.
How do you keep observing outcomes in the region the rule still declines?
What changes if the 12 units of downstream value is a guess with no evidence behind it?

Trailing thirty day volume per customer drops week over week

Hard · seasonality · day of week · window alignment

The trailing 30-day settled volume per active customer is down 7 percent against the same metric seven days earlier. Nothing shipped. You have fct_payment_authorization with requested_at, channel, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, plus dim_customer with is_current, kyc_status, onboarded_at and closed_at for the active denominator. Before anyone writes a retention narrative, decide how much of the 7 percent is calendar structure, and hand back a calendar-robust version of the comparison.

Approach

Do the window arithmetic first. Thirty days is four whole weeks plus two days, so exactly two weekdays appear five times and the other five appear four times. Sliding the window by seven days changes which two, and card-present and card-not-present volumes differ sharply by weekday.
Rebuild on a 28-day window, which contains exactly four of every weekday, and see how much of the 7 percent survives. That single change removes the weekday composition effect with no modelling and no assumptions.
Count the structural events inside each window: public holidays, and the billing anchor days that recurring authorizations cluster on. A window holding one fewer month boundary loses a block of recurring volume that has nothing to do with customer behaviour.
Decompose by channel, since recurring, card_present and ecommerce have different calendar signatures. A drop concentrated in recurring points at anchor-day placement; one spread evenly across channels does not.
Compare year over year at a 364-day lag rather than 365, which preserves weekday alignment, and only then read the residual.
Check the denominator on its own. Active customer counts on a trailing window carry their own calendar structure, and a ratio can move because either side moved.

Follow-up

Which window goes on the executive dashboard, and what do you give up by choosing it?
How would you handle a holiday that moves between years, so that a 364-day lag still misaligns it?
If a genuine 2 percent residual survives, what is the smallest cut that tells you whether it is breadth or depth?

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.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Tell me about a time you received constructive feedback on your technical work and how you acted on it.

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Pick a story where you drove the decision, not one where you observed it.
Quantify the outcome, including what you would not claim credit for.

Follow-up

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

State honestly what your cutoff change actually contributed

Hard · impact attribution · swap set · counterfactual

Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.

Approach

Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.

Follow-up

The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?

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?