Interview guides · Bgb Group

Bgb Group Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

The role of a Data Scientist at Bgb Group is crucial in transforming vast amounts of data into actionable insights that drive strategic decision-making. As a key member of our data team, you will harness advanced analytical techniques and machine learning methodologies to enhance our products, optimize user experiences, and support business growth. This role involves collaborating closely with cross-functional teams, including engineering, product management, and marketing, to develop solutions that are not only innovative but also grounded in data.

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

Separate authorization, settlement and dispute outcomes cleanly
Reconcile amounts in minor units and currency
Set fraud thresholds by expected cost

Overview

You will impact various products and initiatives within Bgb Group, from consumer analytics to operational efficiency projects. The complexity and scale of the data challenges you will face are significant, giving you the opportunity to influence the direction of our services and enhance our market competitiveness. Expect to work on exciting and complex problems that require both technical expertise and creative thinking, making your contributions critical to our success.

The Bgb Group interview process

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

Initial Screening

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

What to demonstrate

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

How to prepare

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

Technical Assessment

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Behavioral Interview

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

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.

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.

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.

SQL that silently fans out on a one-to-many join

State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.

Questions & practice

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

Medium · statistics and probability

Approach

Say what the estimate is of, and over what population it generalises.
Write down the assumption the method needs before you use the method.
Quantify uncertainty explicitly rather than reporting a point estimate alone.

Follow-up

How would you explain this result to someone who does not know statistics?
Which assumption here is most likely to be violated in practice?

What are some common metrics used to evaluate classification models?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
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?
Where could label leakage enter this setup?

Describe the importance of feature engineering in a machine learning model.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Check what information would not exist at prediction time, and exclude it.

Follow-up

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

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.

Worked solution (30 min)

Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
Filter rows to those where valuation_month equals the row's target_valuation, then assert each accident_quarter has exactly one distinct valuation_month left.
Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
Recompute one quarter by hand on a five-policy subset and confirm it matches.

Expected result: One row per accident_quarter and product_line with incurred, earned_premium, loss_ratio, an lae_included flag and status in complete or incomplete. Complete quarters end twelve months before the latest valuation_month, so the four or five most recent quarters carry null ratios.

Verify it yourself

Assert exactly one distinct valuation_month per accident quarter after filtering; more than one means the snapshot join fanned out.
For a growing book, earned premium in a quarter is below written premium booked in that quarter; if it is above, either the book is shrinking or the accident-quarter derivation is wrong.
Incomplete quarters carry null ratios and never appear on the same line as complete ones in any chart.
Swapping the denominator to written premium shifts a quarter by L(W - E)/(EW), so the sign of the change is the sign of written minus earned premium in that quarter: the ratio falls where the book grew and rises where it shrank. Verify the swap per quarter against sign(W - E) rather than expecting a uniform move; a series that moves in one direction everywhere only means the book grew in every quarter.

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?

Can you show me how to use a library like Pandas to manipulate a dataset?

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.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

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.

How would you prioritize multiple data requests from different teams?

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.
Restate the decision this analysis has to support, and who acts on the answer.

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?

How would you approach a project aimed at increasing user retention for a product?

Medium · product sense and metrics

Approach

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.
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?
Which segment would you cut first, and what would that rule out?

Given a dataset with user behavior, how would you identify patterns that drive engagement?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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?

Describe a situation where you optimized a piece of code for performance.

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.

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?

Explain the difference between supervised and unsupervised learning.

Medium

Approach

State your assumptions explicitly before working the problem.
Work from the decision backwards to the evidence you would need.
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?

Design an authorization approval rate a team cannot game

Easy · approval rate · metric gaming · mix shift

A payments team is given authorization approval rate as its quarterly target, defined as approved authorizations over all authorizations in fct_payment_authorization. Using auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, auth_result, is_reversal, parent_auth_id, channel, issuer_country and requested_at, list three ways the team can raise that number without a single additional customer purchase succeeding. Then write the definition you would put on the dashboard instead, with its exclusions, and name the one guardrail you would always read beside it.

Approach

Enumerate the rows in the table that are not purchases: zero-amount account verifications, reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and merchant retries of a declined attempt. Each is a lever on the numerator or the denominator that no customer would ever notice.
Name the mix levers. Recurring and card-present traffic approve at higher rates than ecommerce and cross-border, so routing changes or a marketing push into an easy channel move the blended rate with no change in decisioning quality at all.
Work the retry arithmetic rather than asserting it. A declined purchase that is retried three times and succeeds on the fourth contributes one approval and four denominator rows, which is below a healthy blended rate, so suppressing retries raises the published number while some purchases that would have succeeded on a retry now simply fail.
Replace the definition: dollar-weighted, retry chains collapsed to a single attempt per (card_token_id, merchant_id, amount_minor) within a 15-minute window, excluding reversals, zero-amount verifications and incremental authorizations, with all amounts converted into one reporting currency before summing because amount_minor is in the minor unit of transaction_currency and that unit is not always two decimals. Cut by channel and issuer_country so mix shift shows up as mix shift.
Guardrail: net fraud loss in basis points of settled volume, read only on transaction months with at least 120 days of dispute maturity. The fastest way to approve more is to decline less risk, and that bill arrives two to four months later.

Worked solution (25 min)

Compute the naive rate for one week: approved rows over all rows.
Apply the exclusions one at a time (zero-amount, is_reversal, parent_auth_id not null), recording the rate after each, so you can say what each row type was worth to the headline number.
Add a retry-collapsing CTE keyed on (card_token_id, merchant_id, amount_minor) over a 15-minute window, keeping the last attempt in each chain, and recompute.
Recompute dollar-weighted in a single reporting currency, then split by channel and issuer_country.
Write the final one-line definition with its exclusions, plus the guardrail sentence.

Expected result: Four rates for the same week (naive count, count after exclusions, count after retry collapsing, dollar-weighted after both), with the largest single gap attributed to a named row type, and a published definition that states every exclusion and the currency it reports in.

Verify it yourself

Excluding zero-amount verifications should lower the rate, since those rows approve at a higher rate than purchases. If it raises the rate, confirm that the amount_minor = 0 filter is not also catching rows with a null amount.
Collapsing retry chains raises the rate and shrinks the denominator. The numerator falls only by the number of chains that contained more than one approval.
Declines skew toward higher-value, cross-border and card-not-present transactions, so expect the dollar-weighted rate to sit below the count-weighted one. If it sits above, check for amounts summed across currencies before believing it.

Follow-up

The blended rate is flat but ecommerce cross-border fell 200 basis points. How do you present that?
Why weight by amount rather than by count, and when does the weighted version mislead?
Your 15-minute retry window merges two genuine purchases of the same amount at the same merchant. How would you bound that error?

Manual review rate doubles on the day two changes ship

Hard · definition change · model version · population stability

Manual review rate on fct_loan_application, defined as decided_by = 'manual' over decisions in ('approve','decline'), stepped from 9 to 18 percent on a single day. That day a new model_version went live, and the same release changed which referral paths are recorded as manual. Columns: submitted_at, channel, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. Attribute the step across definition, model and population, with a number against each.

Approach

Establish the shape before the cause. A definition or code change produces a step at the deploy timestamp; a population change produces a ramp. Plot at hourly granularity either side of the release and read which one this is.
Dual-run the definition. Recompute the metric under both the old and the new recording rules across several months of history. If the historical series under the new rule sits near 18 percent throughout, that part of the step is definitional and should be restated, not investigated.
Hold the model constant and decompose the residual by policy_rule_hits identifier. A referral rule keyed to a score threshold shows its own step, and naming the rule is what makes the finding actionable to the policy owner.
Test the score, not the applicants. Compare model_pd_12m distributions by model_version on the same applications where shadow scores exist; otherwise compare adjacent weeks with a population stability index, treating the customary 0.1 and 0.25 marks as heuristics rather than tests, and assess calibration separately from ranking.
Test the applicants, not the score. Compare channel mix, bureau_score distribution and null-bureau share across the boundary. If those are stable, population is not a driver and should be reported as approximately zero rather than left unquantified.
Deliver three components that sum to the observed step, each with the team that owns it.

Follow-up

If the new model is better calibrated, is a higher referral rate the correct outcome or a threshold nobody retuned?
How would you have caught the recording change before it reached the dashboard?
What is the swap set here, and which swap group would you inspect first?

Suggested practice checklist

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

Day 1 — Fix the scope and set a baseline

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

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

Day 2 — One query pattern, written three times

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

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

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

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

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

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

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

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

Day 5 — Your own work, timed

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

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

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

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

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

Day 7 — Taper

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

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

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Tell me about a time you faced a significant obstacle while working on a project. How did you overcome it?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Retract a published number after finding a currency bug

Medium · error disclosure · minor units · process repair

Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

Approach

Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.

Follow-up

The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
What automated check would have caught this, where would it live, and what would it cost in false alarms?

Allocate one analyst-week across three competing risk requests

Medium · prioritisation · decision deadlines · stakeholder negotiation

Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

Approach

Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.

Follow-up

The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?