BT Group Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at BT Group, you occupy a pivotal role in transforming one of the world’s most established telecommunications organizations into a data-driven powerhouse. Your work directly influences how BT Group optimizes network performance, manages customer lifecycles, and develops the next generation of digital services. You aren't just building models; you are solving high-stakes problems that impact millions of users across the United Kingdom.

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

• Reconcile amounts in minor units and currency
• Decompose expected loss into PD, LGD, EAD
• Read vintage curves, not blended portfolio averages

Overview

The environment at BT Group is characterized by scale and complexity. You will collaborate across cross-functional teams, bridging the gap between raw data infrastructure and actionable business strategy. Whether you are diagnosing a sudden drop in product metrics or designing a robust experimentation framework for a new service, your output serves as the foundation for critical executive decisions. This role is ideal for those who thrive when balancing rigorous statistical analysis with clear, product-centric communication.

The BT Group interview process

BT 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

Scenario-Based Evaluations

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

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

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.

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.

Extrapolating a first-week lift inflated by novelty effects

Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.

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.

Questions & practice

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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)

1. Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
2. 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.
3. Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
4. Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
5. 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?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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?

Estimate a delinquency roll-rate matrix and project twelve months

Hard · roll rates · markov chain · survivorship

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

Approach

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

Follow-up

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

How would you handle null values in a large-scale join operation?

Medium · sql and data manipulation

Approach

1. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
2. Say which table is the grain you start from, and join outward from it.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

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

Write a SQL query to identify the top 3 customers by spend per region using window functions.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Say which table is the grain you start from, and join outward from it.
3. 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 does the query change if the join becomes one-to-many?

Explain the difference between RANK, DENSE_RANK, and ROW_NUMBER in practice.

Medium · sql and data manipulation

Approach

1. Check whether any join is one-to-many before aggregating, or the sums inflate.
2. Say which table is the grain you start from, and join outward from it.
3. State the window function and its partition and ordering out loud before writing it.

Follow-up

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

Reconcile captured authorizations against the daily settlement total

Easy · reconciliation · currency · rounding

fct_payment_authorization holds captured_amount_minor in transaction_currency, and settlement_amount_minor in settlement_currency with settlement_fx_rate applied at settlement rather than at authorization. The rate is quoted in major units of settlement_currency per major unit of transaction_currency, and dim_currency.minor_unit_exponent carries the ISO 4217 exponent for each code (0, 2 or 3 depending on the currency). Produce a daily reconciliation: for each settled_at date and settlement_currency, return settled_count, total settlement_amount_minor, and the sum of captured_amount_minor converted into settlement minor units. Flag any date and currency pair whose two totals differ by more than one minor unit per settled authorization. Do not sum amounts across currencies anywhere in the output.

Approach

1. Restrict to rows that actually settled: settled_at is not null and settlement_amount_minor is not null, which is a smaller population than captured rows because a capture can still be in flight.
2. Truncate settled_at to a date with an explicit time zone so the cut matches the ledger's cut, since settled_at is timestamptz and date_trunc on timestamptz silently uses the session time zone.
3. Join dim_currency twice, once on transaction_currency and once on settlement_currency, so both exponents are on the row. Minor units are not a common scale: a bare captured_amount_minor * settlement_fx_rate is correct only when the two exponents are equal, and a zero-decimal currency settling into a two-decimal one is wrong by a factor of 100.
4. Convert per row as ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle)) — minor units to major in the transaction currency, apply the major-per-major rate, then back to minor units in the settlement currency. The collapsed form ROUND(captured_amount_minor::numeric * settlement_fx_rate * POWER(10::numeric, exp_settle - exp_txn)) is the same expression. Round per row and then sum, not SUM(...) * an average rate, because the rate varies row by row and rounding per row is what the settlement file did.
5. Group by the settlement date and settlement_currency together, never by date alone, and carry the currency into every output column name or row.
6. Compare the two totals with a tolerance scaled by settled_count, since per-row rounding accumulates linearly in the number of rows rather than being a fixed constant.

Worked solution (25 min)

1. Filter to settled rows and derive settlement_date from settled_at with an explicit time zone.
2. Join dim_currency on transaction_currency and again on settlement_currency to pick up exp_txn and exp_settle; fail the run if either is null rather than defaulting to 2.
3. Aggregate by settlement_date and settlement_currency: COUNT(*), SUM(settlement_amount_minor), and SUM(ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle))).
4. Add a derived difference column and a boolean flag where ABS(difference) > settled_count.
5. Order by the flag first and then by settlement_date so the exceptions surface at the top.

Expected result: One row per settlement_date and settlement_currency, carrying settled_count, settlement_total_minor, converted_capture_total_minor, their signed difference, and a boolean exception flag. On clean days the difference is small relative to settled_count and has no consistent sign. Pairs with unequal exponents reconcile exactly as well as equal-exponent pairs; if they do not, the exponent terms are missing rather than the FX rate being wrong.

Verify it yourself

• The difference divided by settled_count stays well under one minor unit on unflagged days, which is the expected size of per-row rounding.
• Restricted to rows where exp_txn = exp_settle, the exponent-aware conversion and a bare captured_amount_minor * settlement_fx_rate agree exactly; restricted to rows where the exponents differ by 2, the bare form is smaller by exactly a factor of 100. That is the direct test that the dim_currency join is load-bearing.
• Re-running with SUM over all currencies at once produces a number that changes when a zero-decimal currency's volume moves, which demonstrates why the cross-currency total is meaningless.
• settled_count summed over all dates equals COUNT(*) on the same settled-row filter with no grouping.

Follow-up

• A partial capture means captured_amount_minor is less than amount_minor. Where does that show up in this reconciliation, and where does it not?
• On one currency pair the converted total is consistently about one hundredth of the settlement total, on every date, while the other pairs reconcile. Which two columns do you inspect first, and what single change fixes it?
• The rate is documented as major-per-major. If a feed started publishing it minor-per-minor instead, which pairs would still reconcile and which would break?
• How would you present a total across currencies to a finance partner who has asked for one number?

How would you design a metric to measure the success of a new customer support chatbot?

Medium · product sense and metrics

Approach

1. Name one primary metric, then the guardrail that stops it being gamed.
2. Fix the population and the time window before naming any metric.
3. 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?

How do you define the success criteria for a new feature deployment?

Medium · product sense and metrics

Approach

1. Decompose the metric into the rates that drive it, and say which one you would check first.
2. Name one primary metric, then the guardrail that stops it being gamed.
3. 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?
• What would you do if the primary metric and the guardrail moved in opposite directions?

If you notice a sudden, significant drop in daily active users for a specific product, what steps would you take to diagnose the root cause?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Restate the decision this analysis has to support, and who acts on the answer.
3. Fix the population and the time window before naming any metric.

Follow-up

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

What are common pitfalls in A/B testing, and how do you mitigate selection bias?

Medium · a/b testing and experimentation

Approach

1. Decide the analysis before seeing data, including how long it runs and when you look.
2. Name the randomisation unit first; it decides the variance and what the test can detect.
3. Say whether units interfere with each other, and switch design if they do.

Follow-up

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

How do you determine the required sample size for an A/B test to achieve statistical significance?

Medium · a/b testing and experimentation

Approach

1. Name the guardrails that would stop a launch even on a positive primary result.
2. Name the randomisation unit first; it decides the variance and what the test can detect.
3. State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

Price a false decline when the label does not exist

Hard · proxy metrics · selection bias · randomised holdout

Your expected-cost threshold needs a figure for what a false decline costs beyond the margin on the blocked transaction. fct_payment_authorization records the decline, nothing records what the customer did next elsewhere, and declined transactions never produce a fraud outcome, so neither side of the error is directly observable. Propose the measurement: the proxy you would build from the tables you have, the design that yields an unbiased estimate for at least part of the score range, the bias in each, and the single sentence you would attach to the number when it reaches a pricing decision.

Approach

1. Write down what is unobservable and why. The counterfactual spend of a customer who was not declined, and the fraud label on any transaction the rule blocked, are both missing because of the decision itself. Missingness that depends on the decision is not fixed by matching on observed covariates.
2. Build the observational proxy anyway and be specific. For customers receiving a first risk-rule decline in a window, compare settled volume and active status over the following 30 and 90 days against customers matched on pre-period settled volume, tenure, segment and channel mix who attempted a comparable transaction and were approved. The bias runs one way: matching conditions on having attempted something that scored near the cutoff, and part of the declined group are genuine fraudsters whose disappearance is a saving rather than a loss, so the estimate overstates the damage.
3. Buy one unbiased local estimate. Hold a small random share of authorizations inside a defined risk_score band out of the decline rule and approve them, sizing the sample in advance from the expected fraud rate in that band so the cost of the experiment is known before it runs. The result is unbiased for that band only, and it is simultaneously the only source of fraud labels in the declined region.
4. Bound the extrapolation instead of hiding it. Run the holdout in two or three adjacent bands and report the spread. If the effect is flat across bands a constant is defensible; if it is steep, quote band-specific figures and decline to supply a single number.
5. Handle the window. Attrition after a decline can resolve over months, so a 90-day window truncates it and the randomised estimate is a lower bound on long-run damage at the same time as the observational version is an upper bound. Stating both directions is what makes the number safe to use.
6. Write the sentence that travels with the number: what it is (an estimate from a randomised holdout in one score band over a 90-day window), what it is not (a measurement anywhere else on the score range), and which way it is likely to be wrong.

Worked solution (40 min)

1. Define the decline cohort and the matched comparison cohort precisely, including matching variables and the pre-period window, and produce the 30-day and 90-day settled-volume difference.
2. Decompose the declined cohort into customers who never transact again and customers who transact less, since fraudsters concentrate in the first group and that split tells you how much of the estimate is contamination.
3. Write the holdout design: score band, sample share, expected fraud rate in band, expected cost of running it, and the run length needed to detect an effect large enough to change the cutoff.
4. Recompute p* = C_FP / (C_FP + C_FN) at the top and bottom of your estimated cost range and state whether the range changes the cutoff you would set.
5. Write the one-sentence caveat that will be quoted alongside the number in the pricing decision.

Expected result: Two numbers with opposite biases, an observational estimate labelled as an upper bound and a randomised in-band estimate labelled unbiased for that band only, plus a sensitivity table showing how p* moves across the range and an explicit statement of whether the residual uncertainty changes the decision.

Verify it yourself

• If the cost range moves p* by less than the granularity at which the rule can actually be set, further measurement is not worth buying and that is the finding.
• The observational estimate should exceed the randomised one in magnitude. If it does not, the matching is capturing something other than the effect of the decline and needs re-examining.
• Removing customers with zero prior settled volume from both cohorts should shrink the observational estimate, since new-account fraud concentrates there.

Follow-up

• Compliance and finance both object to deliberately approving transactions you believe are fraudulent. What is your answer, and how do you size the holdout?
• Your interval spans the decision boundary. What do you recommend?
• How would you detect that this number has gone stale?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

• Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
• Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
• State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

• Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
• Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
• Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

• Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
• Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
• Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

• Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
• Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
• Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

• Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
• Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
• List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

• Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
• Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
• Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

• Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
• Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
• Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

Bring numbers, including the ones you are unsure of

An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.

What are your personal professional values, and how do they align with a large organization like BT Group?

Medium · behavioural and stakeholder questions

Approach

1. Quantify the outcome, including what you would not claim credit for.
2. Pick a story where you drove the decision, not one where you observed it.
3. 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?

Disagree with a product manager over an approval-rate target

Medium · metric design · denominators · influence without authority

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

Approach

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

Follow-up

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

Defend a vintage finding that contradicts the portfolio dashboard

Medium · vintage analysis · mix shift · stakeholder pushback

The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.

Approach

1. Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
2. Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
3. Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
4. Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
5. Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.

Follow-up

• Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
• Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?