Interview guides · La Banque Postale

La Banque Postale Data Scientist Interview Questions & Guide 2026

Causal inference drills for La Banque Postale data scientist prep: diff-in-diff, matching, instruments, and the limits of observed data.

The 60-second brief

As a Data Scientist at La Banque Postale, you are at the intersection of traditional banking rigor and modern digital transformation. Your role is pivotal in leveraging data to enhance customer experiences, optimize internal processes, and support the strategic vision of one of France’s most trusted financial institutions. You will work within complex, cross-functional environments where your analytical outputs directly influence decision-making across various business units.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Reconcile amounts in minor units and currency
Set fraud thresholds by expected cost
Read vintage curves, not blended portfolio averages

Overview

The work is both challenging and rewarding, requiring you to bridge the gap between abstract technical modeling and practical, actionable business outcomes. Whether you are working within the Inspection Générale or supporting transversal business lines, you will be expected to translate complex data problems into clear, scalable solutions that adhere to the high security and compliance standards inherent in the banking sector.

Your ability to explain the "why" behind your technical choices is just as important as the code you write. Focus on connecting your models to the specific business goals of La Banque Postale.

The La Banque Postale interview process

La Banque Postale candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Technical Assessment

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

What to demonstrate

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

How to prepare

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

Project Discussion

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.

Management Interaction

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Final Interviews

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

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.

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.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

Questions & practice

Explain the difference between bagging and boosting and when you would prefer one over the other.

Medium · statistics and probability

Approach

Translate the result into the decision it informs, in one plain sentence.
Say what the estimate is of, and over what population it generalises.
Write down the assumption the method needs before you use the method.

Follow-up

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

Describe the trade-offs between interpretability and accuracy in model selection.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

How would you approach a classification problem given an imbalanced dataset in a fraud detection context?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Check what information would not exist at prediction time, and exclude it.
Set a baseline first, so any model has something honest to beat.

Follow-up

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

What is your process for moving a model from a notebook environment into a production-ready pipeline?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Check what information would not exist at prediction time, and exclude it.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Estimate a delinquency roll-rate matrix and project twelve months

Hard · roll rates · markov chain · survivorship

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

Approach

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

Worked solution (45 min)

Sort by loan_id and as_of_month_end, shift to form (from_state, to_state) pairs, and flag pairs whose month gap is not exactly one.
For each loan's final row, assign the absorbing destination from charge_off_flag or prepaid_in_full_flag, and list loans that vanish with neither as an exception count to report.
Apply the restructure rule, then build the 7 by 7 count matrix with a cross-tabulation over ordered state categories and row-normalise it.
Assert row sums equal one and absorbing rows are the identity, then take the current month's bucket distribution as a row vector.
Multiply twelve times, report the charged_off component, and repeat from an all-current vector for comparison.

Expected result: A 7 by 7 row-stochastic matrix with identity rows for charged_off and prepaid, a current-to-current diagonal typically above 0.95, roll rates rising with bucket depth, and a 12-month projected charge-off share of a few percentage points from an all-current start and materially higher from the actual book.

Verify it yourself

Every row sums to one within floating-point tolerance, and both absorbing rows are exactly the identity.
No mass sits in cells that skip a bucket upward, such as dpd_1_29 to dpd_60_89 in one month; if there is, the pairs are not actually consecutive months.
The projection from an all-current start is strictly below the projection from the real book distribution.
Backtest by estimating on months up to t minus 12 and comparing the projected distribution with the realised one at t; a systematic optimistic gap points at dropped exits or at seasoning, not at noise.

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?

Accident-quarter loss ratio on earned rather than written premium

Hard · loss ratio · earned vs written · development age

From fct_policy_period_monthly, compute the accident-quarter loss ratio by product_line: incurred losses, being paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor, over earned_premium_minor for the same accident quarter. State explicitly whether loss_adjustment_expense_minor is included and apply that choice consistently. Also output the same ratio computed on written_premium_minor so the two can be compared. The table holds current values with no valuation-date snapshot. Say in one line which comparison this schema cannot support and what you would need to support it.

Approach

Derive the accident quarter from as_of_month with date_trunc, and note that the table already attributes losses to the month of the loss event while earning premium pro rata into the same month, which is what makes the two sides comparable at all.
Aggregate earned_premium_minor, written_premium_minor and the three loss components to product_line and accident quarter in one pass, keeping loss adjustment expense as its own column so the inclusion choice is a final-select decision rather than something buried in a CTE.
Compute both ratios side by side and a third column for their difference, because the size and sign of that difference is a direct read on whether the book grew or shrank in the quarter.
State the limitation plainly: every row carries today's reserve estimate, so each accident quarter is observed at a different development age and a cross-quarter comparison mixes development with underwriting. A fixed development age needs a valuation-date dimension, that is one row per accident period per valuation, which this table does not have.
Guard against the mirror-image error on the numerator by confirming ibnr_reserve_minor is non-zero on recent quarters; if it is null or zero there, the recent periods are understated twice over and the series is not usable.

Worked solution (40 min)

CTE quarterly: group fct_policy_period_monthly by product_line and date_trunc('quarter', as_of_month), summing earned_premium_minor, written_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor.
Final SELECT: build incurred_minor as the three loss components plus the LAE column, with the LAE inclusion written as a named expression so the choice is visible on the page.
Emit loss_ratio_earned and loss_ratio_written, both cast to numeric, plus their difference and the written-to-earned premium ratio.
Order by product_line and accident quarter, and append the one-line note about the missing valuation dimension to the query as a comment.

Expected result: One row per product_line and accident quarter with earned and written premium, incurred losses, both ratios and their difference. In quarters where written premium exceeds earned, loss_ratio_written is strictly below loss_ratio_earned, and the gap widens with the growth rate.

Verify it yourself

For a mature product line with flat volume, summed earned premium over a full policy term is close to summed written premium, so the two ratios converge there.
The sign of loss_ratio_earned minus loss_ratio_written matches the sign of written premium minus earned premium in every quarter, which is an arithmetic identity and not a data finding.
Recent accident quarters have non-zero ibnr_reserve_minor; a zero there means the numerator is missing its tail and the ratio is understated.
Within a quarter, loss_ratio_earned divided by loss_ratio_written equals written_premium_minor divided by earned_premium_minor exactly, because both ratios share one numerator. Any deviation means the LAE term was built into one ratio and not the other, which is the real test that the inclusion choice was applied consistently.
Recomputing with LAE excluded scales each quarter's ratio by incurred_minor / (incurred_minor + lae_minor). That factor is not constant across quarters unless LAE happens to be booked as a fixed fraction of incurred losses, so a uniform shift is evidence about how LAE is loaded, not evidence that the query is self-consistent — use the previous check for that.

Follow-up

Written premium exceeds earned premium by 18 percent this quarter and by 3 percent two years ago. What happened to the book, and what does it do to each ratio?
How would you build a development triangle from a valuation-dated version of this table, and what would you use the chain-ladder factors for?
Statutory presentation conventionally takes the expense ratio on written premium while the loss ratio uses earned. How do you avoid a combined ratio that quietly mixes the two bases?

Attribute chargebacks to transaction month without fanning out

Medium · fan-out · cohort attribution · maturity

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

Approach

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

Follow-up

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

Can you walk us through a past data science project where you faced a significant roadblock? How did you resolve it?

Medium

Approach

Work from the decision backwards to the evidence you would need.
Say what you would check first and why it is the highest-information step.
State your assumptions explicitly before working the problem.

Follow-up

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

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

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.
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.
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.
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.
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.
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)

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.
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.
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.
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.
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?

Fraud losses appear to halve in recent transaction months

Medium · right censoring · development factors · maturity

A weekly chart attributes fct_card_dispute cases to the requested_at month of the linked fct_payment_authorization row. The two most recent months show the first-chargeback rate falling by half, and a risk rule shipped six weeks ago. Columns: dispute_id, auth_id, dispute_category, dispute_stage, opened_at, disputed_amount_minor, liability_shift_flag, outcome, net_loss_minor, resolved_at. Decide whether the rule worked, and produce the version of the chart you would sign your name to.

Approach

Separate the two dates explicitly. opened_at is when a case was filed, requested_at is when the transaction happened. Attributing by transaction month is the right causal choice and is exactly what makes the newest months structurally incomplete.
Measure the filing lag rather than assuming it: the distribution of opened_at minus requested_at over fully developed months, split by dispute_category, and the age at which around 95 percent of cases have arrived.
Build a development triangle of transaction month by months of development on cumulative case counts, and estimate age-to-age factors from the columns that are complete.
Develop the immature months with those factors and plot the result as an estimate with a visible band, kept visually distinct from the matured series rather than blended into it.
State the assumption the method needs: a stable development pattern across cohorts. A change in filing behaviour, merchant mix or the dispute team's own backlog breaks it, so inspect factor stability down each column before relying on the estimate.
Only then evaluate the rule, comparing pre-change and post-change cohorts at equal development age.

Follow-up

What leading indicator would you accept while the cohort matures, and what is its known bias?
How does liability_shift_flag change which disputes you should expect to see in the first place?
If the rule also blocked good transactions, where does that cost appear, and is any of it in this chart?

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.

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.

How do you handle missing data or outliers in a high-stakes financial model?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

How do you define a project scope and technical specifications when working with stakeholders who are not technical?

Medium · behavioural and stakeholder questions

Approach

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

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?