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Colruyt Group Data Scientist Interview Questions & Guide 2026

Data scientist prep for Colruyt Group candidates: drill A/B design, power and minimum detectable effect on original exercises.

The 60-second brief

As a Data Scientist at Colruyt Group, you are at the intersection of retail innovation and data-driven decision-making. You will be responsible for translating complex business challenges into scalable analytical models that directly impact our supply chain, customer experience, and operational efficiency. Your work is not just about building algorithms; it is about providing actionable insights that help one of the most prominent retail organizations in Belgium and beyond maintain its competitive edge.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

Size an experiment before anyone launches it
Define numerator, denominator and window precisely
Turn a vague request into a measurable question

Overview

The role requires a high degree of technical proficiency combined with a pragmatic mindset. Because Colruyt Group operates at a massive scale, you will often find yourself working with diverse datasets that require both creativity and rigor. While you will leverage traditional machine learning techniques, you will also be expected to contribute to the modernization of our analytical infrastructure, making this an ideal role for a data professional who enjoys bridging the gap between legacy systems and forward-thinking data architecture.

The Colruyt Group interview process

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

Initial Screening

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical 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

In-Depth Discussions

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Counting on an identity key that changes underneath the metric

visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.

Dropping rows with missing values without naming the mechanism

Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.

Averaging per-user rates to produce a population rate

Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.

Questions & practice

Explain the logic behind a random forest or gradient boosting model to a non-technical stakeholder.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

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

What are the trade-offs between different regression models when predicting demand?

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
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

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

How do you validate a model to ensure it doesn't overfit on historical retail data?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
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.

Follow-up

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

Cluster bootstrap for a per-session rate randomised on users

Hard · cluster bootstrap · randomisation unit · variance

An experiment randomised on user_id reports a per-session conversion rate, so sessions inside a user are correlated. Input: one row per session with user_id, variant in {control, treatment} and converted in {0,1}. Write a cluster bootstrap from scratch: resample users with replacement within each arm, keep every session of a drawn user, recompute each arm's ratio of converted sessions to sessions, and take the difference. Return the point estimate, a 95 percent percentile interval from at least 2,000 resamples, the naive session-level interval that ignores clustering, and the ratio of their widths.

Approach

Name the estimand precisely: it is a ratio of sums, sum(converted) over sum(sessions) within an arm, not the mean of per-user rates. Those differ whenever session counts vary across users, and the ratio is what the reported metric is.
Resample the cluster, not the row. Draw n_users user ids with replacement inside each arm and take every session belonging to each draw, including duplicate draws of the same user. Keeping the user count fixed per arm rather than the session count is what preserves the sampling design.
Precompute per-user (converted_sum, session_count) once, so each resample is two vector lookups and a division rather than a repeated filter over the session frame. That turns 2,000 resamples from minutes into under a second.
Take the 2.5th and 97.5th percentiles of the 2,000 differences for the interval, and report the point estimate from the full data rather than from the bootstrap mean, since the bootstrap mean carries the resampling bias.
Compute the naive interval from the session-level binomial standard error and compare widths. The expected inflation is roughly sqrt(1 + (m-1)*rho), with m the mean sessions per user and rho the intraclass correlation of converted within users, so a computed ratio far from that value points at a bug in one of the two intervals.

Worked solution (35 min)

per_user = df.groupby(['variant','user_id'])['converted'].agg(['sum','size']); split into two arrays per arm.
point = (t_sum.sum() / t_n.sum()) - (c_sum.sum() / c_n.sum()).
For b in range(B): idx = rng.integers(0, len(t_sum), len(t_sum)); ratio_t = t_sum[idx].sum() / t_n[idx].sum(); same for control; store the difference. Vectorise by drawing a (B, n) index matrix if memory allows.
ci = np.percentile(diffs, [2.5, 97.5]); naive_se = sqrt(p_t*(1-p_t)/n_sessions_t + p_c*(1-p_c)/n_sessions_c); naive_ci = point +/- 1.96*naive_se.
width_ratio = (ci[1]-ci[0]) / (naive_ci[1]-naive_ci[0]).

Expected result: A point estimate identical to the full-data ratio difference, a percentile interval containing that point estimate, and a width ratio greater than 1 whose value approximates sqrt(1 + (m-1)*rho) for the data's mean cluster size m and intraclass correlation rho. A ratio of approximately 1.0 means sessions were resampled instead of users.

Verify it yourself

Construct data where every user has exactly one session: the cluster and naive intervals then agree to within Monte Carlo error, since m = 1 makes the design effect 1.
Construct data where converted is constant within each user: rho is 1, and the width ratio should approach sqrt(m).
The bootstrap distribution's mean sits close to the point estimate; a large offset means the resampled arms have drifted in size.
Doubling B narrows the Monte Carlo noise in the percentile endpoints without shifting the interval's centre.

Follow-up

Users average 3.4 sessions and the intraclass correlation is 0.12. What width ratio do you predict before running it, and does your bootstrap land there?
Give the delta-method standard error for this ratio and say when you would prefer it to the bootstrap.
Half the users in the treatment arm have exactly one session. What does that do to the cluster bootstrap's coverage, and how would you check it?

Why do you want to join Colruyt Group specifically?

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Compute rates by summing numerator and denominator separately, never by averaging rates.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Find reactivation gaps in account paid-period history

Hard · gaps-and-islands · window-functions · subscriptions

fct_subscription_period holds account_id, subscription_id, period_start_utc, period_end_utc, period_status and change_reason. A mid-period plan or seat change closes one row and opens another, so a single continuous paid tenure is often many rows, and an account may hold two overlapping subscriptions. Collapse rows with period_status in ('active','past_due') into continuous tenures per account, treating gaps of three days or less as continuous. Return account_id, tenure_start, tenure_end, and for every tenure after the first, the gap in days that preceded it.

Approach

Filter to paid rows only: period_status IN ('active','past_due'). Trialing periods are not tenure, and including them turns every trial that never converted into a one-period tenure followed by a fake churn.
Order by period_start_utc and take a running maximum of all prior ends: MAX(period_end_utc) OVER (PARTITION BY account_id ORDER BY period_start_utc, period_end_utc, subscription_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING). Those three columns are the only stable ordering this schema exposes, so check first that they are unique within an account; if rows tie on all three, the island numbering is order-dependent between runs and you need a real row key before the result is reproducible.
LAG on its own is wrong here because with overlapping or nested periods the immediately preceding row by start date is not the one that ends latest, so the running maximum is the part that cannot be shortcut.
Flag a new island when prior_max_end IS NULL OR period_start_utc > prior_max_end + interval '3 days', then number islands with a running SUM of the flag over the same ordering and an explicit ROWS frame.
Group to (account_id, island) taking MIN(period_start_utc) and MAX(period_end_utc), then LAG(tenure_end) OVER (PARTITION BY account_id ORDER BY tenure_start) to compute the preceding gap in days for every tenure after the first.
Sanity-check with change_reason, which is the only lineage this schema carries: list its distinct values first, then confirm that rows recording a plan or seat change sit inside a tenure rather than opening one, and that every tenure after the first opens on a row whose reason records a restart rather than an ordinary renewal. Do not reconcile against a churn timestamp on dim_account, which this schema does not define; and where such a column does exist, a cancellation timestamp records when the request was made and routinely sits weeks before the period it ends.

Worked solution (35 min)

Find an account with a known mid-period upgrade and dump its period rows to use as the trace case.
Check that (period_start_utc, period_end_utc, subscription_id) is unique per account, since the whole ordering rests on it.
Write the paid-rows CTE and the running MAX with the explicit frame.
Add the island flag and the running SUM, then verify the trace account yields one island.
Group to tenures and add the LAG-based gap in days.
List the distinct change_reason values, then count accounts with more than one tenure and compare against the count of accounts carrying a restart-flavoured reason anywhere in their history.

Expected result: One row per (account_id, tenure). An account with a continuous history returns a single row with a NULL preceding gap. Every non-first tenure has gap_days strictly greater than 3 by construction.

Verify it yourself

No two tenures for the same account overlap or touch within the grace window.
MIN(tenure_start) per account equals MIN(period_start_utc) per account in the filtered input.
The account with a known mid-period upgrade produces one tenure, not two.
(period_start_utc, period_end_utc, subscription_id) has no duplicates within an account; if it does, say how many accounts are affected before reading the output.
The share of accounts with more than one tenure is small; if it is most accounts, the status filter or the grace window is wrong.

Follow-up

Why three days of grace? What do 0 and 30 days each do to the count of accounts classed as reactivated?
An account runs two concurrent subscriptions for different teams. One tenure or two, and what does the revenue reader expect?
How would you turn these tenures into a monthly gross logo churn series without double-counting an account that churned and returned in the same month?

A test that won on day four and lost by day twelve

Medium · peeking · sequential testing · multiple comparisons · winner's curse

A two-week test was checked every morning. On day four the primary metric crossed p = 0.03 and the team shipped. By day twelve, with logging still running, the estimate had fallen to a 0.2% lift at p = 0.61. The day-four deck also reported a significant 9% lift among paid_search visitors on mobile_web, one of twenty segments inspected. Explain both patterns quantitatively, say what the day-four evidence was actually worth, and specify the monitoring rule that prevents a repeat.

Approach

Price the peeking. A fixed-sample test controls type I error at one pre-declared look. Ten daily looks at a nominal 5% raise the false positive rate to roughly 0.15 to 0.20, because the running test statistic takes a random walk that will eventually cross the boundary under the null.
Price the winner's curse separately, because it is a different error. Conditioning an estimate on having crossed the threshold biases it away from zero, by a factor of roughly 3 when true power is 20% and only about 1.1 at 80% power. Day four sits at roughly 29% of planned information, which is exactly the low-power case.
Read day twelve as the better estimate rather than as a contradiction. Nothing regressed; the day-four value was an extreme draw that was selected precisely for being extreme.
Price the segment claim. Across twenty segments at a nominal 5%, at least one crosses about 64% of the time under a global null, since 1 - 0.95^20 = 0.64, and the slice that surfaces is by construction the one with the most favourable noise.
Specify the rule instead of scolding. Either fix the horizon and look once, or use group-sequential boundaries such as O'Brien-Fleming that spend almost no alpha early, or use always-valid confidence sequences that stay correct under continuous monitoring. Pre-register the small set of segments, correct across the ones tested, and treat anything found afterwards as a hypothesis needing its own powered test.

Worked solution (25 min)

Count the looks actually taken, twelve daily, and state the inflated alpha band of 0.15 to 0.20 for ten looks, rising with more.
Place day four on the information fraction, about 4/14 = 29% of planned sample, and apply the exaggeration factor for low power.
Compute 1 - 0.95^k for the number of segments actually inspected; with k = 20 this is 0.64.
Report the day-twelve interval as the estimate and say plainly that the experiment supports no effect at any size the team would ship for.
Write the rule into the experiment template: horizon and segments declared at launch, sequential boundaries if interim looks are needed at all.

Expected result: The day-four result is consistent with pure noise: ten looks inflate the false positive rate to roughly 0.15 to 0.20, and an estimate conditioned on crossing at about 29% information is exaggerated by roughly a factor of three at low power. The segment finding appears about 64% of the time across twenty slices under a global null. The supportable conclusion is a 0.2% lift whose interval covers zero.

Verify it yourself

1 - 0.95^20 = 0.6415. If your family-wise number lands near 0.05 you have computed the per-test rate instead.
The day-twelve standard error should be roughly sqrt(12/4) = 1.7 times smaller than day four's. If the interval did not narrow, sample was not accumulating and there is a second problem.
A Bonferroni threshold for twenty segments is 0.0025; check whether the mobile_web result would have survived it.

Follow-up

Under O'Brien-Fleming with five planned looks, roughly what nominal p-value would the first look have needed to stop?
The feature already shipped. What analysis can you still run, and what can it not tell you?
If mobile_web paid_search were genuinely the only affected segment, how would you design the confirmatory test and how large would it have to be?

Measure a paid channel without a user-level experiment

Hard · difference-in-differences · geo holdout · incrementality · clustered inference

Finance needs the incremental contribution of paid social before next year's budget. It carries 18% of last-touch-attributed signups in dim_user.last_touch_channel, but the ad platform will not randomise users and unexposed users are unobservable. The ad platform targets at market level, your warehouse resolves a market label onto fct_session, and 40 markets are eligible with 12 months of weekly signups derivable from dim_user. Design the read, size it on total signups rather than attributed ones, and state the identifying assumption and the check that would falsify it.

Approach

Say why the attributed number cannot answer the question. Last-touch assigns full credit to whichever touch was recorded nearest the conversion, which systematically rewards channels that intercept demand that already existed, and the counterfactual of not running the channel is nowhere in the touchpoint data.
Choose a geo holdout: randomise markets into a paused arm and a spend-as-usual arm, and measure total signups per market. The metric has to be the total, because the question is whether demand is created or merely relabelled, and attributed signups fall to zero in a paused market by construction.
Size on markets, since the market is the randomisation unit and therefore the variance unit. Forty markets give at most twenty per arm, so form matched pairs on twelve months of pre-period weekly signups and randomise within pair, which removes the between-market level differences that would otherwise dominate the residual variance.
Fix the identifying assumption in plain words: absent the pause, treated and control markets' signup trends would have moved in parallel. Falsify it by running the same estimator on placebo cut dates inside the pre-period; a non-zero placebo estimate or visibly diverging pre-trends kills the design as specified.
Estimate with market and week fixed effects and cluster standard errors on market. Below about forty clusters the cluster-robust error is biased downward, so use a wild cluster bootstrap or randomisation inference over the pair assignment. If only one or two markets can be paused, switch to synthetic control: weight untreated markets to match the treated market's pre-period path and use in-space placebos for inference.

Follow-up

Campaign commitments mean the pause starts in different weeks in different markets. What does staggered timing do to a two-way fixed effects estimate when effects vary across cohorts?
How long do you pause, given that paid social has a delayed conversion tail, and how do you keep that tail out of the pre-period?
The estimate says the channel is 40% incremental. What happens to the last-touch number on the dashboard after that?

Gross revenue churn doubled with no cancellations behind it

Hard · billing-periods · fx · revenue-churn · decomposition

Gross monthly revenue churn computed from fct_subscription_period doubled from 1.8% to 3.6% in one month. The support queue shows no rise in cancellations and renewals look normal. You have fct_subscription_period with subscription_id, account_id, period_start_utc, period_end_utc, mrr_cents, mrr_cents_constant_fx, seats_billed, period_status, change_reason and canceled_at_utc, plus dim_account. Decompose the 1.8-point rise into named mechanisms, size each in points of the headline, and state the remainder you cannot explain.

Approach

Reconstruct the numerator row by row and group it by change_reason before arguing about causes. A mid-period plan or seat change closes the current period row and opens a new one, so any implementation that treats a closed row as lost revenue books upgrades, downgrades and seat changes as churn; the change_reason breakdown of the numerator makes that visible in one query.
Check the recognition timestamp. Churn belongs at period_end_utc, because revenue continues until the period ends, not at canceled_at_utc when the button was pressed. Then count period_end_utc rows per month across thirteen months: annual cohorts concentrate their period ends in the month twelve months after they were signed, so a spike that repeats in the same month last year is seasonality in the book, not an event this month.
Recompute the whole numerator and denominator on mrr_cents_constant_fx. If the constant-currency figure is materially flatter, the move is an exchange-rate translation and belongs nowhere in a churn narrative.
Check period_status handling. Rows with period_status = 'past_due' are dunning, not cancellation; a status-based rule counts them as loss while a period-end rule does not, and a dunning backlog can move the number by itself.
Express every mechanism in points of the headline, sum them, and print the residual explicitly next to the month-to-month standard deviation of the prior twelve months. A decomposition without a stated remainder is a story, not an accounting.

Follow-up

Which of these mechanisms should be fixed in the metric definition and which should be reported as a genuine business fact?
How would you present a month whose churn is dominated by an annual cohort anniversary without the audience concluding the business is deteriorating?
What would you change so that an upgrade can never enter the churn numerator again, and how would you test that it worked?

Suggested practice checklist

For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.

Day 1 — Build a fixture you can check answers against

Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.

Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.

Day 2 — Joins, filters and NULL semantics

Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.

Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.

Day 3 — Window functions and frames

Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.

Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.

Day 4 — The four analytical query patterns

Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.

Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.

Day 5 — Write SQL the way you will have to write it live

Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.

Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.

Day 6 — One day for everything that is not SQL

Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.

Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.

Day 7 — Full loop rehearsal

Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.

Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.

Explaining a method to someone who will not follow the maths

Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.

Describe a time you had to explain a complex technical finding to a non-technical colleague.

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What did you decide not to do, and why?
How did you know the outcome was caused by your change?

How do you handle situations where you have incomplete data or shifting project requirements?

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

How did you know the outcome was caused by your change?
What did you decide not to do, and why?

Walk through an analysis you got wrong and what changed

Easy · postmortem · data quality · self-assessment

Describe an analysis of yours that turned out to be wrong after somebody had already acted on it. You have four minutes. The account must name the defect mechanically, the join, the filter, the window or the identity key, rather than describing it as a communication problem. It must also say who did what because of the wrong number, how the error surfaced, how long it stood, and what control you put in place so that class of error cannot reach a decision again. Do not pick an error nobody acted on.

Approach

Recognise what is being probed: whether you can be specific about your own failure without minimising it or performing contrition. The discriminator is whether the defect has a mechanism the listener could reproduce in their own warehouse.
Choose the case by blast radius rather than by comfort. An error nobody acted on tests nothing, and picking one signals that you are managing the interview instead of answering it.
Structure the account in six beats: the number, the decision it drove, the defect, the detection, the correction, the control. Keep the defect to one reproducible sentence, for example an inner join to fct_subscription_period that dropped accounts with no subscription row and so computed retention over payers only.
State the direction of the bias, not only its existence. A filter or join that removes rows usually moves a metric predictably, and knowing which way shows you diagnosed the mechanism rather than patched the symptom.
Be exact about detection and elapsed time. 'A colleague noticed' and 'the row-count assertion failed before publication' are different answers about the same organisation, and the second one is the one your control is supposed to produce next time.
End on the control, its cost, whether it has fired since, and one thing it does not cover.

Follow-up

What did the control cost, and has it fired since? If it never has, how do you know it works?
How long did the wrong number stand before anyone questioned it, and what does that say about the review path it went through?
What is the equivalent mistake you are most likely to make in this role, given the tables you would be working in?