Interview guides · Artefact

Artefact Data Scientist Interview Questions & Guide 2026

Artefact data scientist prep on data pitfalls: selection bias, target leakage, and checking an outlier's provenance before you drop it.

The 60-second brief

This guide covers what a Data Scientist at Artefact is expected to do and how to prepare for the interview.

Product-sense cases reward reasoning from a mechanism to a testable prediction. Reciting every metric you can name reads as pattern matching; naming the single quantity that would move if your explanation were true reads as thinking.

Focus areas

Size an experiment before anyone launches it
Separate novelty effects from durable behaviour change
Define numerator, denominator and window precisely

The Artefact interview process

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

Recruiter Screen

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
Whether your reason for wanting the role points at the work itself rather than the company's reputation
Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Technical Screen

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

Business Case Study

A case round is decided by whether you leave the interviewer with a recommendation, not by how much analysis you narrate on the way there. The prompt is open on purpose, so the first job is to convert it into a decision someone could act on: ask what would be done differently depending on the answer. From there name the quantity that would settle it, state the assumptions you need, and commit. Candidates who cover more ground than anyone expected and still end on "it depends" score below candidates who scoped narrowly and said what they would do.

What to demonstrate

Whether the version of the question you choose to answer is genuinely narrower than the prompt and still worth answering
Whether the recommendation arrives as an action with a number attached, rather than as a summary of what you looked at
Whether assumptions are stated at the moment you rely on them, instead of collected into a disclaimer at the end
Whether you notice when a branch you are exploring would not change the decision either way

How to prepare

Take six open prompts and write only the scoping move for each: the one-sentence question you would actually answer and the decision it feeds. Give yourself three minutes per prompt and stop there.
Put a five-minute warning into every practice case and force a closing statement that names the action, the result that would justify it, and the result that would reverse it.
Record one case and count how long you talked before naming a measurable quantity. Past roughly five minutes, what you are calling scoping is narration.

Cultural Fit Rounds

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.

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

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.

Naming a model class before naming the deployment constraints

Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.

Questions & practice

Explain the underlying mechanics of causal inference and how you would measure the true incremental lift of an ad campaign.

Medium · statistics and probability

Approach

Sanity-check the answer against a simple bound or a simulated case.
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?
Which assumption here is most likely to be violated in practice?

Walk me through how you would build, train, evaluate, and monitor a fraud detection model from scratch.

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

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?

Rebuild per-visitor ordering without groupby convenience methods

Easy · pandas · vectorisation · window logic

You have a DataFrame of 2 million fct_event rows with visitor_id, occurred_at_utc and event_id, unsorted and containing duplicate timestamps within a visitor. Produce three new columns: event_rank, the 1-based position of the event within its visitor ordered by occurred_at_utc; seconds_since_prev, the gap to that visitor's previous event, NULL for the first; and is_first_for_visitor. You may use sort_values, shift, cumsum, numpy and boolean masking. You may not use groupby.transform, groupby.apply, groupby.cumcount, groupby.rank or merge_asof. Break timestamp ties on event_id.

Approach

Sort once by ['visitor_id', 'occurred_at_utc', 'event_id'] and reset the index. The whole exercise reduces to row arithmetic on a sorted frame, and the tiebreak on event_id is what makes the result reproducible across runs.
Mark visitor boundaries with is_first = df['visitor_id'].ne(df['visitor_id'].shift()). This is the single fact every other column derives from.
Compute seconds_since_prev as the diff of the timestamp column, then overwrite it with NaT/NaN wherever is_first is True. The shift crosses the boundary between visitors and will otherwise hand the first row of each visitor the last event of the previous one.
Build event_rank from a running counter that resets at boundaries: take a global cumulative position (np.arange(len(df))) and subtract, per row, the global position at which that visitor started. Get the start position by forward-filling the positions where is_first is True, which is a cumsum-free reset and is O(n).
Verify against the forbidden method once, as a test rather than as the implementation, and confirm the two agree on every row.

Worked solution (20 min)

Sort on the three-key tuple and reset_index(drop=True).
Compute is_first via .ne(.shift()), which is True for row 0 because the shifted value is NaN.
pos = np.arange(len(df)); start = pd.Series(np.where(is_first, pos, np.nan)).ffill(); event_rank = (pos - start + 1).astype(int).
gap = df['occurred_at_utc'].diff().dt.total_seconds(); gap[is_first] = np.nan.
Assert event_rank equals df.groupby('visitor_id').cumcount() + 1 on the sorted frame.

Expected result: Three columns on the sorted frame: event_rank starting at 1 for every visitor and increasing by 1 with no gaps, seconds_since_prev null exactly where is_first_for_visitor is True, and is_first_for_visitor summing to df['visitor_id'].nunique().

Verify it yourself

is_first_for_visitor.sum() equals the number of distinct visitor_id values.
seconds_since_prev.isna().sum() equals that same number.
event_rank.max() per visitor equals that visitor's row count, and event_rank is non-negative everywhere.
A visitor with two events at the identical timestamp gets ranks 1 and 2 with a gap of 0.0, ordered by event_id.

Follow-up

The frame does not fit in memory. How does your approach change if you can only process one visitor-partitioned chunk at a time?
occurred_at_utc is client-supplied and sometimes runs backwards within a visitor. Does your seconds_since_prev go negative, and should it?
How would you extend this to reset the counter at every change of surface as well as visitor?

Write a query using SQL window functions to calculate rolling 7-day active user averages across multi-channel marketing campaigns.

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.

Follow-up

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

How do you select between parametric and non-parametric forecasting models when dealing with noisy time series data?

Medium · sql and data manipulation

Approach

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

Follow-up

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

Paying accounts with no active seat in 28 days

Medium · anti-join · null-semantics · retention-risk

dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.

Approach

Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.

Worked solution (25 min)

Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
Write the active-account spine and the 28-day activity CTE.
Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.

Expected result: One row per silent active account with seats_licensed and days_since_last_core_action, ordered by seats_licensed descending. Accounts that never emitted a core action show NULL and are reported as a separate group rather than as the worst churn risks.

Verify it yourself

Silent accounts plus accounts with at least one active user equals the total count of active non-internal accounts.
The NOT IN variant returns zero rows, which is the demonstration that a NULL is present.
No account_id appears twice in the output.
Every days_since_last_core_action is either NULL or greater than 28.

Follow-up

How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?

How would you approach an assortment optimization problem for a high-volume call center handling mixed expert and non-expert queries?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.

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?

A major retail client is experiencing a sudden drop in call center conversion rates. How would you investigate this metric drop and structure a solution?

Medium · product sense and metrics

Approach

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

Follow-up

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

Imagine a client wants to launch a data-driven customer retention campaign using digital marketing channels. What features and metrics would you prioritize?

Medium · product sense and metrics

Approach

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

Follow-up

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

How would you design a recommendation system for a surfing website to increase user engagement?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

How do you determine statistical significance when dealing with high-variance user conversion data?

Medium · a/b testing and experimentation

Approach

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

Follow-up

What would you do if you could not randomise at all?
How would you handle interference between treated and control units?

How would you design an A/B test for a new digital marketing algorithm, and what experimentation pitfalls would you watch out for?

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
Decide the analysis before seeing data, including how long it runs and when you look.
Say whether units interfere with each other, and switch design if they do.

Follow-up

What would you do if you could not randomise at all?
How would you handle interference between treated and control units?

Define success for halving the free trial length

Hard · monetisation · cohort horizon · trials

A proposal cuts the free trial from 30 days to 14. Trial-to-paid conversion is defined on fct_subscription_period: numerator, subscription_id whose first row with is_first_paid_period = TRUE has period_status IN ('active','past_due') and period_start_utc no later than 14 days after trial end; denominator, subscription_id whose first row has period_status = 'trialing' and period_start_utc in the cohort week. dim_account (account_id, account_created_at_utc, signup_surface) is also available and joins to fct_subscription_period on account_id. Explain why conversion rate alone cannot decide this, define the metric that can, and state the horizon and lag your readout needs. Deliverable: the decision metric and the readout schedule.

Approach

Show the incomparability numerically before arguing about it: at a fixed calendar readout date, a larger share of the 14-day arm's cohorts have completed trial end plus 14 days plus settlement, so the short arm leads mechanically and the lead shrinks as both arms mature.
Fix the censoring first, and do not mistake a rescaling for a fix. The given conversion rate already has trial starts as its denominator, so 'paid accounts per 1,000 trial starts' is that same ratio multiplied by 1,000 and answers nothing new. What the censoring needs is a fixed cohort age: read both arms at account_created_at_utc plus 60 days, which clears the long arm's full 30 + 14 + 3 = 47-day path to a settled first payment, so neither arm is censored at readout.
Then move the denominator upstream, which is a separate fix and the only one that catches a higher share of a smaller population: paid accounts per 1,000 dim_account rows created in the cohort week. Precondition — this diverges from the conversion rate only when trial length is visible before the trial starts, on pricing pages, in ads or in the signup flow. If the arms are assigned at trial start and nothing upstream differs, trial starts per 1,000 new accounts is equal across arms by construction and the two metrics are the same comparison rescaled. Report that ratio per arm and say which regime you are in rather than assuming one.
Add the leg neither denominator can see: a shorter trial converts users who had less time to reach activation, so read month-3 gross revenue churn and week-4 retention of the converted cohort, and read activation at trial day 7 to see whether the converted population changed composition.
Audit 'past_due' before comparing, since the definition counts it as converted: report the rate with and without it per arm, because a gain that sits entirely inside past_due is a billing artefact rather than a conversion effect.
Be explicit that the twelve-month consequence is not observable inside a planning cycle: pre-register that the decision is made on the 60-day metric, that the twelve-month cohort read will be published as a check, and what action follows if the two disagree.

Worked solution (35 min)

Tabulate, at a fixed calendar readout date, the share of each arm's cohorts that have completed trial end plus 14 days plus 3 days of settlement, and show the gap.
Define the decision metric as paid accounts per 1,000 dim_account rows created in the cohort week, evaluated at account_created_at_utc plus 60 days for both arms, and write the 30 + 14 + 3 = 47 arithmetic that justifies 60 as the common clock.
Report trial starts per 1,000 new accounts per arm beside it, so the reader can see whether the upstream denominator is doing any work in this test or whether it is arithmetically pinned to the conversion rate.
Compute the earliest honest readout date from the enrolment window: 60 days after the last account creation in the window plus a 3-day settlement lag, with no partial-cohort comparison permitted before it.
Add the quality leg: month-3 gross revenue churn, week-4 retention of converted accounts, and day-7 activation per arm.
Report the conversion rate with and without past_due per arm, and state which version the decision uses.

Expected result: A decision metric stated as paid accounts per 1,000 new accounts at a fixed 60-day cohort age measured from account creation, the trial-start rate published per arm so it is explicit whether that denominator change carries any information here, a readout date computed from the enrolment window, and at least two quality metrics capable of overturning a conversion-rate win.

Verify it yourself

Both arms must have identical elapsed time at readout: compute min and max of (readout_date minus account_created_at_utc) per arm and confirm the two ranges match.
Trial starts per 1,000 new accounts must be reported per arm. If that ratio is equal across arms, the conversion rate and the per-new-account metric are the same comparison rescaled and cannot diverge — so never present them as two independent confirmations. Only a gap in that ratio gives the per-new-account metric information the conversion rate does not carry.
The past_due share of the numerator should be similar across arms; a gap there is a payment-failure effect and does not belong in a conversion claim.

Follow-up

What happens to a user who would have converted on day 20, and how would you detect that population in the data?
The short arm has lower day-7 activation but higher conversion. Reconcile those two facts into one story.
What randomisation unit do you use here, and what goes wrong with the obvious alternatives?

A conversion rate that fell in one regulatory region

Medium · consent · identity-keys · denominator · geography

Visit-to-signup conversion fell 1.3 points over six weeks. Signups cut by dim_user.country_code put the fall in one regulatory region where a consent banner shipped in week one, but absolute signups from that region are flat. fct_session carries consent_state, visitor_id, is_bot_flagged and session_date and no country column, so the denominator cannot be cut the same way. Using fct_session and fct_event, decide whether behaviour changed or the denominator did, state what these tables cannot settle, and name the one column that would settle it.

Approach

Name the asymmetry before computing anything. The numerator is user-keyed and therefore cuttable by country; the denominator is visitor-keyed and is not. Dividing a region-filtered numerator by an unfiltered denominator produces a quantity that is not a rate, and presenting it as a regional conversion rate is the first mistake available here.
Attack the denominator on the dimension you do have. Compute distinct visitor_id per week and sessions per distinct visitor_id per week: a consent banner that blocks or shortens the identity cookie raises the distinct-visitor count and lowers sessions per visitor, which depresses any visitor-keyed rate with no behaviour behind it.
Split on consent_state. Sessions with consent_state = 'denied' can enter the denominator but can never be joined forward to a signup, so a rising denied share mechanically drives the pooled rate down by roughly its own share. Report the granted-only rate and the denied share as two separate numbers rather than one blended figure.
Cross-check with measures that do not depend on the visitor key at all: absolute weekly signups, which are given as flat, and signups per session rather than per visitor.
State the limit honestly. Without country on the session or on its entry event, the regional attribution rests on the numerator alone, and the correct request is that one column, not a more elaborate model on top of the data you have.

Follow-up

If granted-only conversion is the metric going forward, what selection bias have you accepted, and in which direction does it point?
How would you handle the six weeks of already-published history once the new definition is adopted?
What is the smallest instrumentation change that restores a cuttable denominator without collecting more personal data than before?

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.

What the behavioural round is actually scoring

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

Describe a professional conflict you faced within a cross-functional project team and how you resolved it.

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.
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 would you do differently if you ran that project again?

Quantify your own impact without claiming the topline you touched

Hard · self-assessment · attribution · communication

You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.

Approach

Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.

Follow-up

Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
If you could only keep two of your three claims, which do you drop, and why that one?

Choose between three teams' requests with one analyst-week

Medium · prioritisation · stakeholder · judgement

You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.

Approach

Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.

Follow-up

The dashboard team escalates to your manager. What do you say in that conversation?
Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?