Interview guides · Deel

Deel Data Scientist Interview Questions & Guide 2026

Practice explaining analysis before a Deel data scientist interview: the recommendation, the uncertainty, the follow-ups.

The 60-second brief

As a Data Scientist at Deel, you play a pivotal role in transforming data into actionable insights that drive strategic decisions within the organization. Your work directly influences the development and refinement of products aimed at simplifying global payroll and compliance challenges for businesses. By leveraging data analytics and modeling techniques, you will contribute to enhancing user experiences and optimizing operational efficiency.

How much statistics you need depends on the flavour of the seat. Experiment-facing work wants you deep enough to notice that repeated looks at accumulating data inflate the false positive rate of a fixed-sample test; modelling-facing work wants estimation and honest uncertainty intervals.

Focus areas

Pick a randomisation unit that respects interference
Separate novelty effects from durable behaviour change
Define numerator, denominator and window precisely

Overview

In this role, you will engage with various teams, including product, engineering, and operations, to tackle complex problems that require both technical expertise and creativity. You will work on large datasets, building models that can predict trends, inform product features, and ultimately enhance the value that Deel provides to its users. This is an exciting opportunity to be at the forefront of data-driven decision-making in a rapidly evolving business landscape, where your contributions can have a significant impact on the company’s success.

The Deel interview process

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

Initial Screening

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

What to demonstrate

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

How to prepare

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

Technical Assessment

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Team Interviews

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

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.

Building features from data that postdates the prediction time

Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.

Reporting a p-value with no effect size or interval

Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.

Questions & practice

Discuss the time complexity of your code for a specific algorithm you wrote.

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
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.

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 implement a decision tree from scratch?

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

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?

Simulate the false positive cost of repeated peeking

Medium · simulation · peeking · type i error

Quantify the cost of peeking. Simulate a two-arm experiment with no true effect: each arm accumulates Bernoulli conversions at a base rate of 0.10 up to 40,000 units per arm. Run a two-sided two-proportion z-test at alpha 0.05 at ten equally spaced interim points, and record whether the test ever crossed. Report the false positive rate over at least 10,000 replications, alongside the rate for a single look at the final sample only. Use a fixed seed and report a Monte Carlo standard error on both figures.

Approach

Generate each replication as two cumulative sums of Bernoulli draws, then read the interim points off the cumulative arrays. Regenerating data at each look would make the looks independent, which destroys exactly the dependence the exercise is about: later looks share data with earlier ones.
Use the pooled-variance two-proportion z: p_pool = (x1+x2)/(n1+n2), z = (p1-p2) / sqrt(p_pool*(1-p_pool)*(1/n1 + 1/n2)), reject when |z| > 1.96. State that the normal approximation is fine here because the smallest look has roughly 400 expected conversions per arm.
Vectorise across replications rather than looping: draw a (reps, n) array of uniforms, threshold at 0.10, cumsum along axis 1 and slice the ten look indices. A per-replication loop at 10,000 by 40,000 is unnecessarily slow.
Record the any-cross indicator per replication, take the mean, and compute the Monte Carlo standard error as sqrt(p*(1-p)/reps) so the reported figure comes with its own precision.
Report the single-look rate in the same run as a control. If it does not land near 0.05, the bug is in the test statistic and not in the peeking argument.

Worked solution (30 min)

rng = np.random.default_rng(seed); for memory, batch the replications in chunks and accumulate the any-cross count across chunks.
Per chunk: draw (chunk, 40000) uniforms per arm, x = (u < 0.10).cumsum(axis=1), slice columns at indices 3999, 7999, ..., 39999.
Compute the ten z statistics vectorised over the chunk, take crossed = (np.abs(z) > 1.96).any(axis=1).
Aggregate: peek_rate = total_crossed / reps; single_rate = mean of |z_final| > 1.96; mc_se = sqrt(p*(1-p)/reps) for each.

Expected result: The single-look rate lands at 0.05 within about 0.005 at 10,000 replications. The ten-look rate lands near 0.19, which with 10,000 replications has a Monte Carlo standard error of about 0.004, so anything in roughly 0.18 to 0.20 is consistent and anything near 0.40 indicates independent redraws.

Verify it yourself

The single-look control recovers 0.05 within two Monte Carlo standard errors; if it does not, the z statistic is wrong and the headline number is meaningless.
The crossing rate is monotone in the number of looks: 1 look near 0.05, 2 near 0.08, 5 near 0.14, 10 near 0.19.
Halving the base rate to 0.05 leaves the peeking rate essentially unchanged, since the result is about the test's random walk and not about the conversion level.
Re-running with the same seed reproduces the figure exactly.

Follow-up

Re-run with 40 looks instead of 10. Why does the curve flatten rather than continue rising linearly?
Among the replications that crossed, what is the mean observed lift, and why is it not zero?
What does an O'Brien-Fleming boundary or an always-valid confidence sequence change about this simulation, and what does each cost in power?

Write a function to find the intersection of two arrays.

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.
State the window function and its partition and ordering out loud before writing it.

Follow-up

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

Rebuild sessions from raw events with a thirty-minute gap

Medium · window-functions · sessionisation · gaps-and-islands

From fct_event (event_id, visitor_id, occurred_at_utc) alone, rebuild sessions: a new session begins when the gap from that visitor's previous event exceeds 30 minutes, and every session is force-closed at UTC midnight so none spans two calendar dates. Return one row per session with visitor_id, session_start, session_end, event_count and session_date. Do not read fct_session; the point is to reproduce it. Assume duplicate occurred_at_utc values exist for the same visitor.

Approach

Get the previous timestamp per visitor with LAG(occurred_at_utc) OVER (PARTITION BY visitor_id ORDER BY occurred_at_utc, event_id). The event_id tiebreaker is required, not stylistic: with duplicate timestamps an unstable ordering makes the boundary flags non-deterministic between runs.
Set a boundary flag when prev IS NULL, or occurred_at_utc - prev > interval '30 minutes', or occurred_at_utc::date <> prev::date. The third disjunct is the midnight rule, expressed as a date change rather than a clock comparison so it holds across any gap length.
Number the islands with SUM(flag::int) OVER (PARTITION BY visitor_id ORDER BY occurred_at_utc, event_id ROWS UNBOUNDED PRECEDING). Because event_id is unique the ordering is total, so no two rows are peers and RANGE UNBOUNDED PRECEDING would compute exactly the same numbers here. Write ROWS anyway: it is the half of the guard that survives someone later simplifying the ORDER BY back to occurred_at_utc alone, at which point the default RANGE frame gives every row sharing a timestamp one shared running total.
GROUP BY visitor_id and the island number, then MIN(occurred_at_utc) AS session_start, MAX(...) AS session_end, COUNT(*) AS event_count, session_start::date AS session_date.
Reconcile against fct_session on one sample day. The counts should agree except for server-emitted events carrying no client session, so a systematic difference beyond those is a bug in the gap rule or in the ordering.

Worked solution (30 min)

Pick one high-volume visitor and dump their ordered event timestamps for a day to trace by hand.
Add LAG with the tiebreaker and eyeball the computed gaps.
Add the three-part boundary flag and confirm the first event of each date is flagged.
Add the running SUM with an explicit ROWS frame, then group and aggregate.
Compare total event_count against the raw input count, and compare session counts to fct_session for the sample day.

Expected result: One row per (visitor_id, island). SUM(event_count) equals the input row count exactly, with no events lost or duplicated. No session_end falls on a different date from its session_start.

Verify it yourself

Total events in equals total events out.
MAX(session_end::date - session_start::date) is 0 across all rows.
For a visitor ordered 09:00, 09:10, 09:55, 23:58, 00:02, the result is four sessions, not three.
No two sessions for the same visitor have overlapping [session_start, session_end] ranges.
Swapping ROWS for RANGE while event_id stays in the ORDER BY changes nothing; dropping event_id from both ORDER BY clauses does change the boundaries for a visitor with duplicate timestamps, which locates the guard in the tiebreaker rather than in the frame keyword.

Follow-up

Why 30 minutes? What does a 5-minute rule do to sessions-per-visitor and to any per-session conversion rate?
The same person uses phone then laptop. Two visitor_ids, two sessions. What breaks if you sessionise on user_id instead?
The midnight rule splits an overnight session. Which metrics does that bias, and in which direction?

How would you approach building a recommendation system for a new product line?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you prioritize tasks when working on multiple projects?

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

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

A company sees a sudden drop in user engagement. How would you investigate this issue?

Medium · product sense and metrics

Approach

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

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

How would you handle missing data in a dataset?

Medium

Approach

Say what you would check first and why it is the highest-information step.
Clarify what is being asked and what a complete answer would contain.
Work from the decision backwards to the evidence you would need.

Follow-up

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

Measure whether self-serve help actually answered the question

Medium · proxy metrics · bias · support

A help widget opens in-product. Leadership asks for the percentage of users who got their answer. You have fct_event (event_name, user_id, session_id, occurred_at_utc, is_core_action, properties JSONB carrying article_id), fct_session, and a support ticket table joined on user_id. Nothing records whether the answer was correct or whether the user was satisfied, and no such field is being added. Propose the metric you would publish, state the proxy plainly, name the direction and rough size of its bias, and say which decisions it can and cannot support. Deliverable: the definition plus the bias statement.

Approach

Say first that the target quantity is unobserved and will stay unobserved: nothing in the stream distinguishes a user who was helped from one who gave up, and both leave the same trace, so every candidate metric here is a proxy and the only question is which bias you prefer.
Build the proxy to remove the largest identifiable error: among widget-open sessions, the share with no ticket from that user within 72 hours and at least one is_core_action = TRUE event after the widget opened in the same session. The downstream action requirement strips out most of the silent abandonment that a no-ticket rule alone scores as success.
State the residual bias with a direction and a bound: it still over-counts, because a user who gave up and went elsewhere files no ticket and may still complete an unrelated core action later in the session; bound it using the observable population of widget-open sessions that end within two minutes with no further event.
Publish a directly-measured companion with its own weakness in the same sentence: article thumbs up/down reported with its response rate, and the note that a single-digit response rate missing non-randomly toward annoyed users is exactly why it cannot be the headline.
Write the use statement, because a proxy without one gets reused for the wrong decision: valid for ranking articles against each other and for detecting a week-on-week break, invalid as an absolute deflection rate or as an input to a cost-saved figure.

Worked solution (25 min)

Write the unobservable target in one sentence with the observable set beside it, so the gap is visible on the page rather than assumed away.
Define the published proxy with exact numerator and denominator, including the session scoping of the downstream core action.
Bound the bias from data you already have: compute the share of widget-open sessions ending within two minutes with no further event.
Publish the thumbs signal beside it with its response rate and the direction of its missingness.
Write the two-line use statement covering what the number supports and what it does not.

Expected result: A named proxy with numerator and denominator, a stated bias direction (over-counts success) with an observed magnitude bound, and an explicit list of the decisions the number does and does not support.

Verify it yourself

The proxy and the raw no-ticket rate should differ by roughly the abandonment share computed in step three. If they are nearly identical, the downstream-action requirement is not binding and adds nothing.
A change in support hours or ticket-form availability moves the no-ticket rate with no change in help quality, so the series must carry annotations for support operational changes.
Sessions with consent_state = 'denied' cannot be joined forward to a user_id and therefore cannot enter this metric at all; the denominator definition has to say so.

Follow-up

How would you validate this proxy once, and what would you do if the validation said it over-counts by 20 points?
Support asks for a dollars-saved number from this metric. What do you say, and what would you need before saying anything else?
Ticket volume drops the week support hours change. How do you keep that out of the series?

Size every candidate cause of a trial-to-paid decline

Hard · cohort-maturity · experiment-readout · mix-shift · contribution-sizing

Trial-to-paid conversion on weekly trial-start cohorts from fct_subscription_period reads 3.1 points below the trailing eight-week mean for the three most recent cohorts. Three things happened in that window: a pricing experiment reached 50% of new trials, a payment processor migration added settlement delay, and paid_search spend tripled. Using fct_subscription_period, fct_experiment_exposure and dim_user, rank the causes by their contribution in points of the headline, state the remainder, and give the decision you would take on Monday.

Approach

Kill the immature cohorts first, because everything downstream is computed on them. The metric is lagged by the trial length plus a 14-day conversion window plus a settlement allowance, and a processor migration lengthens exactly that last term; recompute each cohort at a fixed cohort age rather than as of today, and confirm the newest cohort's value is still climbing day over day.
Hold the experiment analysis to the exposed population. Join fct_experiment_exposure on unit_id with is_in_analysis_population = TRUE rather than reading an assignment log, then check the variant split for a sample-ratio mismatch before believing any effect at all. Contribution to the headline is the variant effect multiplied by the exposed share, which is not the same number as the variant effect.
Decompose the cohort mix by dim_user.first_touch_channel using the same weight-times-rate arithmetic as any other mix question, so the paid_search increase is sized as a weight change at a measured conversion rate rather than asserted from the spend figure.
Convert all three to points of the headline, sum them, and print the residual against the historical week-to-week standard deviation of the metric. If the residual is inside that band, say so and stop looking; if it is outside, name what you would investigate next rather than leaving it implied.
Land the decision. Only one of the three is actionable on Monday, so state whether the experiment has accrued enough exposed units to stop at the pre-declared horizon, and state separately what the settlement-lag correction does to the published series and its lag rule.

Follow-up

How do you choose the fixed cohort age, and what do you lose by choosing it too long?
If the pricing variant is genuinely 1.2 points worse, does that settle whether to stop it? What else is on the other side of that decision?
The trailing eight-week mean spans the processor migration. What is the right baseline instead?

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.

The project you argued against

Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.

Can you give an example of how you influenced a decision in your previous role?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.
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?
How did you know the outcome was caused by your change?

Describe a time when you had to make a data-driven decision without all the necessary data.

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Pick a story where you drove the decision, not one where you observed it.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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

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?