Interview guides · H E B

H E B Data Scientist Interview Questions & Guide 2026

Modeling drills for a H E B data scientist interview: baselines, regression interpretation, class imbalance, model defense.

The 60-second brief

As a Data Scientist at H E B, you are joining a pivotal team that drives intelligent decision-making across one of the nation's largest and most innovative privately held retailers. Your role bridges the gap between massive datasets and tangible business outcomes, directly impacting everything from supply chain efficiency to personalized customer experiences. H E B relies on data to maintain its competitive edge, meaning your work will be visible, highly valued, and deployed at incredible scale.

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

Decompose a metric move by segment and mix
Size an experiment before anyone launches it
Define numerator, denominator and window precisely

Overview

In this position, you will tackle complex challenges related to inventory forecasting, pricing optimization, e-commerce growth, and customer behavior modeling. You will work closely with engineering, product, and business operations teams to build robust machine learning models and analytical frameworks. The scale of H E B's operations means that even incremental improvements in your models can translate into massive operational savings and enhanced experiences for millions of Texans.

Candidates can expect a fast-paced, highly collaborative environment that values both technical rigor and practical business sense. You will not just be building models in a vacuum; you will be expected to understand the retail landscape, engineer solutions that scale, and communicate your findings to executive leadership. If you are passionate about applying cutting-edge data science to real-world, high-impact retail problems, this role offers an exceptional platform for growth.

The H E B interview process

H E B candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Phone Screen

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

HireVue 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

Technical Interview

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Panel Interview

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Executive Round

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Where candidates lose points

Watching an experiment daily and stopping when it crosses significance

A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.

Treating last-touch attribution as the causal value of a channel

The attribution label on dim_user is the output of a rule that assigns full credit to whichever touch happened to be recorded last inside a lookback window, and that rule systematically rewards channels that sit close to the conversion, especially branded search and retargeting, which largely intercept demand that already existed. Reallocating spend on those labels moves budget toward the channels that are best at being last, which is why attributed return on ad spend often improves while total signups do not. Nothing in the touchpoint data can settle this, because the counterfactual of not running the channel was never observed. The credible reads are a geo holdout or a scheduled pause, sized in advance on the total-signups metric rather than on the attributed one, and the honest framing in the meantime is that the label describes correlation with conversion and not incremental contribution.

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

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

Walk me through the mathematical difference between L1 and L2 regularization.

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.
Translate the result into the decision it informs, in one plain sentence.

Follow-up

Which assumption here is most likely to be violated in practice?
How would you explain this result to someone who does not know statistics?

Can you explain Bayes' Theorem and provide a real-world example of how you would use it?

Medium · statistics and probability

Approach

Quantify uncertainty explicitly rather than reporting a point estimate alone.
Sanity-check the answer against a simple bound or a simulated case.
Write down the assumption the method needs before you use the method.

Follow-up

Which assumption here is most likely to be violated in practice?
How would you explain this result to someone who does not know statistics?

Describe an emerging machine learning trend that you think H E B should be investing in.

Medium · machine learning and modelling

Approach

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

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

How do you monitor a machine learning model once it is deployed in production?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

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?

Attribute account MRR to first-touch channel without seat fan-out

Medium · fan-out · joins · revenue · attribution

dim_user holds user_id, account_id, first_touch_channel, account_created_at_utc. dim_account holds account_id, lifecycle_status. fct_subscription_period holds account_id, period_start_utc, period_end_utc, period_status, mrr_cents_constant_fx. For a given as-of date, report active MRR by the first-touch channel of the account's earliest-created user. An account has many users and many periods. The channel totals must sum exactly to total active MRR on that date, and accounts whose label is NULL must appear as their own bucket.

Approach

Reduce the label side to one row per account before any join: ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY account_created_at_utc, user_id) = 1 over dim_user, carrying first_touch_channel. Joining dim_user to the period table directly repeats each revenue row once per seat.
Reduce the money side to one row per account too: the period where period_start_utc <= :as_of AND period_end_utc > :as_of AND period_status IN ('active','past_due'). If more than one row survives that filter the account holds overlapping subscriptions, which must be deduped or deliberately summed, and either way the choice is stated rather than left to the join.
Join the two one-row-per-account CTEs and SUM(mrr_cents_constant_fx). Use the constant-FX column, since the same query run in two quarters otherwise reports currency moves as channel performance.
Bucket NULL first_touch_channel as 'unattributed' with COALESCE instead of letting it drop. Roughly the share of users with no observed touch is not small, and dropping it understates the total and inflates every named channel's share.
Reconcile: the SUM over the joined result must equal the SUM over the period CTE alone. That single equality check catches fan-out immediately, which is why it is worth writing before presenting the number.

Worked solution (30 min)

Compute total active MRR on the as-of date from the period table alone and write the number down as the target.
Build the label CTE and assert COUNT(*) = COUNT(DISTINCT account_id).
Build the as-of period CTE and assert the same, investigating any account with more than one surviving row.
Join, COALESCE the channel, aggregate, and compare the total to the target.
Deliberately run the naive dim_user join and record the inflation factor, which is the MRR-weighted mean seat count.

Expected result: One row per first_touch_channel plus an 'unattributed' bucket, whose MRR values sum exactly to the independently computed total active MRR for the as-of date.

Verify it yourself

Channel total equals the standalone total from the period CTE, to the cent.
Both CTEs satisfy COUNT(*) = COUNT(DISTINCT account_id).
The unattributed bucket is present and non-zero rather than silently absent.
The naive join version produces a strictly larger total, and you can state the ratio.

Follow-up

Which user should own the account's label: earliest created, the 'owner' role, or the one who converted? What does each choice systematically favour?
What does this table entitle you to say about the value of a channel, and what does it not?
Some accounts have a parent_account_id. How does rolling those up change both the numerator and the channel mix?

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.

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?

What metrics would you use to evaluate the success of a new personalized coupon campaign?

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?

How do you prioritize your tasks when dealing with multiple urgent requests from different teams?

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.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

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

What do you consider the biggest challenge in scaling data science across a large traditional enterprise?

Medium

Approach

State your assumptions explicitly before working the problem.
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

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

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?

Pooled signup conversion fell while every segment rose

Medium · mix-shift · simpsons-paradox · decomposition

Weekly visit-to-signup conversion, counted on distinct fct_session.visitor_id with is_bot_flagged = TRUE and consent_state = 'denied' sessions excluded, fell from 4.4% to 3.9% week over week. Split by device_type and referrer_channel, all twelve cells are flat or up. A paid_social campaign launched on Monday. Using fct_session and fct_event, quantify how much of the 0.5-point fall is mix and how much is within-segment rate, then state what you would tell the growth lead.

Approach

Write the pooled rate explicitly as the sum over segments of weight times segment rate, and materialise both weeks' weights and rates into one table. Until that table exists there is nothing to decompose, only opinions.
Compute three quantities and report all three: the rate effect holding the prior week's weights fixed, the mix effect holding the prior week's rates fixed, and the interaction residual. Reporting only the first two hides a term that can be material when both weights and rates move a lot.
Rank segments by their individual mix contribution, computed as the change in that segment's weight multiplied by its prior-period rate. This is what lets you say one cell caused the move rather than gesturing at the campaign.
Verify the new traffic is human and countable before accepting the mix story: check is_bot_flagged coverage on the new channel, the distribution of duration_seconds and event_count for its sessions, and whether its consent_state profile differs from the rest.
Deliver the conclusion as a definition change rather than a diagnosis: a pooled rate over a mix that moves is not comparable week over week, so the recurring report should carry per-channel rates plus absolute signups, with the pooled figure demoted or dropped.

Follow-up

Paid social converts at roughly a quarter of organic but absolute signups rose. Is the campaign working, and what would you need to answer that properly?
Would you reach the same conclusion if the campaign had moved the mix by two points instead of sixteen? Where is your threshold and why?
How would you present this to someone who has been watching the pooled number in a weekly meeting for a year?

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

Tell me about a time you handled significant work pressure or a tight deadline.

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.
Close with what you would do differently, concretely.

Follow-up

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

Walk me through a complex data science project on your resume from start to finish.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Explain a wide interval to a non-technical executive

Medium · communication · uncertainty · pricing

A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

Approach

Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.

Follow-up

The executive says to give the midpoint and they will manage the risk. What do you do?
How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?