Interview guides · EY-Parthenon

EY-Parthenon Data Scientist Interview Questions & Guide 2026

Data scientist prep for EY-Parthenon: practice defining a product and business analytics metric, picking its guardrails and defending the tradeoff.

The 60-second brief

A Data Scientist at EY-Parthenon operates at the critical intersection of advanced analytics and high-stakes business strategy. Unlike data roles in pure-play technology companies, your work here directly informs the strategic decisions of C-suite executives and global organizations. You are not just building models; you are building the analytical foundation that drives growth platforms and execution strategies for complex, large-scale client engagements.

Learn the economics of the product category before the loop. Marketplaces, subscription products and ad-supported products turn on different core quantities (match rate and liquidity, retention and churn, fill rate and yield) and fail in different characteristic ways.

Focus areas

Decompose a metric move by segment and mix
Turn a vague request into a measurable question
Pick a randomisation unit that respects interference

Overview

In this role, you will bridge the gap between technical complexity and business value. You will be expected to translate ambiguous, high-level strategic problems into structured, data-driven solutions. Whether you are optimizing growth trajectories or identifying operational efficiencies, your insights will be instrumental in shaping the future direction of the clients you serve. It is a position of significant influence that requires both technical rigor and the ability to communicate impact to non-technical stakeholders.

Because EY-Parthenon is a strategy-led firm, your ability to articulate the "why" behind your data models is often just as important as the code you write. Always connect your technical output to a tangible business outcome.

The EY-Parthenon interview process

EY-Parthenon candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Technical Rounds

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

Final Managerial Round

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Comparing cohort retention curves of different maturities, or building the curve from users who are still present

A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.

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.

Sizing estimates built on unnamed, unrevisable assumptions

Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.

Accepting a metric definition without asking about the denominator

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

Questions & practice

How do you ensure your model remains robust when deployed in a production environment?

Medium · machine learning and modelling

Approach

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

Follow-up

Where could label leakage enter this setup?
How would you choose the decision threshold, and who owns that choice?

Explain the trade-offs between different machine learning algorithms for a specific classification problem.

Medium · machine learning and modelling

Approach

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

Follow-up

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

Audit a one-day event extract for structural defects

Easy · data quality · late arrival · pandas

You receive a one-day extract of fct_event as a DataFrame with event_id, occurred_at_utc, received_at_utc, visitor_id, user_id, account_id, event_name, is_bot_flagged and surface. Write a function returning one row per data-quality rule with the rule name, the failing row count and the failing share of the extract. Cover at minimum: duplicate event_id, received_at_utc earlier than occurred_at_utc, occurred_at_utc later than the extract's maximum received_at_utc, account_id present while user_id is NULL, and rows whose occurred_at date differs from their received_at date. Do not drop rows; report only.

Approach

Compute the extract's own reference clock first: max(received_at_utc). Wall-clock now() is wrong here because the extract may be replayed days later, which would turn every row into a future-dated failure.
Express each rule as a boolean Series over the same index so the checks compose, then aggregate with .sum() and divide by len(df). Building a list of (name, mask) pairs keeps the rule set extensible and keeps one code path for counting.
For the duplicate rule, decide and state the convention: df.duplicated('event_id', keep=False).sum() counts every member of a duplicated group, df.duplicated('event_id').sum() counts only the surplus copies. Either is defensible; an unstated choice is not. The rest of this item assumes keep=False.
Treat received_at < occurred_at as clock skew, not corruption: occurred_at is client-supplied. Separate it from the date-mismatch rule, which is the one that actually breaks a daily metric keyed on occurred_at.
Know which rules imply which before you read the counts. A row whose occurred_at exceeds max(received_at_utc) has its own received_at no later than that maximum, so it is necessarily a clock-skew row as well: the future-dated mask is a subset of the skew mask, always. Neither is a subset of the date-mismatch mask, because skew of a few minutes inside one UTC date mismatches nothing.
Return a tidy DataFrame sorted by failing_share descending, and add a boolean column saying whether the rule should block publication, so the output is a decision rather than a list of numbers.

Worked solution (20 min)

Parse both timestamp columns with utc=True and assert the dtype, since a silently-object column makes every comparison string-wise and wrong.
Set ref = df['received_at_utc'].max() and build the five masks against it.
Assemble results as pd.DataFrame(rows) with columns rule, failing_rows, failing_share, blocks_publication.
Keep the masks addressable (a dict of name to Series) rather than only their sums, so the overlap between rules can be asserted rather than assumed.
Verify the function is pure: assert the input frame's shape is unchanged after the call.

Expected result: A DataFrame with one row per rule, failing_share equal to failing_rows divided by len(df) for every row, and the input frame returned unmodified. The rules overlap rather than partition the extract, and one containment is structural: every future-dated row is also a clock-skew row. The counts therefore must not be summed or presented as a total.

Verify it yourself

Plant violations deliberately and assert an exact expected vector rather than assuming one hit per rule. On a 10-row frame carrying two rows that share an event_id, one same-date skew row, one row whose occurred_at sits an hour past max(received_at_utc), one row with account_id set and user_id NULL, and one row whose occurred_at falls on the previous UTC date: duplicate_event_id is 2 under the keep=False convention, clock_skew is 2 (the planted skew row plus the future-dated row, which cannot avoid tripping it), future_dated is 1, orphan_account is 1, date_mismatch is 1.
Assert the containment directly rather than by counting: (future_dated_mask & ~clock_skew_mask).sum() is 0. A non-zero value means the reference clock was not max(received_at_utc).
Feed a clean frame and confirm all counts are 0 and no rule errors on empty masks.
Confirm len(input) is identical before and after, since the deliverable is a report and not a cleaned frame.

Follow-up

The date-mismatch count is 2.1 percent on this extract. What late-arrival rule would you write for a daily metric, and how many days would you hold the number open?
Duplicate event_id values appear only on the 'core_action_completed' event. What upstream cause would you check before deduplicating?
Which of these rules should fire an alert at the pipeline, and which should only appear in a weekly review?

Pair flow starts to completions within a thirty-minute bound

Medium · event-streams · conditional-aggregation · flow-funnel

fct_event holds event_id, flow_id, flow_instance_id, event_name, occurred_at_utc, surface, app_version. Using only rows where event_name is 'flow_started', 'flow_completed' or 'error_shown', compute the daily core-flow completion rate by surface and app_version. Numerator: distinct flow_instance_id whose flow_completed occurs within 30 minutes of its flow_started with no error_shown for the same instance in between. Denominator: distinct flow_instance_id with a flow_started that day. Report rows with a NULL flow_instance_id as a separate coverage count, not inside either side.

Approach

Collapse the stream to one row per flow_instance_id using conditional aggregation: MIN(occurred_at_utc) FILTER (WHERE event_name = 'flow_started') AS started, MIN(...) FILTER (WHERE event_name = 'flow_completed') AS completed, MIN(...) FILTER (WHERE event_name = 'error_shown') AS first_error. This makes the 'first' semantics explicit and avoids a self-join entirely.
Apply the predicates on that single row: completed IS NOT NULL AND completed <= started + interval '30 minutes' AND (first_error IS NULL OR first_error > completed). Note the last clause encodes 'in between' literally, so an error surfaced after a successful completion does not disqualify the attempt.
Bucket by started::date, surface and app_version. Splitting by app_version is not decoration: a completion-rate regression is nearly always confined to one client build, and a pooled daily rate hides it behind the installed base.
Count NULL flow_instance_id rows as a separate coverage figure. They cannot be attributed to an attempt at all, so they belong in neither numerator nor denominator, and their share tells you how much of the rate is unmeasurable.
For any roll-up to week or to all surfaces, re-sum the numerator and denominator rather than averaging the daily rates.

Follow-up

occurred_at_utc is client-supplied. What do you do with an instance whose completion timestamp precedes its start?
flow_instance_id is missing on 4 percent of starts for one app_version. What can and cannot you conclude about that build?
An instance starts at 23:55 and completes at 00:04. Which day owns it, and what does the alternative do to the daily series?

Weekly visit-to-signup conversion split by acquisition channel

Easy · funnel · conversion · aggregation

From fct_session (session_id, visitor_id, started_at_utc, referrer_channel, is_bot_flagged, consent_state) and fct_event (visitor_id, occurred_at_utc, event_name), compute visit-to-signup conversion for one ISO week, split by channel. session_id is the unique key of fct_session. Denominator: distinct visitor_id with a session starting in the week, is_bot_flagged = FALSE and consent_state <> 'denied'. Numerator: those visitors with a 'signup_completed' event in the same week. Label each visitor with the referrer_channel of their first session in the window. Return channel, visitors, signups and rate, plus one all-channel total row.

Approach

Build a visitor spine that is one row per visitor: filter sessions to the week, drop is_bot_flagged and consent_state = 'denied', then take the first session per visitor with ROW_NUMBER() OVER (PARTITION BY visitor_id ORDER BY started_at_utc, session_id) = 1 to carry the channel label. Collapsing to one row here is what makes the channel buckets mutually exclusive and the totals additive.
session_id is the unique key, so that ordering is total and the label is reproducible. If the table carried no unique key you would have to write an explicit tie rule instead, because two sessions on different channels at the identical timestamp would otherwise label the visitor differently between runs.
Attach the outcome as a semi-join (EXISTS on a signup_completed event for that visitor inside the same week) rather than a join to the event table, so a visitor who fires the event twice does not count twice and inflate the numerator past the denominator.
Aggregate with COUNT() as visitors and COUNT() FILTER (WHERE signed_up) as signups, and compute the rate as signups::numeric / NULLIF(visitors, 0) so an empty channel returns NULL rather than a division error.
Produce the total with GROUP BY GROUPING SETS ((channel), ()), which re-sums numerator and denominator for the total row. Averaging the channel rates gives a different and wrong number whenever channel volumes differ, which they always do.
Verify the spine before trusting the output: COUNT(*) must equal COUNT(DISTINCT visitor_id), and the per-channel visitor counts must sum to the total row.

Worked solution (20 min)

Write the filtered session CTE and check its row count against an unfiltered count, so you know how much volume the bot and consent filters removed.
Add the ROW_NUMBER first-session pick and assert one row per visitor.
Add the EXISTS outcome flag and aggregate with FILTER.
Add GROUPING SETS for the total and format the rate to four decimal places.
Spot-check one channel by hand: pull its visitor list, count signups directly, compare.

Expected result: One row per referrer_channel plus a total row, with visitors, signups and a decimal rate. Channels with zero signups appear with rate 0, not missing. The total rate lies strictly between the minimum and maximum channel rate unless one channel holds all the volume.

Verify it yourself

Per-channel visitor counts sum exactly to the total row's visitor count.
COUNT(*) equals COUNT(DISTINCT visitor_id) on the spine CTE.
Replacing the EXISTS with a plain inner join changes the signup count only if duplicate signup_completed events exist; if it does change, say by how much.
Every rate is between 0 and 1.
Re-running the first-session pick with session_id removed from the ORDER BY returns the same labels; if it does not, visitors hold same-timestamp sessions on different channels and the tie rule is doing real work.

Follow-up

The denominator is distinct visitors. If a browser release shortens cookie lifetime, what happens to this rate, and how would you tell that apart from a genuine drop?
A visitor's first session is direct and their signup session is paid search. Your label says direct. When is that the wrong answer for the decision being made?
How do you roll four weeks into a month, and why is averaging the four weekly rates wrong?

If a client’s revenue drops by 20% in one quarter, how would you approach identifying the root cause?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.
Fix the population and the time window before naming any metric.

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
How would you detect that the metric is being gamed rather than genuinely improving?

What statistical tests would you use to validate a hypothesis in a business context?

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.
Fix the population and the time window before naming any metric.

Follow-up

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

How would you calculate the potential market size for a new digital subscription service?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

How would you structure an analysis to determine if a new product launch is successful?

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.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

Estimate a threshold-triggered programme with regression discontinuity

Hard · regression discontinuity · instrumental variables · identification

Accounts reaching seats_licensed >= 25 in dim_account are automatically assigned a dedicated onboarding specialist; below 25 they are not. Leadership wants the programme's effect on 12-month net revenue retention and will not randomise coverage away from any account. Three years of dim_account and fct_subscription_period rows are available, including mrr_cents_constant_fx. Specify the design, the estimand it identifies, two threats that would invalidate it, and what changes when 8% of accounts below the threshold received a specialist anyway.

Approach

Set up a regression discontinuity on the running variable seats_licensed with a cutoff at 25, comparing accounts just below with accounts just above. The identifying assumption is continuity: absent the programme, expected 12-month NRR would be a continuous function of seat count through 25.
Name the estimand honestly and early. This is a local average treatment effect at 25 seats. It says nothing about a 5-seat or a 200-seat account, and that belongs in the first line of the answer rather than a footnote.
Estimate with local linear regression on each side, a triangular kernel, an MSE-optimal bandwidth and robust bias-corrected confidence intervals. Do not fit a high-order global polynomial; it imports weight from observations far from the cutoff and is known to manufacture discontinuities.
Test the two threats that actually apply. Manipulation: an account that wants the specialist can buy a 25th seat, which piles density just above the cutoff, so run a density test on seats_licensed around 25 and look for a spike at exactly 25. Bundling: if a price break, a plan tier or a support SLA also switches at 25 seats, the discontinuity measures the whole bundle, so check current_plan_tier and the price schedule at the cutoff.
Treat the 8% crossover as a fuzzy design. Treatment probability jumps at the cutoff without going from 0 to 1, so divide the jump in NRR by the jump in the probability of receiving a specialist. That is a Wald instrumental-variables estimator with the cutoff indicator as the instrument; it needs exclusion, which is exactly what the bundling check is about, plus monotonicity, and it narrows the estimand further to compliers at the cutoff.

Worked solution (45 min)

Fix the running variable as seats_licensed at the moment the assignment rule was evaluated, and fix the outcome as 12-month NRR computed from fct_subscription_period on mrr_cents_constant_fx for the account's cohort.
Plot mean NRR in one-seat bins on each side of 25 with a local linear fit. The picture comes before the estimate, because a discontinuity invisible in the binned plot is rarely real.
Run the density test at 25 and a continuity check on pre-cutoff characteristics such as billing_country, account_type and pre-programme MRR; these must be smooth through the cutoff.
Estimate the sharp RD with an MSE-optimal bandwidth and robust bias-corrected intervals, then repeat at half and double the bandwidth as a sensitivity check.
Estimate the first stage, the jump in specialist assignment at 25, and report the fuzzy estimate as the ratio with the estimand stated as the complier effect at the cutoff.

Expected result: A fuzzy regression discontinuity at 25 seats, estimated as the ratio of the NRR jump to the specialist-probability jump, identifying the local complier average effect at the cutoff under continuity, exclusion and monotonicity. With 8% crossover below the cutoff and near-full compliance above it, the first stage is about 0.92, so the fuzzy estimate is roughly 1.09 times the reduced-form jump and its standard error inflates by the same factor.

Verify it yourself

Characteristics fixed before the cutoff is crossed must not jump at 25. If account_type mix or billing_country jumps, the local comparison is not as-good-as-random.
The estimate should be stable across half and double the MSE-optimal bandwidth. Strong bandwidth sensitivity means the functional form is doing the work, not the discontinuity.
The reduced-form jump divided by the first-stage jump must reproduce the fuzzy point estimate exactly; if it does not, the two are being computed on different samples.
The effective sample inside the bandwidth, not the full account count, drives the standard error. Local linear RD typically needs on the order of three to four times the sample a randomised comparison would for the same power.

Follow-up

The density test shows a spike at exactly 25 seats. Is the design dead, and what would you do next?
You have 40,000 accounts but the bandwidth keeps 900. How does the detectable effect compare with a randomised comparison of the same nominal size?
Seat counts change over time. Which value of seats_licensed is the running variable, and what breaks if you pick the wrong one?

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

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.

Your part, not the team's

Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.

Describe a situation where you had to explain a complex technical finding to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Tell me about a time you failed to meet a project deadline; how did you manage it?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.
Close with what you would do differently, concretely.

Follow-up

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

Turn an ambiguous onboarding question into a measurable metric

Easy · scoping · metric definition · stakeholder

Two days before a planning review, a director asks whether onboarding is working. You have dim_user (account_created_at_utc, signup_surface, is_internal), fct_event (is_core_action, flow_id, flow_instance_id, event_name, occurred_at_utc, received_at_utc) and fct_session. No further meeting with the director is possible before you start work. Deliver three clarifying questions you would send in writing, the metric you will compute in the meantime with its numerator, denominator, window and exclusions, and one sentence naming the question you are deliberately not answering.

Approach

Recognise what is being probed: whether you convert a goal into a computable predicate without stalling for requirements or guessing in silence. Listing clarifying questions is the generic answer; shipping a defensible default alongside them is the strong one, because the review is in two days and it will happen with or without you.
Infer the decision behind the request. A question about whether onboarding works, arriving before a planning cycle, usually means whether to staff it next quarter. That points at a rate with visible headroom over several cohorts, not at a descriptive dashboard.
Write the three questions so that each one changes the SQL. Which population, all signups or only self-serve from dim_user.signup_surface. What counts as working, reaching a core action or completing the onboarding flow_id. Against what bar, last quarter's cohorts or a stated target.
Propose the default explicitly: seven-day activation on weekly signup cohorts. Numerator, users with is_core_action = TRUE events on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Denominator, the signup cohort with is_internal = FALSE. Publish with an eight-day lag, and state that the two-distinct-days threshold is a frozen choice rather than a discovery.
Name the exclusion in the same breath as the number. The series shows whether users activate; it does not establish that onboarding caused the level, which needs a staged rollout or an experiment.

Follow-up

The director replies that they meant the onboarding flow specifically, not activation. What changes in the query and in the caveats?
Your cohort metric needs an eight-day lag and the review is in two days. What do you present, and how do you label it?
Two of your three questions come back unanswered. Which one do you refuse to proceed without?