Interview guides · Condé Nast

Condé Nast Data Scientist Interview Questions & Guide 2026

Data scientist interview prep for Condé Nast: original practice on experiment design, SQL and metric reasoning, with worked solutions.

The 60-second brief

As a Data Scientist at Condé Nast, you are at the forefront of leveraging data to drive strategic decisions and enhance the user experience across a myriad of acclaimed brands. This role is pivotal in shaping the future of digital media, where your analytical skills will directly impact how content is delivered and engaged with by millions of consumers globally. By harnessing complex data sets, you will contribute to optimizing product offerings and enhancing user engagement, which is vital for maintaining Condé Nast's competitive edge in a rapidly evolving market.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Focus areas

Pick a randomisation unit that respects interference
Define numerator, denominator and window precisely
Decompose a metric move by segment and mix

Overview

The significance of the Data Scientist role extends beyond mere number crunching; it involves synthesizing insights that inform marketing strategies, personalize user experiences, and ultimately drive revenue growth. You will work closely with cross-functional teams, including product managers, engineers, and designers, to tackle challenging questions about user behavior and content performance. The role is both exciting and demanding, offering the opportunity to influence major strategic decisions that affect some of the most recognized brands in the world, such as,, and.

The Condé Nast interview process

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

HR Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Interviews

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

Behavioral Assessments

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

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.

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.

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.

Reading a dozen metrics with no multiplicity control

Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.

Questions & practice

Explain how you would approach a regression analysis.

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

Follow-up

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

How would you implement a recommendation algorithm?

Medium · machine learning and modelling

Approach

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

Follow-up

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

Rebuild per-visitor ordering without groupby convenience methods

Easy · pandas · vectorisation · window logic

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

Approach

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

Worked solution (20 min)

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

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

Verify it yourself

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

Follow-up

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

Write a function to perform a specific data transformation.

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

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

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.

Worked solution (25 min)

Write the per-instance collapse CTE and confirm COUNT(*) equals COUNT(DISTINCT flow_instance_id).
Add the three predicates one at a time, recording how many instances each removes.
Aggregate by day, surface and app_version with FILTER on the clean-completion flag.
Compute the NULL-instance coverage count as a separate select and report it alongside.
Pick the worst day-surface-version cell and pull ten raw instances to confirm the disqualification reason.

Expected result: One row per (day, surface, app_version) with started, completed_clean and a rate bounded by 0 and 1, plus a scalar coverage count of events carrying a NULL flow_instance_id. Instances completing after 30 minutes appear in the denominator only.

Verify it yourself

No rate exceeds 1.
COUNT(*) equals COUNT(DISTINCT flow_instance_id) in the collapsed CTE.
Total started across cells equals the count of distinct flow_instance_id values carrying a flow_started event in the window.
Instances with an error strictly between start and completion are excluded from the numerator but present in the denominator.

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?

How do you prioritize your tasks when working on multiple projects?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.

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?

Given a dataset with user engagement metrics, how would you identify key trends?

Medium · product sense and metrics

Approach

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

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 metrics would you consider when evaluating a marketing campaign's success?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
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

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 design an A/B test for a new feature on our website?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
Name the randomisation unit first; it decides the variance and what the test can detect.
Say whether units interfere with each other, and switch design if they do.

Follow-up

What would you conclude if the result is positive but the test is underpowered?
What would you do if you could not randomise at all?

How do you ensure data quality and integrity in your analyses?

Medium

Approach

Work from the decision backwards to the evidence you would need.
Say what you would check first and why it is the highest-information step.
State your assumptions explicitly before working the problem.

Follow-up

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

Define success for a rebuilt first-run onboarding checklist

Easy · activation · metric tree · cohorts

A rebuilt first-run checklist ships to all new signups next month. You have dim_user (user_id, account_created_at_utc, signup_surface, is_internal) and fct_event (user_id, occurred_at_utc, is_core_action, event_name, surface). Propose a primary metric with an explicit numerator, denominator, window and publication lag, plus two guardrails and one diagnostic you would refuse to treat as success. The team wants a number it can read weekly, and the constraint is that everything must be computable from those two tables alone. Deliver the metric tree from the north star down to the metric you chose.

Approach

Fix the grain and the cohort key first: the checklist is seen by users, so count on user_id with account_created_at_utc as the cohort key, and say out loud that the north star is account-grained so the tree crosses grains here deliberately rather than by accident.
Take seven-day activation as the primary — core action on at least 2 distinct UTC dates inside [account_created_at_utc, +7 days) — because the two-distinct-days predicate cannot be satisfied by the single checklist-completion click the feature itself produces.
Demote checklist completion rate to a diagnostic and give the reason: it is an output of the feature, so it is near-perfectly correlated with having shipped the feature and cannot fall when the feature is bad.
Pick guardrails by the failure each one catches, not by what is easy to query: week-4 signup-cohort retention catches an activation gain that does not persist, and p50 minutes to first core action catches a checklist that adds steps to a path users already completed.
State the operational rules explicitly: is_internal = FALSE, an 8-day publication lag, and signup_surface values 'invite' and 'sso_provisioned' split out because those users arrive through an administrator rather than a self-serve signup and may not be shown the checklist at all.

Worked solution (20 min)

Write the primary in full: numerator = cohort users with is_core_action = TRUE events on at least 2 distinct UTC dates in [account_created_at_utc, +7 days); denominator = all dim_user rows in the cohort week with is_internal = FALSE.
Draw the tree downward: weekly active accounts completing a core action, then seven-day activation rate multiplied by weekly signups, then checklist step completion and time-to-first-core-action labelled as diagnostics.
Attach the timing rules: 8-day lag, and a cohort with fewer than seven full elapsed days published blank rather than partial.
Attach each guardrail to its named failure mode in one line each, so the guardrail list reads as a list of specific risks rather than a list of metrics.
Write the exclusion rule and the surface split, and state what you would do if 'invite' users turn out to be a third of the cohort.

Expected result: A primary metric stated as numerator, denominator, window, lag and exclusions; two guardrails each tied to a named failure mode; and checklist completion explicitly demoted to a diagnostic with the reason given.

Verify it yourself

The denominator includes cohort users who never returned. If it is built from users with any event, the metric is conditioned on survival and will read several points too high.
Recomputing a cohort's activation rate three days after first publication must return the same number. If it moves, the right-hand window bound or the late-arrival rule is missing.
The per-surface cohort counts must sum to the unsplit cohort count, so confirm signup_surface IN ('invite','sso_provisioned') is broken out rather than dropped. Note the limit while you are there: neither dim_user nor fct_event carries a pre-signup identifier, so whether those users were ever shown the checklist is not answerable from these two tables — the surface split is the only handling available, and the metric definition has to say that rather than imply exposure was verified.

Follow-up

Activation rises three points but week-4 retention is flat. What do you tell the team, and what would you need to distinguish a real gain from pulled-forward activity?
The checklist ships on web only. What changes in the denominator, and what breaks if you leave every surface in?
Signup mix shifted toward 'invite' the same week. How would you show whether the activation move was mix or behaviour?

Separate a definition edit from a behaviour change

Easy · definition-change · metric-versioning · cohorts

Seven-day activation rate is documented as core actions on at least 2 distinct UTC dates inside the first seven days after account_created_at_utc, and every cohort published through last week was computed on that definition. The dashboard now reads 38% for the signup cohort that started Monday, against 31% for the cohort before it. Two things landed in that week: an onboarding change, and a commit to the metric's SQL that you have not read yet and that came with no documentation update. You have dim_user, fct_event and the metric's version history. Deliver two numbers: how many of the 7 points are behaviour and how many are the definition. Then say which published cohorts are no longer comparable.

Approach

Diff the metric SQL across the commit boundary and enumerate every changed predicate individually: the distinct-day threshold, the is_internal exclusion, the is_core_action source, the window bounds, and the identity key. A single commit routinely changes more than one, and each has its own sign.
Dual-run both definitions over the same 26 cohort weeks. The definition effect is the level shift between the two series computed on identical data, which isolates it from anything that happened in the world.
Read the behaviour effect only from the old-definition series, comparing the new cohort to its own trailing cohorts. The new-definition series cannot answer the behaviour question because it has no pre-period.
Explain the mechanism of the changed predicate rather than only its size. Lowering a distinct-day threshold from 2 to 1 admits every user who acted once in a single session, which is a large and low-intent population, so a several-point jump is the expected magnitude and not a surprise.
State the restatement plan: pick one definition, backfill the full history on it, and annotate the break date. A series carrying two definitions is not a time series.

Follow-up

The onboarding team wants the 1-day threshold kept because it is easier to move. What is your argument, and what would change your mind?
How would you make a definition change visible to every consumer of this metric without relying on people reading a changelog?

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.

Tell me about a time when you faced a significant challenge in a project. How did you overcome it?

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.
Pick a story where you drove the decision, not one where you observed it.

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?

Handle a request for numbers supporting a decision already made

Hard · integrity · framing · stakeholder

A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

Approach

Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.

Follow-up

The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
What changes if the honest analysis says the sunset is clearly the wrong call?
How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?