Interview guides · Edgeverve

Edgeverve Data Scientist Interview Questions & Guide 2026

Edgeverve Data Scientist interview guide: reported rounds, practice questions with worked solutions and a 7-day prep plan.

The 60-second brief

As a Data Scientist at Edgeverve, you play a pivotal role in harnessing data to drive business insights and enhance product offerings. Your expertise in statistical analysis, machine learning, and data interpretation will directly influence the strategic direction of Edgeverve's projects and initiatives. This role is critical not just for optimizing current processes but also for enabling Edgeverve to innovate and maintain its competitive edge in the market.

The boundary with engineering varies enough to be worth asking about directly. Some seats end at the analysis and the writeup; others carry feature pipelines, scheduling and alerting, which decides whether production data plumbing belongs in your prep at all.

Focus areas

Pick a randomisation unit that respects interference
Size an experiment before anyone launches it
Separate novelty effects from durable behaviour change

Overview

In particular, you will engage with large datasets, applying advanced analytical techniques to solve complex problems across various domains, such as finance, supply chain, and customer experience. Your contributions will directly affect how Edgeverve develops its AI capabilities and improves the user experience for its clients.

This position offers a unique opportunity to work on high-stakes projects where your analytical skills can lead to significant business transformations. Expect to collaborate closely with cross-functional teams, using your insights to inform decisions that shape Edgeverve's products.

The Edgeverve interview process

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

Online 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

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Where candidates lose points

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.

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

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

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.

Reading an observational correlation as a causal effect

Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.

Questions & practice

You have a dataset with an imbalanced class distribution. What strategies would you employ?

Medium · statistics and probability

Approach

Translate the result into the decision it informs, in one plain sentence.
Write down the assumption the method needs before you use the method.
Quantify uncertainty explicitly rather than reporting a point estimate alone.

Follow-up

What sample size would you need to detect an effect half this size?
How would you explain this result to someone who does not know statistics?

Write a function to implement k-means clustering.

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.
Check what information would not exist at prediction time, and exclude it.

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Permutation test for a difference in conversion rates

Medium · permutation test · resampling · inference

Write a two-sided permutation test from scratch for a difference in conversion rates, using no scipy hypothesis function. Input: a DataFrame with unit_id, variant in {control, treatment} and converted in {0,1}, one row per randomisation unit. Compute the observed difference in proportions, then build the null distribution by reshuffling the variant labels while holding each arm's size fixed. Report the p-value as (1 + the count of permuted statistics at least as extreme in absolute value) / (B + 1) with B at least 10,000, and return the permutation distribution.

Approach

Name the null being tested: the sharp null that each unit's outcome is the same under either label. That is what licenses permuting labels, and it is stronger than the null of equal means, which matters when someone asks whether the test is valid under unequal variances.
Extract converted to a single numpy array of 0s and 1s and record n_treatment. Every permutation is then just a reshuffle of one array, and the treatment mean is the mean of the first n_treatment entries of the shuffled array.
Vectorise the B permutations with rng.permuted on a tiled 2-D array, or with argsort of a (B, n) random matrix. A Python loop calling np.random.shuffle B times is correct but roughly an order of magnitude slower and often runs past the time limit.
Use the +1 correction in both numerator and denominator. Without it a p-value of exactly 0 is reportable, which is false: the observed labelling is itself one of the permutations, so the smallest attainable p-value is 1/(B+1).
Compare the resulting p-value against a two-proportion z-test as a sanity check. At these sample sizes they should agree closely; a large divergence means the statistic or the shuffle is wrong, not that the permutation test found something subtle.

Worked solution (25 min)

y = df['converted'].to_numpy(); n_t = (df['variant'] == 'treatment').sum(); obs = y[treat_mask].mean() - y[~treat_mask].mean().
Build the null: for B draws, shuffle y and take the mean of the first n_t entries minus the mean of the rest.
p = (1 + (np.abs(null) >= abs(obs) - 1e-12).sum()) / (B + 1), with the small tolerance so exact ties count as at least as extreme.
Return obs, p and the null array; plot or describe the null to confirm it is centred at 0.

Expected result: The observed difference, a p-value in [1/(B+1), 1], and a null distribution whose mean is approximately 0 and whose standard deviation approximately matches the analytic pooled standard error sqrt(p_pool*(1-p_pool)*(1/n1 + 1/n2)).

Verify it yourself

The null distribution's mean is within a few Monte Carlo standard errors of 0; if it is centred on the observed difference, the code bootstrapped instead of permuted.
The null's standard deviation agrees with the analytic pooled standard error to within a couple of percent.
The p-value agrees with a hand-computed two-proportion z-test p-value to within about 0.01 at these sample sizes.
Setting converted identical in both arms by construction yields a p-value near 1, and a planted large effect yields the floor of 1/(B+1).

Follow-up

The arms are 200 and 20,000 units. Does the permutation test stay valid, and what happens to its resolution at B = 10,000?
Give a 95 percent confidence interval for the difference. Can you get it from this permutation distribution, and if not, what would you run instead?
The randomisation unit is user_id but the outcome is per session. What breaks, and what is the fix?

Given a dataset, how would you implement dimensionality reduction?

Medium · sql and data manipulation

Approach

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

Follow-up

How does the query change if the join becomes one-to-many?
How would you verify this result without re-running the same query?

How would you optimize a SQL query for better performance?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
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?

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?

What data structures would you use to optimize performance for a large-scale data processing task?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
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.

Follow-up

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

How would you design a face recognition system?

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

Follow-up

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

How do you prioritize tasks when working on multiple projects?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
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

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?

Given a dataset, how would you determine the most significant predictors for a specific outcome?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
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?

What is the difference between supervised and unsupervised learning?

Medium

Approach

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

Follow-up

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

A test that won on day four and lost by day twelve

Medium · peeking · sequential testing · multiple comparisons · winner's curse

A two-week test was checked every morning. On day four the primary metric crossed p = 0.03 and the team shipped. By day twelve, with logging still running, the estimate had fallen to a 0.2% lift at p = 0.61. The day-four deck also reported a significant 9% lift among paid_search visitors on mobile_web, one of twenty segments inspected. Explain both patterns quantitatively, say what the day-four evidence was actually worth, and specify the monitoring rule that prevents a repeat.

Approach

Price the peeking. A fixed-sample test controls type I error at one pre-declared look. Ten daily looks at a nominal 5% raise the false positive rate to roughly 0.15 to 0.20, because the running test statistic takes a random walk that will eventually cross the boundary under the null.
Price the winner's curse separately, because it is a different error. Conditioning an estimate on having crossed the threshold biases it away from zero, by a factor of roughly 3 when true power is 20% and only about 1.1 at 80% power. Day four sits at roughly 29% of planned information, which is exactly the low-power case.
Read day twelve as the better estimate rather than as a contradiction. Nothing regressed; the day-four value was an extreme draw that was selected precisely for being extreme.
Price the segment claim. Across twenty segments at a nominal 5%, at least one crosses about 64% of the time under a global null, since 1 - 0.95^20 = 0.64, and the slice that surfaces is by construction the one with the most favourable noise.
Specify the rule instead of scolding. Either fix the horizon and look once, or use group-sequential boundaries such as O'Brien-Fleming that spend almost no alpha early, or use always-valid confidence sequences that stay correct under continuous monitoring. Pre-register the small set of segments, correct across the ones tested, and treat anything found afterwards as a hypothesis needing its own powered test.

Worked solution (25 min)

Count the looks actually taken, twelve daily, and state the inflated alpha band of 0.15 to 0.20 for ten looks, rising with more.
Place day four on the information fraction, about 4/14 = 29% of planned sample, and apply the exaggeration factor for low power.
Compute 1 - 0.95^k for the number of segments actually inspected; with k = 20 this is 0.64.
Report the day-twelve interval as the estimate and say plainly that the experiment supports no effect at any size the team would ship for.
Write the rule into the experiment template: horizon and segments declared at launch, sequential boundaries if interim looks are needed at all.

Expected result: The day-four result is consistent with pure noise: ten looks inflate the false positive rate to roughly 0.15 to 0.20, and an estimate conditioned on crossing at about 29% information is exaggerated by roughly a factor of three at low power. The segment finding appears about 64% of the time across twenty slices under a global null. The supportable conclusion is a 0.2% lift whose interval covers zero.

Verify it yourself

1 - 0.95^20 = 0.6415. If your family-wise number lands near 0.05 you have computed the per-test rate instead.
The day-twelve standard error should be roughly sqrt(12/4) = 1.7 times smaller than day four's. If the interval did not narrow, sample was not accumulating and there is a second problem.
A Bonferroni threshold for twenty segments is 0.0025; check whether the mobile_web result would have survived it.

Follow-up

Under O'Brien-Fleming with five planned looks, roughly what nominal p-value would the first look have needed to stop?
The feature already shipped. What analysis can you still run, and what can it not tell you?
If mobile_web paid_search were genuinely the only affected segment, how would you design the confirmatory test and how large would it have to be?

Tell a pipeline outage from a collapse in usage

Medium · pipeline-failure · instrumentation · event-volume

Weekly active accounts completing a core action fell 9%, and almost the entire fall sits in accounts whose events carry surface = 'ios'. App crash rates and store reviews are unchanged. You have fct_event with occurred_at_utc, received_at_utc, event_name, is_core_action, app_version and surface, plus fct_session and the ingestion job run log. Establish within the hour whether iOS engagement fell or iOS events stopped arriving, name the evidence that distinguishes them, and say what you would publish on the dashboard in the meantime.

Approach

Compare the event-name composition inside surface = 'ios' against the prior four weeks as shares, not counts. A behaviour collapse scales most event names together; a dropped event definition or a broken downstream filter hits specific event_name values while page_view and session-opening events hold steady. That shape difference is the fastest discriminator available.
Profile the received_at_utc minus occurred_at_utc distribution per day for surface = 'ios'. A stalled-then-backfilling pipeline shows a fat upper tail and a recovering p99; a silently dropped stream shows an unchanged lag distribution over a smaller volume. The two failure modes have different remedies and different histories.
Cut by app_version. A logging SDK change arrives with one build and ramps with its adoption curve; an infrastructure fault arrives across every build within the same hour. Checking this costs one group-by and rules out half the hypothesis space.
Cross-check against a signal that does not travel the suspect path: server-emitted events with session_id NULL, and subscription or billing activity for the same accounts. If those accounts are still transacting, the users did not leave.
Publish an ex-iOS total with an explicit annotated break rather than a blended total. A blended number during a known ingestion gap is wrong in a direction you can already name, and republishing it daily spreads the artefact into every downstream report.

Follow-up

Suppose the events do eventually backfill. What is your policy for restating the published weekly numbers, and who needs to be told?
What monitor would have caught this before a human noticed the weekly metric, and what would it alert on?
If is_core_action is maintained in the tracking plan, what governance would stop a change to that list from silently moving a north-star metric?

Suggested practice checklist

For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.

Day 1 — Metric anatomy

For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.

Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.

Day 2 — Diagnosing a drop without guessing

Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.

Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.

Day 3 — Should we build it

Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
Write the counter-metric that would make you kill the feature even if it wins on the primary metric.

Deliverable: A one-page product memo ending in a decision rather than a list of considerations.

Day 4 — The places aggregate numbers lie

Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.

Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.

Day 5 — Technical maintenance, aimed at metrics

Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.

Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.

Day 6 — Turning engineering work into data science stories

Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.

Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.

Day 7 — Mock case and gap list

Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
Listen back and mark every moment you proposed a solution before the success metric existed.
Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.

Deliverable: A recorded case plus a rewritten opening 90 seconds.

Explaining a method to someone who will not follow the maths

Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.

How do you handle disagreements with team members?

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Pick a story where you drove the decision, not one where you observed it.
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?

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?

Disagree with a product manager's roadmap claim using data

Medium · causal inference · selection bias · stakeholder

A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.

Approach

Recognise what is being probed: whether you can separate a number being right from an inference being wrong, and do it without costing the PM face. The generic answer recites that correlation is not causation; the strong one names the specific confound and proposes the cheapest design that could distinguish the explanations.
State the alternative concretely. Accounts that connect an integration in week one are accounts that already have a workflow and a technical owner, so week-one intent plausibly drives both the connection and week-four retention. The selection is on intent, which no amount of post-hoc adjustment observes.
Order the discriminating analyses by cost. First, condition on pre-connection activity by comparing retention within strata of week-one core-action count, which removes the crude version of the confound but not unobserved intent. Second, look for variation in integration availability that was unrelated to intent, such as a staggered release or an outage window. Third, an encouragement design that randomises a prompt to connect and reads the intent-to-treat effect on week-four retention, which is the only version that identifies an effect.
Run the timing check, because it is nearly free and it is the most persuasive single piece of evidence. If the retention advantage among connectors is already visible before any of them connected, the causal story is largely finished.
Pre-commit to what would change your mind and say it before you show anything: if the gap survives stratification and the encouragement arm moves week-four retention at all, the feature has a case and you will say so.
Open the 1:1 by agreeing with the true part, that the correlation is real and worth chasing, then ask what effect size the roadmap plan assumes. That makes the size of the claim the topic instead of its authorship.

Follow-up

The encouragement test needs six weeks and the roadmap locks in two. What do you recommend in the interim?
Stratifying on week-one activity closes half the gap. What do you conclude, and what do you still not know?
How would you word this in the roadmap document so the PM's original number is reframed rather than deleted?