Interview guides · Alcon

Alcon Data Scientist Interview Questions & Guide 2026

Case practice for Alcon data scientist candidates: scope a vague prompt into a measurable question before you analyze it.

The 60-second brief

This guide covers what a Data Scientist at Alcon is expected to do and how to prepare for the interview.

Scope your preparation by the data you would actually touch, because the title will not tell you. A seat that lives in event logs and weekly readouts rewards fluency in aggregation and metric definitions; a seat that owns a model in production rewards fluency in train/serve skew, retraining cadence and drift monitoring. The fastest way to find out which one you are interviewing for is to ask what the team shipped last quarter and what it gets paged about.

Focus areas

Define numerator, denominator and window precisely
Decompose a metric move by segment and mix
Turn a vague request into a measurable question

The Alcon interview process

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

Phone Screen

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical Evaluations

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

Where candidates lose points

Counting on an identity key that changes underneath the metric

visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.

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.

Reporting a mean for a heavy-tailed metric without saying what it hides

For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.

Analysing at a different unit than the one randomised

Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.

Questions & practice

Describe a situation where you had to deploy a computer vision model. What were the hardware constraints, and how did you optimize the model's latency?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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?

Explain a time when a model you deployed did not perform as expected in production. How did you diagnose and resolve the issue?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Describe how backpropagation works in a deep neural network. What are the mathematical implications of vanishing gradients?

Medium · machine learning and modelling

Approach

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

Follow-up

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

Split a pooled conversion drop into rate and mix

Medium · mix shift · decomposition · pandas

You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.

Approach

State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.

Worked solution (25 min)

Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.

Expected result: Three scalars summing exactly to the observed pooled change, plus a segment table whose contribution column also sums to that change. In the stated case rate and mix carry opposite signs: several segments converted better while volume moved toward a weaker-converting one, which is what makes the pooled move look inexplicable until it is split.

Verify it yourself

On synthetic data where every segment appears in both weeks, every segment rate is held constant and only the weights move, rate_effect and interaction are exactly 0.
On synthetic data where every segment appears in both weeks, weights are held constant and only rates move, mix_effect and interaction are exactly 0.
The per-segment contribution column sums to the pooled change, matching the sum of the three scalars.
Swapping the two weeks negates the total, as it must, but not the parts. Assert the three identities that actually hold: the interaction term is exactly invariant, because both of its factors change sign; the swapped rate effect equals -(rate_effect + interaction); and the swapped mix effect equals -(mix_effect + interaction). They still sum to the negated pooled change. Expecting all three to flip sign is the common wrong assertion.
Plant a segment present only in week 1, holding 3 percent of week-1 visitors at a rate well below pooled. Under the documented pooled-rate convention its mix contribution is w_s1 * r_pooled0 and its interaction contribution is w_s1 * (r_s1 - r_pooled0), with a rate contribution of exactly 0; under a zero-fill convention its mix contribution is exactly 0 and the whole w_s1 * r_s1 lands in interaction. Assert whichever one the function documents, and fail if mix is asserted non-zero while the code zero-fills.

Follow-up

The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?

How would you design a data pipeline to gather, clean, and integrate structured and unstructured data from multiple legacy databases?

Medium · sql and data manipulation

Approach

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

Follow-up

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

Seven-day activation rate by weekly signup cohort

Easy · cohorts · activation · date-bounds

dim_user holds user_id, account_created_at_utc, is_internal. fct_event holds user_id, occurred_at_utc, is_core_action. A user is activated when core-action events fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Return, for the last twelve complete weekly signup cohorts, the cohort week, cohort size, activated users and the activation rate. Exclude is_internal users. Every signup in the cohort week stays in the denominator, including users who never returned.

Approach

Start from dim_user as the denominator spine with is_internal = FALSE and DATE_TRUNC('week', account_created_at_utc) as the cohort key. Driving the query from the event table instead would silently condition on having events and delete the entire non-activating population.
Join fct_event on user_id with is_core_action = TRUE and a per-user bound, occurred_at_utc >= u.account_created_at_utc AND occurred_at_utc < u.account_created_at_utc + interval '7 days'. The bound is correlated to each user's own signup timestamp, not a single global date range.
Aggregate per user with COUNT(DISTINCT occurred_at_utc::date) >= 2, then LEFT JOIN that back onto the spine and COALESCE the flag to FALSE so non-activators contribute a zero rather than vanishing.
Restrict the published cohorts to those whose week ended at least eight days ago. A cohort younger than that has not finished its seven-day window, so its rate is mechanically low and reads as a decline.
Roll up by summing the numerator and denominator per cohort week, and state the two-distinct-days threshold next to the number since it is a choice that re-bases the whole history if changed.

Worked solution (20 min)

Write the cohort spine and confirm its total equals the count of non-internal signups in the date range.
Write the per-user distinct-active-days CTE with both interval bounds and inspect a handful of users manually.
LEFT JOIN, COALESCE the flag, aggregate to cohort week.
Apply the eight-day publication lag and drop the incomplete cohort.
Re-run with a closed upper bound (<= +7 days) and note how many users change state, to show the boundary is doing work.

Expected result: Twelve rows, one per cohort week, each with cohort_size, activated and rate. Cohort sizes sum to the total non-internal signup count across those twelve weeks. No cohort is younger than eight days.

Verify it yourself

activated is never greater than cohort_size in any row.
A user with three core actions on a single day is not counted as activated.
Removing the LEFT JOIN in favour of an inner join raises every rate, which demonstrates the denominator was being conditioned on survival.
Cohort sizes sum to an independent COUNT(*) over dim_user for the same range.

Follow-up

Why two distinct days rather than one event? What happens to the published history if someone changes it to three?
Invited seats and SSO-provisioned users get an account_created_at_utc at provisioning and may never sign in. Should they be in this denominator?
The rate rose 3 points this week. What do you check before believing it?

How do you prioritize your tasks and manage your time when you are guiding a team across multiple active, high-visibility projects?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

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

Explain how you would set up an A/B test to measure the impact of a newly implemented automated analytics solution.

Medium · a/b testing and experimentation

Approach

State the primary metric and the minimum effect worth shipping, then size the test.
Decide the analysis before seeing data, including how long it runs and when you look.
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?
How would you handle interference between treated and control units?

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?

Pooled signup conversion fell while every segment rose

Medium · mix-shift · simpsons-paradox · decomposition

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

Approach

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

Follow-up

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

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

Repeat the depth protocol on the second-ranked area with the same six-problem structure.
Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

Re-run the six prompts from day one under the same clock and compare both correctness and time.
Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

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 a disagreement with a team member or stakeholder regarding a project's technical direction. How did you resolve it?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
State the situation in two sentences and spend the rest on your reasoning.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

How did you know the outcome was caused by your change?
What would you do differently if you ran that project again?

Describe a time when you used quantitative methods to identify an operational bottleneck or a process improvement opportunity.

Medium · behavioural and stakeholder questions

Approach

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

Defend a flat experiment readout against a post-hoc segment

Medium · experiments · segmentation · pushback

A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.

Approach

Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.

Follow-up

The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?