Acclaim Technical Services Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at Acclaim Technical Services (ATS), you operate at the intersection of high-stakes mission support and advanced analytical engineering. ATS is a specialized firm supporting U.S. Federal agencies, and your work directly impacts national security and defense initiatives. You are not just building models; you are translating complex, often unstructured mission requirements into technical solutions that empower decision-makers.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

• Turn a vague request into a measurable question
• Define numerator, denominator and window precisely
• Size an experiment before anyone launches it

Overview

The role involves managing the full lifecycle of data—from curation and cleaning to the development of sophisticated AI models, statistical algorithms, and predictive analytics. You will work in environments where data quality varies, and your ability to navigate these limitations while maintaining rigor is essential. Because ATS is an Employee Stock Ownership Plan (ESOP) company, the culture emphasizes ownership, long-term impact, and collaboration with mission partners. You should expect to be deeply embedded in the mission, where your technical output provides the clarity needed for critical operational decisions.

The Acclaim Technical Services interview process

Acclaim Technical Services candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Technical Screening

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

Deep-Dive Problem Solving

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

• Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
• Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
• How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

• Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
• Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
• Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Behavioral Interview

Most of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.

What to demonstrate

• Whether you can state the other side's argument accurately before you explain why you disagreed
• What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
• Whether you distinguish being overruled from being wrong, and can give an example of each

How to prepare

• Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
• For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
• Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.

Discussions with Leadership

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

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.

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.

Crediting a treatment for regression to the mean

Selecting a group because it is extreme (lowest-engagement users, accounts having their worst month, the bottom decile of a score) moves that group's expected next-period value back toward the average even under no treatment, by exactly as much as the selecting measure is imperfectly correlated with its own later value. Compare against units that met the same selection rule and went untreated, or use two pre-periods so the bounce-back is visible before the intervention starts. A pre-post number on a group chosen for being extreme measures the selection rule, not the treatment.

Comparing periods without accounting for seasonality or day-of-week

Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.

Questions & practice

If a test shows a positive result but the confidence interval is very wide, how do you communicate this to leadership?

Medium · statistics and probability

Approach

1. Write down the assumption the method needs before you use the method.
2. Sanity-check the answer against a simple bound or a simulated case.
3. Translate the result into the decision it informs, in one plain sentence.

Follow-up

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

Implement seven-day activation from its written definition

Medium · metric definition · cohorts · pandas · joins

Implement the seven-day activation rate. Inputs: dim_user with user_id, account_created_at_utc and is_internal; fct_event with user_id, occurred_at_utc and is_core_action. A user activates when core-action events carrying a non-NULL user_id fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). The denominator is every non-internal user whose account_created_at_utc lands in the cohort week, including users with no events at all. Return one row per cohort week with numerator, denominator and rate, publishing only weeks whose last signup is at least eight days old.

Approach

1. Build the denominator first, from dim_user alone, filtered on is_internal = False. Deriving it from the join is the standard way to lose every user who never fired an event, which is exactly the population the metric is about.
2. Join events to users on user_id with a left join from the user side, then apply the window as a half-open interval: occurred_at >= created AND occurred_at < created + 7 days. The right bound is exclusive, so an event at exactly created + 7 days does not count.
3. Count distinct UTC dates per user, not distinct events. Floor occurred_at_utc to date before the nunique, and do it in UTC rather than local time so the threshold does not move with the user's country.
4. Apply the >= 2 threshold, aggregate to cohort week, and compute the rate by re-summing numerator and denominator per week rather than averaging any per-user or per-day rate. Fix the week anchor explicitly: cohort_week is the Monday of the signup week in UTC, which is what Postgres DATE_TRUNC('week') returns and what any SQL version of this metric will produce. In pandas, subtract dt.weekday days from the floored timestamp. If you reach for periods instead, the anchor that matches is to_period('W') (equivalently 'W-SUN'), whose weeks end Sunday and therefore start Monday; to_period('W-MON') labels weeks that end on Monday, so it runs Tuesday through Monday and its start_time is a Tuesday. Mixing the two shifts every cohort label by one day and silently moves Mondays into the previous week.
5. Suppress immature weeks: drop any cohort week whose maximum account_created_at_utc is within 8 days of the data cut, and return them as absent rather than as a partial number.

Follow-up

• The threshold is 2 distinct days. What changes in the reported history if someone moves it to 3, and how would you publish that change?
• Invited seats and SSO-provisioned users have no pre-signup session. Should they be in this denominator at all, and what does including them do to the rate for sales-assisted accounts?
• How would you produce the same metric at account grain, and which of the two would you put on the dashboard?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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)

1. 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.
2. 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.
3. rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
4. 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.
5. 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?

Write a query using SQL window functions to calculate a rolling average of user activity over the last 30 days.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Explain the difference between various joins when merging datasets with significant overlap or discrepancies.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Read an experiment from first exposure, not assignment

Hard · experiments · window-functions · srm

fct_experiment_exposure holds experiment_id, unit_type, unit_id, variant, user_id, assigned_at_utc, first_exposed_at_utc, is_in_analysis_population and planned_end_utc. fct_event holds user_id, occurred_at_utc, is_core_action, and carries events up to a known data cut, :data_cut_utc. For one experiment randomised on unit_type = 'user', return per variant: exposed units, units with at least one core action in the seven days after that unit's own first exposure, the rate, and the variant share of exposed units. Only units whose seven-day window has fully elapsed as of the data cut belong in the readout. Units appearing under more than one variant are excluded from both arms and counted separately.

Approach

1. Run the contamination pass as an aggregate, not a window: SELECT unit_id FROM fct_experiment_exposure WHERE experiment_id = :exp GROUP BY unit_id HAVING COUNT(DISTINCT variant) > 1, then anti-join it away. PostgreSQL rejects COUNT(DISTINCT variant) OVER (PARTITION BY unit_id) outright, since DISTINCT is not implemented for window functions; if you want the test inline, MIN(variant) OVER (PARTITION BY unit_id) <> MAX(variant) OVER (PARTITION BY unit_id) is the equivalent that does run.
2. Do not resolve contamination by keeping the earliest variant. A unit that saw both arms carries treatment from both, so assigning it to either one biases that arm.
3. Define the population as is_in_analysis_population = TRUE AND unit_type = 'user' AND first_exposed_at_utc < planned_end_utc AND first_exposed_at_utc + interval '7 days' <= :data_cut_utc. The horizon filter is what makes the readout reproducible next week instead of drifting with every re-run; the data-cut filter is the one that actually buys seven days of follow-up, since a unit exposed an hour before the horizon otherwise contributes an hour of observation to a seven-day rate.
4. Measure the outcome on a per-unit relative window: LEFT JOIN fct_event on user_id with is_core_action = TRUE and occurred_at_utc in [first_exposed_at_utc, first_exposed_at_utc + interval '7 days'). LEFT JOIN so units with no outcome stay in the denominator at zero rather than being deleted by an inner join.
5. Check the sample ratio before reading the effect: variant share of exposed units against the intended split, tested as a binomial. Run it on the truncated population as well as on the full exposed set, because if one arm exposes later on average the data-cut filter removes more of that arm and can manufacture a ratio mismatch the randomisation did not have. A mismatch on the full set means the exposure data is not a valid randomisation and invalidates the readout rather than being a footnote under it.
6. Report the per-variant rate, the absolute difference, and the fact that the variance unit is unit_id. That is straightforward here only because the grain is already one row per user; a per-session outcome under user randomisation would need a delta-method or bootstrap standard error instead.

Worked solution (35 min)

1. Count units per variant and units with more than one variant; report the contaminated count before anything else.
2. Build the exposed population CTE with both the horizon filter and the seven-days-before-data-cut filter, assert one row per unit_id, and record per variant how many units the second filter removed.
3. LEFT JOIN the bounded outcome events and flag conversion with a boolean aggregate.
4. Aggregate per variant to exposed, converted, rate and share, and run the binomial check on the share, both before and after the truncation.
5. Re-run with a fixed calendar window from experiment_start_utc and record the difference as the size of the dilution you avoided.

Expected result: One row per variant with exposed, converted, rate and share, plus a scalar count of excluded contaminated units. Every retained unit was first exposed at least seven days before the data cut, so each one has a full seven days of observation and the rates are directly comparable.

Verify it yourself

• Exposed counts across variants sum to the population count minus the contaminated units.
• MAX(first_exposed_at_utc) among retained units is at least seven days before :data_cut_utc.
• Variant shares sum to 1.
• No unit_id contributes to two variant rows.
• The truncation drops a similar share of each arm; if it does not, note the imbalance alongside the SRM result rather than reading the effect.
• Swapping the relative window for a calendar one changes the rates; if it does not, exposure timing is balanced and you can say so.

Follow-up

• Some units were assigned days before they were exposed. What does analysing the assigned set instead do to the estimated effect, and in which direction?
• The split is 51/49 on 400,000 exposed units. Do you read the result?
• The treatment arm exposes on average two days later than control. What does that do to a fixed calendar outcome window, and which arm does it favour?

What are the most important metrics to consider when evaluating the reliability of an automated analytic tool?

Medium · product sense and metrics

Approach

1. Restate the decision this analysis has to support, and who acts on the answer.
2. Name one primary metric, then the guardrail that stops it being gamed.
3. 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?
• What would you do if the primary metric and the guardrail moved in opposite directions?

If a key performance metric suddenly drops by 20%, what steps would you take to diagnose the cause?

Medium · product sense and metrics

Approach

1. Decompose the metric into the rates that drive it, and say which one you would check first.
2. State what result would change your recommendation, so the answer is falsifiable.
3. 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?

How would you design a dashboard to track the effectiveness of a new cybersecurity policy?

Medium · product sense and metrics

Approach

1. Restate the decision this analysis has to support, and who acts on the answer.
2. State what result would change your recommendation, so the answer is falsifiable.
3. Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

What are the most common experimentation pitfalls you have encountered in your previous work?

Medium · a/b testing and experimentation

Approach

1. Decide the analysis before seeing data, including how long it runs and when you look.
2. Name the randomisation unit first; it decides the variance and what the test can detect.
3. 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?

How do you determine the required sample size to ensure statistical significance for an experiment?

Medium · a/b testing and experimentation

Approach

1. State the primary metric and the minimum effect worth shipping, then size the test.
2. Say whether units interfere with each other, and switch design if they do.
3. Name the randomisation unit first; it decides the variance and what the test can detect.

Follow-up

• How would you handle interference between treated and control units?
• What would you conclude if the result is positive but the test is underpowered?

Choose one success number for a homepage redesign

Easy · conversion · guardrails · mix effects

A homepage redesign is ready to test. Marketing wants visit-to-signup conversion as the single success metric: distinct fct_session.visitor_id with a 'signup_completed' event in fct_event, over distinct visitor_id with a session started in the window where is_bot_flagged = FALSE and consent_state <> 'denied'. Sessions carry referrer_channel and device_type. Argue for or against that metric, name what you would decide on instead, and give the one guardrail you would refuse to ship without. Deliverable: the metric, the guardrail, and the failure mode in writing.

Approach

1. List everything that moves this rate without the page changing: referrer_channel mix, device_type mix, cookie lifetime and bot-rule changes, then pre-register the segments you will decompose on so the decomposition is not chosen after seeing the result.
2. Argue that signup is an intermediate outcome the homepage can inflate by over-promising, and propose activated signups per 1,000 eligible visitors — the same numerator further restricted to users clearing the seven-day activation bar — as the number the decision actually rests on.
3. Explain the consent exclusion rather than copying it: a 'denied' session can never be joined forward to a user, so leaving it in the denominator puts visitors there who have no path into the numerator and depresses the level permanently.
4. Separate what the metric can and cannot be used for: inside a randomised comparison over one window it is fine, but as a trend line across a platform privacy change it will step down because visitor_id resets more often, with no behaviour change behind it.
5. State the trade you would accept in advance: a smaller conversion gain with flat activation beats a larger conversion gain with activation down, and write the threshold before the readout.

Worked solution (15 min)

1. Restate the proposed metric and list every input that can move it with the page unchanged.
2. Compute the mix-held-fixed counterfactual: reweight this period's per-segment rates by last period's segment volumes, and report the difference against the pooled rate as the mix effect.
3. Define the decision metric: activated signups per 1,000 eligible visitors, with the seven-day activation predicate spelled out.
4. Write the guardrail and the explicit trade-off sentence you would put in the ship decision.
5. Record the denominator exclusions with the reason for each, not just the predicate.

Expected result: Conversion kept as a diagnostic, activated signups per 1,000 visitors named as the decision metric, a specified mix decomposition, and one guardrail carrying a written trade-off.

Verify it yourself

• Weekly rates rolled up by re-summing numerator and denominator differ from the average of the weekly rates whenever weekly traffic is uneven. If the two agree, a rate-of-rates has been computed somewhere.
• Per-segment numerators must sum to the pooled numerator. If they do not, a visitor is appearing in two segments and the segmentation is not a partition.
• Activation can only be read eight days after the last signup in the window, so the earliest honest decision date is eight days after the test stops.

Follow-up

• The redesign wins overall but the entire gain sits in paid social. What do you do, and what would change your mind?
• How do you roll four weekly conversion rates up to a month, and why does the obvious way give a different answer?
• Where does holding the segment mix fixed stop working as a correction?

Attribute a flow-completion drop to one client build

Medium · release-regression · instrumentation · staged-rollout

Core-flow completion rate on surface = 'ios', defined as distinct flow_instance_id reaching 'flow_completed' within 30 minutes of 'flow_started' with no 'error_shown' on the same id in between, fell from 78% to 71% over four days. A new iOS build began a staged rollout on day one. Using fct_event columns flow_id, flow_instance_id, app_version, event_name, user_id and occurred_at_utc, produce the completion rate per build per day, size the loss in absolute completions, and say whether the fix is a rollback. Treat the possibility that the instrumentation changed rather than the flow.

Approach

1. Cut the rate by app_version and day, each build against its own denominator. A pooled series during a staged rollout is a weighted average whose weights move daily, so it declines in proportion to the rollout share even when the old build is perfectly flat; confirming that proportionality is itself the evidence the regression is build-specific rather than environmental.
2. Test the denominator before believing the rate. Compute flow_started events per distinct user_id on each build: if the new build re-mints flow_instance_id on retry, or fails to carry the id from start to completion, the denominator inflates and the numerator falls with nothing changing for the user.
3. Normalise to a user-level outcome that is immune to the id question, namely completions per distinct user_id per day on each build. This is the number that says whether anyone actually failed to finish.
4. Check the error path: pull error_shown counts and their properties for flow_instance_id values on the new build. A genuine regression produces errors; an instrumentation break produces missing completions with no corresponding error volume, and the two prescribe different fixes.
5. Size the real loss as the per-user completion gap multiplied by daily users on the new build, and give the rollback recommendation conditional on the error evidence rather than on the rate.

Follow-up

• The rollout is at 45% and product wants to go to 100% tomorrow. What do you say, and what would you need by when?
• If the flow_instance_id is genuinely being re-minted, what is the correct historical treatment of the four affected days?
• How would you have caught this on day one instead of day four?

Suggested practice checklist

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

• Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
• Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
• State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

• Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
• Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
• Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

• Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
• Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
• Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

• Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
• Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
• Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

• Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
• Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
• List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

• Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
• Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
• Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

• Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
• Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
• Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

The project you argued against

Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.

Tell me about a time you identified a flaw in an existing analytical process. How did you advocate for the necessary change?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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?

Quantify your own impact without claiming the topline you touched

Hard · self-assessment · attribution · communication

You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.

Approach

1. Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
2. Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
3. Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
4. Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
5. Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
6. Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.

Follow-up

• Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
• A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
• If you could only keep two of your three claims, which do you drop, and why that one?