Interview guides · Alten Calsoft Labs

Alten Calsoft Labs Data Scientist Interview Questions & Guide 2026

Take-home style practice for data scientist candidates: a messy dataset, a fixed time budget, and an analysis you write up and defend.

The 60-second brief

As a Data Scientist at Alten Calsoft Labs, you are stepping into a highly impactful role that bridges advanced analytics, machine learning, and strategic business initiatives. Alten Calsoft Labs operates as a premier technology consulting and engineering partner for top-tier enterprises. In this specific capacity—focusing heavily on Digital Marketing—you will be the analytical engine driving how our clients understand, target, and retain their customer base.

Ask early whether the loop includes an asynchronous take-home or a timed live case, because the two are graded on different things. A take-home is read as an artifact: the question you decided to answer, what you did about missing or malformed records, and a conclusion stated plainly enough for someone to act on. A reviewer who cannot rerun your notebook discounts the result whatever score is printed in it. Hold to the stated time box and write down what you would have done with more of it, since the follow-up round is usually a live defence of the same work.

Focus areas

Size an experiment before anyone launches it
Decompose a metric move by segment and mix
Separate novelty effects from durable behaviour change

Overview

Your work directly influences product growth, user acquisition strategies, and marketing spend efficiency. By leveraging vast amounts of user behavior data, campaign performance metrics, and demographic information, you will build predictive models that optimize digital marketing funnels. This is not a role where you will simply build models in a vacuum; you will be expected to translate complex data into actionable insights that dictate multimillion-dollar marketing strategies.

What makes this role particularly critical is the blend of technical rigor and business acumen required. Based in our Palo Alto hub, you will collaborate closely with cross-functional teams, including marketing leaders, product managers, and data engineers. You will tackle challenges involving multi-touch attribution, customer lifetime value (LTV) prediction, and large-scale A/B testing, making your contributions highly visible and essential to the bottom line.

The Alten Calsoft Labs interview process

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

Recruiter Screen

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
Whether you name what you have not done instead of stretching to cover every line of the posting
Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

Technical 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

Onsite Interviews

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Watching an experiment daily and stopping when it crosses significance

A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.

Reading a dozen metrics with no multiplicity control

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

Extrapolating a first-week lift inflated by novelty effects

Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.

Questions & practice

Explain the bias-variance tradeoff and how you manage it in your models.

Medium · statistics and probability

Approach

Sanity-check the answer against a simple bound or a simulated case.
Quantify uncertainty explicitly rather than reporting a point estimate alone.
Say what the estimate is of, and over what population it generalises.

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?

What are the assumptions of linear regression, and what happens if they are violated?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Rebuild per-visitor ordering without groupby convenience methods

Easy · pandas · vectorisation · window logic

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

Approach

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

Worked solution (20 min)

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

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

Verify it yourself

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

Follow-up

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

Write a Python function to parse a messy JSON log file and extract specific event timestamps.

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Compute rates by summing numerator and denominator separately, never by averaging rates.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

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?

How would you use Python to merge two large datasets that do not fit into memory?

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Say which table is the grain you start from, and join outward from it.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

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?

Paying accounts with no active seat in 28 days

Medium · anti-join · null-semantics · retention-risk

dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.

Approach

Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.

Worked solution (25 min)

Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
Write the active-account spine and the 28-day activity CTE.
Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.

Expected result: One row per silent active account with seats_licensed and days_since_last_core_action, ordered by seats_licensed descending. Accounts that never emitted a core action show NULL and are reported as a separate group rather than as the worst churn risks.

Verify it yourself

Silent accounts plus accounts with at least one active user equals the total count of active non-internal accounts.
The NOT IN variant returns zero rows, which is the demonstration that a NULL is present.
No account_id appears twice in the output.
Every days_since_last_core_action is either NULL or greater than 28.

Follow-up

How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?

What metrics would you use to evaluate the success of a newly launched email newsletter?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
State what result would change your recommendation, so the answer is falsifiable.
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?
How would you detect that the metric is being gamed rather than genuinely improving?

How do you evaluate the performance of a churn prediction model where only 2% of users actually churn?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

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

How would you measure the true incremental impact of a new Facebook ad campaign?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.
Decompose the metric into the rates that drive it, and say which one you would check first.

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?

If we notice a 15% drop in user engagement on our mobile app over a week, how would you diagnose the root cause?

Medium · product sense and metrics

Approach

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

Follow-up

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

How would you design an experiment to test the impact of a new feature if you cannot split users randomly (e.g., a city-wide marketing rollout)?

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
Say whether units interfere with each other, and switch design if they do.
State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

What is Simpson's Paradox, and can you give an example of how it might occur in marketing data?

Medium · a/b testing and experimentation

Approach

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

Follow-up

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

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

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.
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.
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.
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.
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)

Restate the proposed metric and list every input that can move it with the page unchanged.
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.
Define the decision metric: activated signups per 1,000 eligible visitors, with the seven-day activation predicate spelled out.
Write the guardrail and the explicit trade-off sentence you would put in the ship decision.
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?

Decide whether a one-day core-action drop is real

Easy · seasonality · day-of-week · late-arriving-data

A daily dashboard counts distinct fct_event.user_id with is_core_action = TRUE, filtered on occurred_at_utc, and is read at 09:00 UTC. This morning it shows yesterday down 22% against the day before. fct_event is partitioned on received_at_utc. You have fct_event, fct_session and dim_user with thirteen months of history. Deliver a one-paragraph verdict, escalate or do not escalate, with the evidence that settles it, before anyone proposes a product hypothesis.

Approach

Identify which two weekdays the comparison actually spans, then pull the same weekday-pair transition for the last 52 weeks and place the observed 22% inside that distribution. A day-over-day comparison in a product with a weekday pattern is a comparison of two different populations, so the reference class is the same transition historically, not the prior day.
Measure partition completeness rather than assuming it. For each of the last 30 days compute the share of that day's occurred_at_utc rows that had landed by 09:00 UTC the following morning, split by surface; mobile clients buffer events offline, so the freshest partition is systematically short and the shortfall is not uniform across surfaces.
Recompute the same series keyed on received_at_utc. If the drop survives on both keys it is not a lateness artefact; if it exists only on occurred_at_utc it is the partition filling in.
Check the two exclusion flags before segmenting anything: a change in is_bot_flagged coverage or a batch of is_internal accounts entering or leaving moves a distinct-user count with no user behaviour behind it.
Only if the movement survives all of the above, begin the segment decomposition. Say explicitly in the verdict which of these four checks the movement passed, so the next reader does not repeat them.

Follow-up

What publication lag would you set for this dashboard, and how would you derive the number rather than pick it?
If you switch the metric to received_at_utc, what does that break for anyone comparing to historical figures?
How would you detect the same problem automatically, so a human does not have to notice it each morning?

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.

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.

How do you handle A/B tests where the primary metric is a ratio (e.g., click-through rate)?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Quantify the outcome, including what you would not claim credit for.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

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

Explain a wide interval to a non-technical executive

Medium · communication · uncertainty · pricing

A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

Approach

Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.

Follow-up

The executive says to give the midpoint and they will manage the risk. What do you do?
How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?

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?