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Aera Technology Data Scientist Interview Questions & Guide 2026

Timed data scientist practice for Aera Technology: short SQL and probability sets run end to end, then scored against a rubric.

The 60-second brief

At Aera Technology, the Data Scientist role sits at the intersection of advanced machine learning and real-world supply chain decision-making. You are not just building models in a vacuum; you are developing the "Cognitive Operating System" that enables enterprises to automate complex business decisions. Your work involves transforming massive, high-dimensional datasets into actionable insights that drive efficiency and precision for global organizations.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Strip CI, retry and synthetic traffic first
Power experiments for heavy-tailed account revenue
Separate contracted seats from actively used seats

Overview

This role is critical because Aera Technology focuses on autonomous decision-making. You will be tasked with solving high-stakes problems, such as scaling forecasting services to handle hundreds of thousands of time series or diagnosing metric drops in complex automated workflows. You will collaborate closely with engineering and product teams to translate business requirements into robust, scalable data solutions. Success here requires a blend of rigorous statistical thinking, efficient coding, and a deep, intuitive sense for how data drives product outcomes.

The Aera Technology interview process

Aera Technology candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

High-Level Screen

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Deep-Dive Technical Evaluations

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

Meet Team Members

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

Leadership Interaction

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.

Final Decision

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What to demonstrate

Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
Whether you ask what decision is actually on the table before explaining anything

How to prepare

Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else

Where candidates lose points

Computing monthly churn against the entire customer base when contracts are annual

An annual contract has no opportunity to churn except at its renewal date, so an account that is eleven months from renewal is in the denominator while being incapable of appearing in the numerator. The resulting rate is smaller than the real one by roughly the ratio of the base to the renewal-eligible base, and it oscillates with the seasonality of when deals were originally signed rather than with anything about the customers. The corresponding trap on the other side is counting a churn on the date the record was updated rather than on term_end_date, which shifts losses into whichever month the operations team did its paperwork.

Treating raw request or usage volume as engagement

Most traffic in this domain is emitted by machines. Continuous-integration pipelines, scheduled batch jobs, synthetic monitors, backfills and client retries can all grow by an order of magnitude from one configuration change made by one engineer, and none of it represents a new decision to use the product. The inversion is what makes it dangerous: when the platform degrades, clients retry, so error-driven retry volume rises at the exact moment the customer is most likely to leave, and an engagement dashboard built on raw counts shows growth immediately before a churn. Filter on traffic_class and on successful status before anything else, and keep failed-request volume as its own separate series.

Reaching for a model before the target metric exists

Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.

Solving silently instead of narrating the reasoning

Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.

Questions & practice

If you are calculating the probability of a rare event, what distribution would you use and why?

Medium · statistics and probability

Approach

Say what the estimate is of, and over what population it generalises.
Write down the assumption the method needs before you use the method.
Quantify uncertainty explicitly rather than reporting a point estimate alone.

Follow-up

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

Explain the assumptions behind Linear Regression and how you validate them.

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

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

Simulate how the renewal calendar distorts monthly churn rates

Medium · simulation · churn · numpy · cohorts

Simulate 1,200 accounts on annual contracts. Draw each account's renewal month from a deliberately lumpy calendar: 30% renew in January and the remaining 70% are spread evenly over the other eleven months. At each renewal an account churns with probability 0.18, independent of month; survivors renew and come back twelve months later. Run 24 simulated months. For each month compute two rates: churned accounts over all live accounts, and churned accounts over accounts whose term ended that month. Report the mean and the month-to-month standard deviation of each series, and state which one belongs in an executive summary.

Approach

Build the panel with numpy state arrays rather than a per-account loop: a next_renewal_month vector, an alive boolean vector, and a loop over the 24 months only. Looping over 24 months is fine; looping over 1,200 accounts inside it is what makes the simulation too slow to iterate on.
Maintain the live set honestly. An account that churned in month m must leave the denominator from m+1 onward and can never be renewal-eligible again; if it stays, the naive rate drifts downward for reasons that have nothing to do with churn and the calendar effect gets buried.
Compute both series over the same months and compare dispersion, not only level. The eligible-base rate should sit near 0.18 with binomial noise scaled by that month's renewal count; the naive rate spikes in January and collapses in thin months.
Quantify the gap instead of describing it: the ratio of the two means is roughly the reciprocal of the average monthly renewal-eligible fraction, and the naive series' standard deviation is driven by the signing calendar rather than by customer behaviour.
Check against the closed form before trusting the output. With the live set maintained correctly the eligible-base rate is an unbiased estimator of 0.18 in every month, so a systematic offset means the bookkeeping is wrong, not that the simulation found something.

Worked solution (30 min)

rng = np.random.default_rng(0); p = [0.30] + [0.70/11]*11; next_renewal = rng.choice(12, size=1200, p=p); alive = np.ones(1200, bool)
For m in range(24): eligible = alive & (next_renewal == m); churn = eligible & (rng.random(1200) < 0.18); record churn.sum(), eligible.sum(), alive.sum() at month start; alive &= ~churn; next_renewal[eligible & ~churn] += 12
naive = churned / live_at_start; eligible_rate = churned / eligible, left as NaN where eligible == 0.
Report naive.mean(), naive.std(ddof=1), np.nanmean(eligible_rate), np.nanstd(eligible_rate, ddof=1) and the ratio of the two means.

Expected result: The eligible-base rate averages close to 0.18 (roughly 0.17 to 0.19 at this sample size) with no January spike. The naive rate averages near 0.015, about one twelfth of it, and its January values run several times a typical month, so its month-to-month standard deviation is a large fraction of its own mean.

Verify it yourself

The 24 monthly churn counts sum to 1200 minus the final alive count.
Switching the calendar to uniform (p = 1/12 everywhere) collapses the naive series' dispersion while leaving the eligible-base mean unchanged.
Setting the churn probability to 0 makes both series identically zero and leaves 1,200 accounts alive.
Quadrupling the account count roughly halves the standard deviation of the eligible-base rate without moving its mean.

Follow-up

Compounded over twelve months the naive rate lands close to the true annual churn. Does that rescue it?
How would you report churn in a month where only nine accounts were renewal-eligible?
Eighteen-month terms are now being sold alongside annual ones. What breaks?

Given a table of user logs, how do you identify the sequence of events leading to a specific conversion?

Medium · sql and data manipulation

Approach

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

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?

Write a query using SQL window functions to calculate a rolling 7-day average of sales.

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Say which table is the grain you start from, and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

How would you handle missing data in a large-scale join operation?

Medium · sql and data manipulation

Approach

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.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Sessionise interactive API traffic with a thirty-minute inactivity gap

Medium · window functions · sessionisation · gaps and islands

fct_api_request carries account_id, user_id (null for service accounts), request_at, traffic_class and http_status. Using only traffic_class = 'interactive' rows with a non-null user_id, group each seat's requests into sessions on a 30-minute inactivity threshold: a request more than 30 minutes after the previous request from the same (account_id, user_id) opens a new session. For one ISO week return, per account, the session count, the median session duration in minutes and the median requests per session. A single-request session has a duration of zero.

Approach

Filter first: traffic_class = 'interactive' and user_id IS NOT NULL. Machine traffic has no sessions in any useful sense, and leaving CI or batch rows in produces sessions that are really cron schedules.
Get the previous timestamp with LAG(request_at) OVER (PARTITION BY account_id, user_id ORDER BY request_at). Partitioning by user_id alone stitches one person's work across two different accounts into one fabricated session, because a human holds memberships in several accounts.
Flag a boundary where the lag is NULL or request_at - lag > interval '30 minutes'. Decide and state whether exactly 30 minutes continues the session; strictly greater is the conventional choice and needs to be written down either way.
Assign session ids with SUM(boundary::int) OVER (PARTITION BY account_id, user_id ORDER BY request_at ROWS UNBOUNDED PRECEDING), the standard running-count construction for islands.
Roll up to sessions with min(request_at), max(request_at) and count(*), then to accounts with percentile_cont(0.5) WITHIN GROUP (ORDER BY ...). Use medians, not means: session length is strongly right-skewed and one long-running client dominates the average.

Worked solution (30 min)

Build the filtered week: interactive rows with user_id IS NOT NULL inside [week_start, week_start + 7 days).
Add prev_at via LAG(request_at) OVER (PARTITION BY account_id, user_id ORDER BY request_at) and derive is_new_session = (prev_at IS NULL OR request_at - prev_at > interval '30 minutes').
Add session_seq = SUM(is_new_session::int) OVER (PARTITION BY account_id, user_id ORDER BY request_at ROWS UNBOUNDED PRECEDING).
Aggregate to sessions by (account_id, user_id, session_seq) taking min, max and count, with duration_minutes = EXTRACT(EPOCH FROM max - min) / 60.
Aggregate to accounts with count(*) AS sessions, percentile_cont(0.5) WITHIN GROUP (ORDER BY duration_minutes) and percentile_cont(0.5) WITHIN GROUP (ORDER BY request_count).

Expected result: One row per account with session_count, median_session_minutes and median_requests_per_session, computed over interactive human traffic only, where a lone request yields a session of duration zero and request count one.

Verify it yourself

Summing request_count across all sessions equals the filtered row count for the week exactly.
Every session duration is non-negative, and no two consecutive requests inside one session are more than 30 minutes apart when you re-derive the LAG within sessions.
Recomputing with the partition on user_id alone yields strictly fewer sessions and longer medians for any account sharing users with another account, which confirms the partition key matters.

Follow-up

Sessions belonging to colleagues in one account are correlated. What does that do to a t-test on session length across an experiment arm?
A long-poll or streaming endpoint keeps a connection open for hours. How do you stop it reading as one twelve-hour session?

If a key business metric suddenly drops by 10%, how do you structure your investigation to identify the root cause?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.
State what result would change your recommendation, so the answer is falsifiable.

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 design a dashboard to track the performance of an automated decision service?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Name one primary metric, then the guardrail that stops it being gamed.
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 do you define "success" for a new feature that automates inventory replenishment?

Medium · product sense and metrics

Approach

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

Follow-up

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?

How do you balance the trade-off between sample size and the duration of an experiment?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
Name the guardrails that would stop a launch even on a positive primary result.
State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

How do you determine the statistical significance of an A/B test result?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
Name the randomisation unit first; it decides the variance and what the test can detect.
Name the guardrails that would stop a launch even on a positive primary result.

Follow-up

What would you do if you could not randomise at all?
How would you handle interference between treated and control units?

Measure switching cost when switching cost is not observable

Medium · proxy metrics · bias statement · integration depth · stratification

Leadership wants switching cost tracked as a leading indicator of renewal. Nothing in the warehouse records it. Available: fct_api_request (account_id, workspace_id, environment, api_key_id, sdk_name, sdk_version, endpoint, traffic_class, http_status, request_at) and dim_account (account_id, employee_band, deployment_model, is_internal). Propose a proxy, state the direction and likely size of its bias, and name one decision the proxy is good enough for and one it is not. Deliver the proxy definition, the written bias statement, and the validation you would run against observed renewal outcomes.

Approach

Say first that switching cost is unobservable in this data and that the deliverable is a biased correlate with its bias written down, not a measurement. Anything presented as a direct measure of switching cost is already wrong before the SQL starts.
Define the proxy as production integration breadth per account over a trailing 28 days: distinct endpoint route templates, distinct api_key_id, and distinct workspace_id with environment = 'production', all restricted to http_status < 400 and traffic_class in ('interactive','batch'). Breadth, not volume, because request volume is one CI configuration change away from an order of magnitude.
State the biases with their mechanisms and their sign. Upward with account size, because breadth correlates with employee_band, so the proxy ranks large accounts as sticky whether or not they depend on anything. Downward for deployment_model = 'self_hosted', whose traffic does not all cross the managed gateway, so their breadth is systematically understated. Blind to criticality: one endpoint carrying a production billing path is a larger switching cost than twenty endpoints behind a read-only dashboard, and nothing in this data distinguishes them.
Handle the bias by stratifying rather than by pretending it is gone. Report the proxy within employee_band and deployment_model strata and state explicitly that cross-stratum comparisons are not supported by the construction.
Validate against the only outcome that is actually observed: renewal on the renewal-eligible base. Within strata, report renewal rate by proxy quintile for accounts whose term_end_date has passed plus a 45-day grace, and report discrimination at the operating point a capacity-bound team can work rather than a global AUC.

Worked solution (30 min)

Compute the trailing-28-day proxy per account: distinct endpoint, distinct api_key_id and distinct production workspace_id under successful, interactive-or-batch filters.
Join dim_account for employee_band and deployment_model and drop is_internal = true.
Build the renewal-eligible cohort from fct_subscription_period term_end_date in the target months with a 45-day grace, labelling each account renewed or not.
Within each employee_band stratum, report renewal rate by proxy quintile, and report the proxy distribution for self_hosted accounts separately so its understatement is visible.

Expected result: A proxy defined as production integration breadth over a fixed 28 days, a written bias statement naming upward bias with account size, downward bias for self-hosted deployment and total blindness to criticality, plus a within-stratum validation showing renewal rate by proxy quintile. The answer names one supported decision, such as ordering a capacity-bound outreach list inside a stratum, and one unsupported decision, such as setting price or contract terms by switching cost.

Verify it yourself

Every account's proxy must be computed over the same 28-day window; a variable window makes the quintiles incomparable.
Self-hosted accounts should show systematically lower breadth; if they do not, gateway coverage of that deployment model differs from what was assumed and the bias statement is wrong.
The renewal-eligible cohort should be roughly one twelfth of the paying base per month on annual contracts; a much larger share means the term_end_date filter is not doing what you think.

Follow-up

An account's endpoint breadth drops 40% in a week. Name three explanations that have nothing to do with reduced dependency.
Which decision would you refuse to make on this proxy, and what evidence would you need before making it?

Billable units per account jumped while nothing shipped

Medium · retries · idempotency · guardrail metrics

Billable units per paying account rose 22 percent month over month with no pricing or packaging change. From fct_api_request (account_id, endpoint, http_status, is_retry, idempotency_key, traffic_class, billable_units, request_at) and fct_usage_daily (account_id, sku_code, usage_date, billable_quantity), determine how much of the rise is delivered value and how much is duplicated work. Deliverable: the decomposed figure, the accounts it concentrates in, and a recommendation on whether to report the 22 percent at all.

Approach

Check the guardrail before the headline: compute the customer-visible server error rate per account for both months, numerator http_status >= 500 and denominator excluding synthetic_monitor and load_test. Metered volume rising alongside an error rate is the known failure mode in this domain.
Deduplicate logical work by counting billable_units once per (account_id, idempotency_key) at the first successful request rather than once per row. Rows with a null idempotency_key cannot be deduplicated, so report their share as an explicit uncertainty band instead of assuming they are all unique.
Split by traffic_class before interpreting anything, because one change to a continuous-integration configuration can multiply request volume overnight without a human deciding anything about the product.
Test concentration: compute the per-account distribution of the increase and its top-decile share. A rise carried by a few accounts scaling one batch job is a different finding from a broad shift and gets a different recommendation.
Reconcile against fct_usage_daily for the same accounts and dates, and be ready to explain the expected gap in two sentences: request rows include retries and failures carrying zero billable_units, and the usage table restates after first write.

Follow-up

A client retrying a request the server already completed produces duplicate billed work. What protocol or product change removes that, and what would you measure to confirm it worked?
If the duplicated volume was genuinely invoiced, what should finance do, and how does that change what belongs in the metric?

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.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Tell me about a project where you had to pivot your approach due to changing business requirements.

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Close with what you would do differently, concretely.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

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

Scope an open-ended request to predict account churn

Medium · scoping · churn modelling · operating point

A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.

Approach

The interviewer is probing whether you convert a vague request into a decision with a capacity constraint attached. A candidate who starts talking about model families has already failed the exercise.
Pin the event and the horizon first. Churn is only possible at term_end_date, so the population is accounts renewing in the next 60 to 90 days, not the whole base. Ask explicitly whether contraction and downgrade count as churn or only full non-renewal, because the three have different base rates and different interventions.
Pin the action and the capacity. Six people times a realistic number of meaningful interventions per week gives k, and k is what the list is ranked to. Evaluate on precision at k rather than a global AUC over accounts that will never be contacted.
Audit leakage before choosing features. Every feature needs a timestamp proving it existed before the prediction date. A downgrade amendment, a churn reason code, and a ticket opened after the renewal conversation started are all leaks that will make the offline number look excellent and the live list useless.
Ask for the counterfactual now rather than later. Coverage is assigned deliberately, so without a held-out slice agreed at the start the intervention can never be evaluated, and you will be asked for its impact in nine months regardless.
Propose the smallest artefact that closes the loop: a weekly ranked list sized to capacity with two or three inspectable reasons per row, plus a stated policy for accounts below the line.

Follow-up

The director insists all accounts are in scope, not only those renewing soon. How do you answer without simply refusing?
Historical non-renewals number about 30 a year. At what point do you tell them a model is the wrong tool and a rules list is better?
Which candidate features would you drop purely because you cannot date them?

Report an underpowered consumption test to a non-technical executive

Medium · communication · uncertainty · experimentation

An account-randomised packaging change ran six weeks across 900 paying accounts. The effect on billable units per account per month is plus 4.1 percent, with a 95 percent interval from minus 3.2 to plus 11.8 after clustering standard errors at the account and applying the pre-registered winsorisation at the 99th percentile. An executive with no statistical background wants one number this week to decide a full rollout. Produce a three-sentence spoken answer, one chart, and an explicit recommendation of ship, stop or keep running, with the cost of each option stated.

Approach

The interviewer is probing whether you can be decision-useful without either hiding the uncertainty or hiding behind it. Start from the decision rather than the statistics: establish what the executive would do differently at plus 4 percent versus zero, because if the action is identical the interval does not matter.
Translate the interval into consequences in units the executive already reasons about. Multiply both endpoints by the cohort's baseline consumption and contracted rates to give an annualised revenue range, so the answer is a range of dollars rather than a range of percentages.
Price the option to wait. Using the observed variance, state roughly how many additional account-weeks halve the interval width, so keep running becomes a quantified choice instead of a stall.
Offer a cheaper path to the same decision: a lower-variance proximate outcome such as successful billable units on the new SKU, or CUPED using each account's pre-period consumption, quoting the expected variance reduction as one minus the squared pre-post correlation.
Give a recommendation and name the single observation that would reverse it. A strong answer commits; a generic one recites the interval and leaves the decision on the table.

Follow-up

The executive says it clearly works and is just not provable, so ship it. What is your answer?
How much of the interval width comes from clustering and how much from the revenue tail, and what would you do about each?
If you had to ship this week with no more data, which guardrail would you watch for the first fortnight and at what threshold would you roll back?