Interview guides · American Civil Liberties Union

American Civil Liberties Union Data Scientist Interview Questions & Guide 2026

Data scientist prep for American Civil Liberties Union candidates: drill A/B design, power and minimum detectable effect on original exercises.

The 60-second brief

The Data Scientist role at the American Civil Liberties Union (ACLU) is a high-impact position situated at the intersection of data-driven advocacy and organizational strategy. As a Data Scientist, you are responsible for transforming raw data into actionable insights that empower the ACLU to protect civil liberties and advance systemic justice. Your work directly influences how the organization allocates resources, engages donors, and measures the efficacy of its advocacy campaigns.

How much statistics you need depends on the flavour of the seat. Experiment-facing work wants you deep enough to notice that repeated looks at accumulating data inflate the false positive rate of a fixed-sample test; modelling-facing work wants estimation and honest uncertainty intervals.

Focus areas

Decompose expected loss into PD, LGD, EAD
Set fraud thresholds by expected cost
Separate authorization, settlement and dispute outcomes cleanly

Overview

You will operate in a fast-paced environment where your analytical rigor serves a clear mission. You will be expected to bridge the gap between complex statistical modeling and the practical, real-world needs of stakeholders who are focused on legal and social change. This role is critical for the ACLU because it provides the quantitative foundation for high-stakes decision-making, requiring you to balance technical precision with a deep understanding of the organization’s long-term objectives.

The American Civil Liberties Union interview process

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

Phone Screen

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
Whether your reason for wanting the role points at the work itself rather than the company's reputation
Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Technical Evaluation

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Where candidates lose points

Assuming a model is fair because protected attributes are not among its inputs

Postcode, device, tenure, income proxies and even transaction patterns correlate with protected characteristics, so a model can produce a disparate outcome without ever reading the attribute. Credit decisions additionally carry an explainability obligation in many jurisdictions, since a denial has to be accompanied by its principal reasons, which constrains model form and feature engineering rather than being a reporting afterthought. Treating fairness testing and reason-code generation as design constraints from the first model version is far cheaper than retrofitting them to a deployed one.

Recalibrating an underwriting cutoff on approved and funded applicants only

Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

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.

Questions & practice

Collapse retry chains and compute a dollar-weighted approval rate

Medium · sessionisation · window functions · dollar-weighted rates

fct_payment_authorization gives auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, requested_at, auth_result, is_reversal, channel and issuer_country. Two reference frames give the minor-unit exponent per currency and a daily rate to one reporting currency. Collapse retry chains first: attempts sharing card_token_id, merchant_id and amount_minor whose consecutive gaps are under 15 minutes form a single attempt, whose outcome is its last row. Exclude reversals and zero-amount verifications. Return a 7-day rolling dollar-weighted approval rate by channel and issuer_country.

Approach

Filter before grouping: drop is_reversal rows and zero-amount verifications, since neither is a purchase attempt and both would otherwise sit in the denominator.
Sort by card_token_id, merchant_id, amount_minor and requested_at, take the gap to the previous row within that key, mark a chain start where the gap exceeds 15 minutes or the key changes, and label chains with a cumulative sum of that flag. This is a gap rule between consecutive attempts, not a fixed clock bucket, so a chain may span more than 15 minutes in total.
Keep each chain's terminal row by requested_at. If a retry was approved, the purchase was approved; keeping the first row reports the decline that caused the retry as the outcome.
Convert amounts exactly once: amount_minor divided by 10 to the power of the currency exponent, multiplied by the reference rate for the authorization date. Do not reach for settlement_fx_rate, which is null on precisely the declined rows the denominator needs.
Build the rolling window as a ratio of two rolling sums, approved value over total value, per channel and issuer_country. A rolling mean of daily ratios weights a quiet Sunday the same as a busy Friday.

Follow-up

The count-weighted rate is flat while the dollar-weighted rate falls 80 basis points. What do you look at first?
How would you choose the 15-minute window rather than inheriting it?
A merchant moves from two retries to five. Which of your two rates moves, and is that a real change in approval quality?

Simulate false alarms in a merchant chargeback monitoring rule

Medium · simulation · rare events · monitoring thresholds

Baseline matured first-chargeback rate is 12 per 10,000 settled transactions. A monitoring rule alerts when a merchant's observed monthly rate exceeds twice baseline. For monthly settled transaction counts of 500, 2,000, 10,000 and 50,000, simulate the false-alarm probability per merchant-month under the baseline, and the power to detect a merchant whose true rate is 30 per 10,000. Then, for a portfolio of 4,000 merchants split 60, 25, 10 and 5 percent across those four counts, give the expected number of false alarms per month.

Approach

Recognise the rule is a threshold on an integer count, not on a continuous rate. At n = 500, twice baseline is 24 per 10,000, so the first observable value above it is 2 chargebacks, or 40 per 10,000. Derive the trigger count for every n before simulating anything.
Draw binomial counts with numpy at p = 0.0012 and take the share at or above the trigger for the false-alarm rate, then repeat at p = 0.0030 for power. Use at least 200,000 draws per cell so a probability near 0.001 has a usable standard error.
Cross-check every simulated cell against the Poisson approximation with lambda = n*p, which is tight here because p is tiny. A mismatch almost always means the trigger count is off by one.
Weight the per-merchant false-alarm probabilities by the portfolio mix, and report the share of expected alerts contributed by each size band rather than only the total.
Close on the operating consequence: a fixed multiplicative threshold is not a constant false-alarm rate across merchant sizes, so either the threshold scales with n or small merchants need a minimum volume before the rule applies.

Worked solution (30 min)

For each n, compute trigger = floor(2 * 0.0012 * n) + 1 and print the four values before simulating.
Simulate 200,000 binomial draws per n at p = 0.0012 and take the share at or above the trigger.
Repeat at p = 0.0030 and record power for the same triggers.
Compute the Poisson tail 1 - CDF(trigger - 1, lambda = n*p) for both p values and confirm agreement within Monte Carlo error.
Multiply the false-alarm probabilities by 2400, 1000, 400 and 200 merchants and sum.

Expected result: Trigger counts of 2, 5, 25 and 121. False-alarm probability roughly 12 percent at n = 500, roughly 10 percent at n = 2,000, under 0.1 percent at n = 10,000 and effectively zero at n = 50,000. Power against 30 per 10,000 roughly 44, 72, 85 and above 99 percent. Expected false alarms about 390 per month, with over 99 percent of them coming from the two smallest bands.

Verify it yourself

The four trigger counts are derived, not guessed: each is the smallest integer strictly above 0.0024n, giving 2, 5, 25 and 121. Derive them in exact or integer arithmetic, because 20.0012*10000 evaluates to 23.999999999999996 in binary floating point and floor-plus-one silently returns 24.
Simulated probabilities match the Poisson tail at lambda = n*p within about two Monte Carlo standard errors.
Power exceeds the false-alarm rate at every n; if it does not, the null and alternative rates are swapped.
Doubling the draw count moves each probability by less than its own simulated standard error.

Follow-up

How would you set a threshold that holds the false-alarm rate roughly constant across merchant size?
The rule reads the transaction month, but disputes arrive for up to 120 days afterwards. What does that do to the alert and how would you fix it?
What does a month of these false alarms cost, and how would you decide whether it is worth paying?

Estimate a delinquency roll-rate matrix and project twelve months

Hard · roll rates · markov chain · survivorship

fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.

Approach

Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.

Follow-up

How would you validate the projection against what actually happened, and over what window?
The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
When would you prefer a vintage curve to a roll-rate projection, and why?

Explain how you would use SQL window functions to calculate rolling averages for campaign performance.

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.
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?

Attribute chargebacks to transaction month without fanning out

Medium · fan-out · cohort attribution · maturity

Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.

Approach

Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.

Worked solution (30 min)

CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.

Expected result: One row per transaction month with settled_txns, chargeback_cases, a rate in basis points, and a status of 'final' or 'incomplete'. The final months form a roughly flat series; the incomplete months at the right edge are visibly and correctly labelled rather than sloping to zero.

Verify it yourself

settled_txns for a given month is identical whether or not the dispute CTE is joined, which is the direct test that no fan-out occurred.
COUNT(DISTINCT auth_id) in the numerator is at or below the dispute row count; if they are equal, no authorization in the data carries two cases and the fan-out risk is latent rather than absent.
Every month flagged 'final' has its last day at least 120 days before the current date.
Attributing by opened_at instead shifts the series later and flattens the right edge, which demonstrates why the attribution key matters.

Follow-up

Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?

How would you design a dashboard to track the impact of a new digital advocacy campaign?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.
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?
Which segment would you cut first, and what would that rule out?

How do you define "success" for a new donor acquisition program?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

What are the most important trade-offs when choosing between short-term engagement and long-term user retention?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.

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 a core engagement metric suddenly drops by 10%, how would you begin to diagnose the root cause?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Fix the population and the time window before naming any metric.
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?
How would you detect that the metric is being gamed rather than genuinely improving?

How do you determine the appropriate sample size to ensure statistical significance?

Medium · a/b testing and experimentation

Approach

Say whether units interfere with each other, and switch design if they do.
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.

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?

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

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
State the primary metric and the minimum effect worth shipping, then size the test.
Decide the analysis before seeing data, including how long it runs and when you look.

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?

Diagnose a sample ratio mismatch before reading the result

Easy · sample ratio mismatch · instrumentation · chi-square

A two-week test of a new risk rule logged 512,340 exposures in treatment and 508,110 in control against an intended 50/50 split. The dashboard reports a 1.8 percent relative lift in the count-weighted authorization approval rate, p equals 0.004. Exposures are written by the events pipeline at the point the rule is evaluated. Before anyone reads the lift, test whether the split is consistent with 50/50, state your verdict, rank the mechanisms that would produce this imbalance, and say what you do with the two weeks of data.

Approach

Run the test rather than eyeballing the ratio: a chi-square goodness-of-fit on one degree of freedom against the expected 50/50, or equivalently a binomial test on the treatment share. A 0.42 percent imbalance looks trivial and is not, because at a million exposures the null distribution is extremely tight.
Declare the result uninterpretable rather than adjusting it. An SRM means units are missing from one arm conditional on something, and the something is usually correlated with the outcome, so reweighting or trimming does not restore exchangeability; it just hides the selection.
Localise the mismatch before hypothesising about it: recompute the split by day, by channel, by issuer_country and by whether the rule actually fired. An SRM confined to one slice points straight at the code path that produced it.
Rank mechanisms by how often they are the cause here. First, exposure logged downstream of a step the treatment changes, so any arm-specific drop-off before the log line silently deletes units. Second, assignment keyed on something mutable or nullable, such as card_token_id across a reissue or a null customer_id on guest traffic. Third, bot or fraud filtering applied after assignment and hitting arms unequally. Fourth, retry rows deduplicated after assignment rather than before. Fifth, a ramp or a rollback that moved mid-test.
Close the loop with an A/A run on the fixed pipeline before rerunning the A/B, and add a continuous SRM check with an alert threshold so the next occurrence is caught on day one instead of at readout.

Worked solution (15 min)

Total exposures N = 1,020,450, so expected per arm E = 510,225 and the absolute deviation is 2,115 in each direction.
Chi-square = 2 * 2115^2 / 510225 = 2 * 4,473,225 / 510,225 = 2 * 8.767 = 17.53 on one degree of freedom.
Convert: sqrt(17.53) = 4.19 standard deviations, so p is about 3e-5. The observed treatment share is 0.50208 against a null standard error of 0.000495, which is the same statement in another form.
Declare SRM, withhold the 1.8 percent lift from any decision, and produce the per-day and per-channel split table that localises the cause.

Expected result: Chi-square is about 17.5 on one degree of freedom, p is about 3e-5. This is a sample ratio mismatch. The correct output is a defect report with a localisation table, not a treatment effect; the 1.8 percent lift is not reported as a result even with a caveat.

Verify it yourself

The binomial standard error of the treatment share is sqrt(0.25 / 1020450) = 4.95e-4; the observed share 0.50208 sits 4.19 standard errors from 0.5, matching the chi-square.
Re-running the same test on the assignment log rather than the exposure log should show a clean 50/50 if the randomiser is sound and the loss is downstream, which immediately narrows the search.
Summing the per-day chi-square contributions should reproduce the total; a single day dominating the sum is a deploy, not a design flaw.

Follow-up

Suppose the mismatch is entirely in one issuer_country and the treatment adds a 3-D Secure step there. What is the most likely code path, and what does that imply about the sign of the observed lift?
What SRM alert threshold would you set for a daily check, and how do you keep it from firing constantly across many concurrent tests?
If the split is exactly 50/50 but the two arms have different distributions of mcc and channel, is that an SRM? What is it, and what do you do about it?

A single day of settled volume comes in thirty percent low

Medium · pipeline failure · null rates · data freshness

Yesterday's settled volume is 30 percent below the same weekday across the previous eight weeks, while authorization counts and approved counts for the day look normal. You have fct_payment_authorization with requested_at, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency, created_at and updated_at, plus warehouse load metadata. Decide within the hour whether this is a real drop or a load problem, and say what you would post to the on-call channel in either case.

Approach

Check the data before the business. Compare max(updated_at) and row counts per load partition for the affected day against the prior eight same weekdays; a truncated load usually shows as a count deficit confined to a specific hour range.
Measure null rates per column by requested_at date. Authorization fields present while settled_at and settlement_amount_minor are null points at the settlement feed; a uniform deficit across all columns points at ingestion.
Separate late arrival from loss. Settlement lands after authorization by design, so re-measure the same day 24 and 48 hours later. A deficit that closes on the next load is lag, not a drop.
Reconcile against the independent source: tie the day's settled total to the settlement ledger or acquirer file. If the ledger has the money and the warehouse does not, the business number was never wrong.
Only after those four steps consider a business explanation, and then only if a segment cut produces a coherent story rather than a flat haircut spread evenly across every merchant and channel.
Close by splitting the alert: a freshness and completeness check on the settlement feed, separate from the volume alert, so the next occurrence is classified automatically.

Follow-up

How would you make the daily volume alert immune to settlement lag without also hiding a genuine drop?
What is the right way to restate a published number after a backfill lands?
Which check catches a partial load that is uniform across the day rather than concentrated in one window?

Suggested practice checklist

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

Day 1 — Metric anatomy

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

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

Day 2 — Diagnosing a drop without guessing

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

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

Day 3 — Should we build it

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

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

Day 4 — The places aggregate numbers lie

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

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

Day 5 — Technical maintenance, aimed at metrics

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

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

Day 6 — Turning engineering work into data science stories

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

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

Day 7 — Mock case and gap list

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

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

Getting an analysis acted on

Work that nobody used is a common and unflattering pattern in data careers, and interviewers probe for it. Have a story about an analysis that changed a decision, and be specific about how you got it in front of the person who could act. Also have one about work that went nowhere, with your reading of why.

Describe a time you had to optimize a complex query for a large dataset.

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
State the situation in two sentences and spend the rest on your reasoning.
Close with what you would do differently, concretely.

Follow-up

How did you know the outcome was caused by your change?
What did you decide not to do, and why?

Defend a vintage finding that contradicts the portfolio dashboard

Medium · vintage analysis · mix shift · stakeholder pushback

The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.

Approach

Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.

Follow-up

Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?

Turn a one-line fraud-number request into a scoped brief

Easy · scoping · metric definition · denominators

A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.

Approach

Establish the decision behind the question first, because a risk-rule change, a board number and a merchant contract negotiation need different denominators, and asking which one is not stalling.
Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
Commit to a default so the reply is useful even if nobody answers, for example net fraud loss in basis points of settled volume, attributed to the requested_at month, matured months only.
State the exclusions in the same breath as the default: non-fraud dispute categories, transaction months with less than 120 days of maturity, and first-party abuse that arrives coded as consumer_dispute.
Give a delivery time for the default and a longer one for the fuller cut, so the choice between them carries a visible cost.

Follow-up

They come back wanting it by merchant for a contract negotiation. What changes in the definition and in the maturity rule?
How would you separate first-party abuse from third-party fraud in this data, and what would you refuse to conclude from the split?