Interview guides · American Express

American Express Data Scientist Interview Questions & Guide 2026

Diagnose a metric move for American Express data scientist prep: split by segment, rule out a logging change, separate mix shift from behaviour.

The 60-second brief

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

Allocate prep to your weakest link rather than your favourite topic. Of the three things that usually gate the outcome (SQL that is correct under messy joins, sound reasoning about experiments, and structured framing of an open-ended problem), candidates tend to over-invest in modelling theory and under-invest in framing.

Focus areas

Decompose expected loss into PD, LGD, EAD
Read vintage curves, not blended portfolio averages
Set fraud thresholds by expected cost

The American Express interview process

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

Recruiter Screening

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 Round

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

Case Study Rounds

This round runs as a working session, so part of what it decides is whether you are useful to think with. The interviewer will interrupt: a hint that the data you assumed does not exist, a challenge to your metric, a nudge toward a branch you skipped. Treating those as interference is the common failure. Reason out loud while your thinking is still provisional so there is something to react to, and when a redirect arrives, take it instead of defending the path you had already started down.

What to demonstrate

Whether your reasoning is audible while it is still unsettled, or only after you have privately decided
What you do with a hint: absorb it and adjust, or argue for the original route
Whether your clarifying questions have answers that would change your approach, as opposed to filling silence
Whether you can be wrong about something in the middle of the case and keep moving without restarting

How to prepare

Run practice cases with a partner instructed to interrupt twice: once to remove a data source you assumed existed, once to reject the metric you chose. Practise absorbing both without going back to the start.
Before each practice case, write down the clarifying questions you plan to ask, then check afterwards whether any answer actually changed what you did. Drop the ones that did not.
Explain an analysis you already know well to someone outside the field and have them stop you at every point where the reasoning jumped a step.

Behavioral Interview

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

What to demonstrate

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

How to prepare

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

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.

Averaging delinquency across a book that is growing

A loan three months old cannot be 90 days past due, so a portfolio with many recent originations reports a low blended 90+ rate purely from age mix. The blended rate falls fastest exactly when originations grow fastest, which is precisely when credit quality most needs watching, so the metric moves in the reassuring direction during the riskiest period. Only comparisons at equal months on book are valid, which is what a vintage or roll-rate view enforces.

SQL that silently fans out on a one-to-many join

State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.

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.

Questions & practice

How would you handle class imbalance when building a fraud detection model for credit card transactions?

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.
Check what information would not exist at prediction time, and exclude it.

Follow-up

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

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.

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.

Worked solution (45 min)

Sort by loan_id and as_of_month_end, shift to form (from_state, to_state) pairs, and flag pairs whose month gap is not exactly one.
For each loan's final row, assign the absorbing destination from charge_off_flag or prepaid_in_full_flag, and list loans that vanish with neither as an exception count to report.
Apply the restructure rule, then build the 7 by 7 count matrix with a cross-tabulation over ordered state categories and row-normalise it.
Assert row sums equal one and absorbing rows are the identity, then take the current month's bucket distribution as a row vector.
Multiply twelve times, report the charged_off component, and repeat from an all-current vector for comparison.

Expected result: A 7 by 7 row-stochastic matrix with identity rows for charged_off and prepaid, a current-to-current diagonal typically above 0.95, roll rates rising with bucket depth, and a 12-month projected charge-off share of a few percentage points from an all-current start and materially higher from the actual book.

Verify it yourself

Every row sums to one within floating-point tolerance, and both absorbing rows are exactly the identity.
No mass sits in cells that skip a bucket upward, such as dpd_1_29 to dpd_60_89 in one month; if there is, the pairs are not actually consecutive months.
The projection from an all-current start is strictly below the projection from the real book distribution.
Backtest by estimating on months up to t minus 12 and comparing the projected distribution with the realised one at t; a systematic optimistic gap points at dropped exits or at seasoning, not at noise.

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?

How would you identify duplicate or anomalous transaction records in a large SQL database?

Medium · sql and data manipulation

Approach

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.
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 would you verify this result without re-running the same query?

Write a SQL query using window functions to calculate the rolling 3-month average spend for active cardmembers.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Accident-quarter loss ratio on earned rather than written premium

Hard · loss ratio · earned vs written · development age

From fct_policy_period_monthly, compute the accident-quarter loss ratio by product_line: incurred losses, being paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor, over earned_premium_minor for the same accident quarter. State explicitly whether loss_adjustment_expense_minor is included and apply that choice consistently. Also output the same ratio computed on written_premium_minor so the two can be compared. The table holds current values with no valuation-date snapshot. Say in one line which comparison this schema cannot support and what you would need to support it.

Approach

Derive the accident quarter from as_of_month with date_trunc, and note that the table already attributes losses to the month of the loss event while earning premium pro rata into the same month, which is what makes the two sides comparable at all.
Aggregate earned_premium_minor, written_premium_minor and the three loss components to product_line and accident quarter in one pass, keeping loss adjustment expense as its own column so the inclusion choice is a final-select decision rather than something buried in a CTE.
Compute both ratios side by side and a third column for their difference, because the size and sign of that difference is a direct read on whether the book grew or shrank in the quarter.
State the limitation plainly: every row carries today's reserve estimate, so each accident quarter is observed at a different development age and a cross-quarter comparison mixes development with underwriting. A fixed development age needs a valuation-date dimension, that is one row per accident period per valuation, which this table does not have.
Guard against the mirror-image error on the numerator by confirming ibnr_reserve_minor is non-zero on recent quarters; if it is null or zero there, the recent periods are understated twice over and the series is not usable.

Worked solution (40 min)

CTE quarterly: group fct_policy_period_monthly by product_line and date_trunc('quarter', as_of_month), summing earned_premium_minor, written_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor.
Final SELECT: build incurred_minor as the three loss components plus the LAE column, with the LAE inclusion written as a named expression so the choice is visible on the page.
Emit loss_ratio_earned and loss_ratio_written, both cast to numeric, plus their difference and the written-to-earned premium ratio.
Order by product_line and accident quarter, and append the one-line note about the missing valuation dimension to the query as a comment.

Expected result: One row per product_line and accident quarter with earned and written premium, incurred losses, both ratios and their difference. In quarters where written premium exceeds earned, loss_ratio_written is strictly below loss_ratio_earned, and the gap widens with the growth rate.

Verify it yourself

For a mature product line with flat volume, summed earned premium over a full policy term is close to summed written premium, so the two ratios converge there.
The sign of loss_ratio_earned minus loss_ratio_written matches the sign of written premium minus earned premium in every quarter, which is an arithmetic identity and not a data finding.
Recent accident quarters have non-zero ibnr_reserve_minor; a zero there means the numerator is missing its tail and the ratio is understated.
Within a quarter, loss_ratio_earned divided by loss_ratio_written equals written_premium_minor divided by earned_premium_minor exactly, because both ratios share one numerator. Any deviation means the LAE term was built into one ratio and not the other, which is the real test that the inclusion choice was applied consistently.
Recomputing with LAE excluded scales each quarter's ratio by incurred_minor / (incurred_minor + lae_minor). That factor is not constant across quarters unless LAE happens to be booked as a fixed fraction of incurred losses, so a uniform shift is evidence about how LAE is loaded, not evidence that the query is self-consistent — use the previous check for that.

Follow-up

Written premium exceeds earned premium by 18 percent this quarter and by 3 percent two years ago. What happened to the book, and what does it do to each ratio?
How would you build a development triangle from a valuation-dated version of this table, and what would you use the chain-ladder factors for?
Statutory presentation conventionally takes the expense ratio on written premium while the loss ratio uses earned. How do you avoid a combined ratio that quietly mixes the two bases?

What metrics would you prioritize when evaluating a credit risk model, and why might accuracy be misleading?

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?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you balance risk mitigation with customer friction when designing digital onboarding metrics?

Medium · product sense and metrics

Approach

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.
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?
Which segment would you cut first, and what would that rule out?

A new customer onboarding feature was launched, and sign-ups increased, but activation decreased. How would you analyze this metric drop?

Medium · product sense and metrics

Approach

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.
Name one primary metric, then the guardrail that stops it being gamed.

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 would you investigate a sudden 15% drop in transaction activation rates among new cardholders?

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?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you determine statistical significance when dealing with highly skewed spending data in financial experiments?

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
Say whether units interfere with each other, and switch design if they do.
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?

How would you design an A/B test to evaluate the effectiveness of a new rewards structure for premium cardholders?

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
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

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?

Portfolio delinquency improving while the loan book doubles

Easy · vintage analysis · age mix · right censoring

The blended 90-plus days-past-due rate across fct_loan_performance_monthly fell from 3.1 to 2.2 percent over two quarters while monthly funded volume roughly doubled. Credit leadership wants to know whether underwriting improved. Columns: loan_id, as_of_month_end, origination_month, months_on_book, original_principal_minor, principal_balance_minor, days_past_due, delinquency_bucket, restructured_flag, charge_off_flag, charge_off_date. Produce the view that answers the question honestly, and state in one sentence what the blended rate can and cannot tell you.

Approach

Name the mechanical floor first. A first instalment falls due roughly a month after funding, so a loan cannot reach dpd_90_plus until around its fourth month on book. Every recent origination therefore enters the denominator with a numerator that is structurally zero.
Build a vintage table: rows origination_month, columns months_on_book, cell equal to the share of that cohort whose worst days_past_due reached 90 or more, or whose charge_off_flag became true, at or before that age.
Use each loan's worst state to date rather than its current bucket, and take the pre-restructure worst state, because restructuring resets days_past_due and would otherwise read as a cure.
Compare cohorts only at equal months_on_book, and render cells beyond a cohort's current maturity as absent rather than zero, so the table cannot be misread left to right.
Decompose the blended move into an age-mix component and a within-age component, so the write-up states how much of the 0.9 point improvement is arithmetic rather than asserting it.

Follow-up

What does the diagonal of a vintage table represent, and when is reading it the right thing to do?
How would a change in charge-off timing policy show up in this table, and how would you separate it from credit quality?
Which single chart goes in front of the credit committee, and what do you say when someone asks for the blended series anyway?

Suggested practice checklist

For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.

Day 1 — Build a fixture you can check answers against

Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.

Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.

Day 2 — Joins, filters and NULL semantics

Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.

Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.

Day 3 — Window functions and frames

Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.

Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.

Day 4 — The four analytical query patterns

Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.

Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.

Day 5 — Write SQL the way you will have to write it live

Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.

Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.

Day 6 — One day for everything that is not SQL

Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.

Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.

Day 7 — Full loop rehearsal

Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.

Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.

The project you argued against

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

How do you handle skewed distributions when engineering numerical features for a predictive model?

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

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

Explain an incomplete dispute chart to a non-technical executive

Easy · dispute maturity · stakeholder communication · right-censoring

A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.

Approach

Lead with the mechanism in the listener's own terms, not with the statistical name for it: a dispute is attributed to the month the transaction happened, but it can be filed up to roughly 120 days later, so recent months contain only the disputes filed so far.
Show completeness rather than arguing about the rate: for each transaction month, plot the share of its eventual disputes already filed, estimated from months that are fully matured. The last three months will sit visibly below 100 percent.
Replace the chart with two artefacts: a matured series that stops 120 days back and is labelled final, and a development-factor estimate for the immature months drawn as a dashed range and labelled an estimate.
Hand over one sentence the executive can repeat without you in the room: the recent months look better because the disputes have not arrived yet, not because fewer will arrive.
Offer a weekly signal they can watch instead, such as the risk-score mix of approved volume or the decline-rule hit rate, and state up front what it does and does not predict.

Follow-up

The deck ships tomorrow regardless. What exactly goes on the slide, and what wording do you insist on?
How would you estimate the development factors, and how would you notice if they had shifted?

Allocate one analyst-week across three competing risk requests

Medium · prioritisation · decision deadlines · stakeholder negotiation

Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

Approach

Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.

Follow-up

The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?