Interview guides · Progyny

Progyny Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

As a Data Scientist at Progyny, you play a pivotal role in harnessing data to drive strategic decision-making and improve the outcomes of our products and services. Your expertise in data analysis, statistical modeling, and machine learning significantly influences how we understand and meet the needs of our users, particularly in the context of reproductive health. In this dynamic role, you will collaborate with cross-functional teams to develop insights that shape our offerings and enhance user experiences.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

Separate authorization, settlement and dispute outcomes cleanly
Reconcile amounts in minor units and currency
Set fraud thresholds by expected cost

Overview

The impact of your work as a Data Scientist at Progyny extends beyond mere analytics; it directly influences product development, operational efficiency, and overall business strategy. You will engage with complex datasets, examining trends and patterns that inform critical decisions. Your contributions will be instrumental in advancing our mission to provide superior fertility and family-building benefits, making this position not only essential but also incredibly rewarding.

You can expect to work on diverse projects that span various aspects of our business—from analyzing user engagement metrics to developing predictive models that inform program improvements. This role offers the opportunity to tackle complex challenges, apply innovative solutions, and drive meaningful change in a rapidly evolving field.

The Progyny interview process

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

Recruiter Call

Most candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.

What to demonstrate

Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
The substance of the questions you ask back, which an experienced screener reads as a level signal

How to prepare

Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered

Technical Interview

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

Take-Home Assignment

The clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.

What to demonstrate

Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived

How to prepare

Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight

Final Technical Interviews

A loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.

What to demonstrate

Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience

How to prepare

Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story

Where candidates lose points

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

Using written premium as the denominator of a loss ratio

Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.

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

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

Extrapolating a first-week lift inflated by novelty effects

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

Questions & practice

Describe a machine learning project you have worked on and your specific contributions.

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Pick an evaluation metric that matches the cost of each error type, not a default.
Set a baseline first, so any model has something honest to beat.

Follow-up

What would you monitor after launch to know the model is still valid?
How would you choose the decision threshold, and who owns that choice?

Describe your process for building a predictive model from start to finish.

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Bootstrap a fraud loss rate that clusters within merchant

Medium · bootstrap · clustered resampling · heavy tails

You have a per-transaction frame with auth_id, merchant_id, settled_amount_reporting and net_loss_reporting, both already in one reporting currency. Most rows carry zero loss, a few carry large ones, and losses cluster within merchant. Using only numpy's random generator and no resampling helper from any library, write a bootstrap that returns a 95 percent interval for net fraud loss in basis points of settled volume, resampling merchants with replacement and taking all rows belonging to each drawn merchant. Also produce the naive row-level interval and state which you would report.

Approach

State the estimator before writing it: total net loss divided by total settled volume, times 10,000. It is a ratio of sums, so each replicate recomputes both sums. Averaging per-transaction loss rates instead would weight a five-unit transaction like a five-thousand-unit one.
Pre-aggregate loss and volume to merchant level once. For a ratio of sums, drawing merchants and taking all their rows is arithmetically identical to drawing merchant-level (loss_sum, volume_sum) pairs, so a replicate becomes one integer draw plus two vectorised sums rather than a groupby inside the loop.
Draw B replicates of M merchant indices with replacement, where M is the observed merchant count, compute the ratio per replicate, and take the 2.5th and 97.5th percentiles. Say explicitly that this is a percentile interval and that BCa would correct the skew-induced bias if the decision is close.
Repeat with independent row draws for the naive interval and compare widths on the same replicate count.
Report the clustered interval. Rows within a merchant share an acceptance profile, a category code and a fraud exposure, so they are not independent, and the row-level interval understates variance by roughly the design effect.

Worked solution (30 min)

Compute the point estimate directly on the full data and keep it for comparison.
Aggregate to merchant-level loss and volume arrays, record M, and set B to 2,000 with a seeded numpy Generator.
In a vectorised loop, draw integer indices of shape (B, M), index both arrays, sum along axis 1, and take the ratio times 10,000.
Repeat for the row-level version using the per-transaction arrays and N draws.
Take the 2.5 and 97.5 percentiles of each replicate array and report both intervals alongside the point estimate.

Expected result: Two intervals in basis points sharing the same point estimate. The clustered interval is materially wider, commonly two to four times, and is right-skewed because a heavy merchant can be drawn more than once in a replicate.

Verify it yourself

The point estimate equals the direct ratio on the full data and lies inside both intervals.
A synthetic input where every merchant holds exactly one transaction makes the clustered and naive intervals coincide; this is the test that the resampling unit is wired correctly.
Raising B from 2,000 to 10,000 shifts the endpoints by less than the reporting precision; if it does not, a handful of merchants is driving the tail and the estimate needs that caveat.
Replicate ratios never fall outside the range implied by drawing only the lowest-loss or only the highest-loss merchants.

Follow-up

Your clustered interval is three times wider. How do you explain that to someone who wanted a tighter number?
One merchant accounts for 40 percent of losses. What does that do to the interval, and what would you do about it?
How does this change if the question is whether two months differ rather than what this month's rate is?

How would you optimize a slow-running SQL query?

Medium · sql and data manipulation

Approach

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

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 do you approach debugging a piece of code?

Medium · sql and data manipulation

Approach

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

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?

Collapse merchant retry chains before measuring authorization approval

Medium · window functions · gaps and islands · sessionisation

Merchant retry logic turns one declined purchase into several rows in fct_payment_authorization. Collapse attempts into chains: inside a partition of (card_token_id, merchant_id, amount_minor), consecutive rows whose requested_at gap is 15 minutes or less belong to the same chain. Return one row per chain with chain_started_at, attempt_count, amount_minor, channel, and chain_outcome set to 'approved' when any attempt in the chain was approved. Then report a 7-day rolling dollar-weighted approval rate by channel over chains rather than attempts. Exclude is_reversal rows.

Approach

In a CTE, take LAG(requested_at) OVER (PARTITION BY card_token_id, merchant_id, amount_minor ORDER BY requested_at) and set is_new_chain when the previous value is null or the gap exceeds 15 minutes.
Turn the flag into a chain identifier with SUM(is_new_chain::int) OVER (same partition, same order, rows unbounded preceding to current row), which is the standard gaps-and-islands construction.
Aggregate to one row per chain: MIN(requested_at), COUNT(*), and BOOL_OR(auth_result = 'approved') as the chain outcome, because the purchase succeeded if any attempt in the chain did.
Roll the chains to a daily grain per channel, then apply the rolling window as PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW. An interval-offset RANGE frame is value-based: it admits every row in the partition whose chain_date falls in the closed window [D - 6 days, D], so a channel with no chains on some dates still gets the right seven-day sums — sparsity is exactly what this construct tolerates. ROWS BETWEEN 6 PRECEDING AND CURRENT ROW is the one that needs a dense grain, because it counts rows and on a sparse grain reaches back further than seven days.
Left-join a dense date spine only if the output needs a row on days with no chains. That is a presentation requirement, not a correctness one, and it leaves every rolling sum unchanged. The RANGE frame's preconditions are that chain_date is a date or timestamp (an interval offset needs an ordering column it can be added to) and that the aggregation left one row per (channel, chain_date).
Divide approved chain value by total chain value inside the rolling frame, never the average of daily rates, since averaging rates weights a quiet day the same as a heavy one.

Worked solution (40 min)

CTE 1: filter out is_reversal rows, then compute prev_requested_at with LAG and the is_new_chain flag.
CTE 2: build chain_id with a running SUM of the flag over the same partition and order.
CTE 3: group by partition keys plus chain_id to emit chain_started_at, attempt_count, amount_minor, channel and chain_outcome.
CTE 4: aggregate chains to (chain_date, channel) with approved value and total value. Add the date spine here only if the report must show empty days.
Final SELECT: two rolling SUMs over PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW, divided at the end.

Expected result: A rolling series with one row per date and channel, where the chain count is strictly below the attempt count on ecommerce and recurring, and the chain-level approval rate is at or above the attempt-level rate for every channel, since collapsing removes failed attempts that later succeeded.

Verify it yourself

SUM(attempt_count) over all chains equals the filtered attempt row count exactly; if it does not, the chain identifier is not partitioned the same way as the LAG.
Every chain has exactly one row whose is_new_chain flag is true, so COUNT(DISTINCT chain_id) equals the number of flagged rows.
A chain whose attempt_count is 1 must have chain_outcome equal to that single row's approved status.
The rolling denominator on day D equals the sum of the daily denominators over every date present between D-6 and D inclusive — seven terms on a dense grain, fewer on a sparse one, and the same total either way.
Adding or removing the date spine leaves every rolling sum identical, because spine rows contribute zero. Swapping RANGE for ROWS changes them on any channel whose grain has a gap, which is the direct test of which construct tolerates sparsity.

Follow-up

This definition chains off the previous attempt, so a chain can span an hour if attempts arrive every 14 minutes. When is that right, and when would you anchor the window to the first attempt instead?
Two genuine purchases of the same amount at the same merchant, four minutes apart, collapse into one chain. How large is that error and how would you bound it?
A channel goes dark for three days mid-window. Which of the two frame types would silently widen, and what would the resulting rate look like?
What changes if the merchant retries with a slightly different amount to dodge a velocity rule?

Given a dataset, how would you identify potential outliers?

Medium · product sense and metrics

Approach

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.
Restate the decision this analysis has to support, and who acts on the answer.

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?

If tasked with improving a product's user engagement, what data would you analyze?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

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

How do you prioritize your tasks when working on multiple projects?

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.
Restate the decision this analysis has to support, and who acts on the answer.

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?

Walk us through how you would solve a business problem using data analysis.

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
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.

Follow-up

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

How do you ensure the reproducibility of your analyses?

Medium

Approach

State your assumptions explicitly before working the problem.
Work from the decision backwards to the evidence you would need.
Clarify what is being asked and what a complete answer would contain.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Price a false decline when the label does not exist

Hard · proxy metrics · selection bias · randomised holdout

Your expected-cost threshold needs a figure for what a false decline costs beyond the margin on the blocked transaction. fct_payment_authorization records the decline, nothing records what the customer did next elsewhere, and declined transactions never produce a fraud outcome, so neither side of the error is directly observable. Propose the measurement: the proxy you would build from the tables you have, the design that yields an unbiased estimate for at least part of the score range, the bias in each, and the single sentence you would attach to the number when it reaches a pricing decision.

Approach

Write down what is unobservable and why. The counterfactual spend of a customer who was not declined, and the fraud label on any transaction the rule blocked, are both missing because of the decision itself. Missingness that depends on the decision is not fixed by matching on observed covariates.
Build the observational proxy anyway and be specific. For customers receiving a first risk-rule decline in a window, compare settled volume and active status over the following 30 and 90 days against customers matched on pre-period settled volume, tenure, segment and channel mix who attempted a comparable transaction and were approved. The bias runs one way: matching conditions on having attempted something that scored near the cutoff, and part of the declined group are genuine fraudsters whose disappearance is a saving rather than a loss, so the estimate overstates the damage.
Buy one unbiased local estimate. Hold a small random share of authorizations inside a defined risk_score band out of the decline rule and approve them, sizing the sample in advance from the expected fraud rate in that band so the cost of the experiment is known before it runs. The result is unbiased for that band only, and it is simultaneously the only source of fraud labels in the declined region.
Bound the extrapolation instead of hiding it. Run the holdout in two or three adjacent bands and report the spread. If the effect is flat across bands a constant is defensible; if it is steep, quote band-specific figures and decline to supply a single number.
Handle the window. Attrition after a decline can resolve over months, so a 90-day window truncates it and the randomised estimate is a lower bound on long-run damage at the same time as the observational version is an upper bound. Stating both directions is what makes the number safe to use.
Write the sentence that travels with the number: what it is (an estimate from a randomised holdout in one score band over a 90-day window), what it is not (a measurement anywhere else on the score range), and which way it is likely to be wrong.

Worked solution (40 min)

Define the decline cohort and the matched comparison cohort precisely, including matching variables and the pre-period window, and produce the 30-day and 90-day settled-volume difference.
Decompose the declined cohort into customers who never transact again and customers who transact less, since fraudsters concentrate in the first group and that split tells you how much of the estimate is contamination.
Write the holdout design: score band, sample share, expected fraud rate in band, expected cost of running it, and the run length needed to detect an effect large enough to change the cutoff.
Recompute p* = C_FP / (C_FP + C_FN) at the top and bottom of your estimated cost range and state whether the range changes the cutoff you would set.
Write the one-sentence caveat that will be quoted alongside the number in the pricing decision.

Expected result: Two numbers with opposite biases, an observational estimate labelled as an upper bound and a randomised in-band estimate labelled unbiased for that band only, plus a sensitivity table showing how p* moves across the range and an explicit statement of whether the residual uncertainty changes the decision.

Verify it yourself

If the cost range moves p* by less than the granularity at which the rule can actually be set, further measurement is not worth buying and that is the finding.
The observational estimate should exceed the randomised one in magnitude. If it does not, the matching is capturing something other than the effect of the decline and needs re-examining.
Removing customers with zero prior settled volume from both cohorts should shrink the observational estimate, since new-account fraud concentrates there.

Follow-up

Compliance and finance both object to deliberately approving transactions you believe are fraudulent. What is your answer, and how do you size the holdout?
Your interval spans the decision boundary. What do you recommend?
How would you detect that this number has gone stale?

Approval rate fell but approved value did not

Easy · approval rate · retry chains · denominators

Over ten days the count-weighted 7-day approval rate on fct_payment_authorization fell from 91 to 86 percent, while captured value in the reporting currency is flat. Available columns: auth_id, card_token_id, merchant_id, mcc, channel, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, risk_score, is_reversal, parent_auth_id, captured_at, captured_amount_minor, issuer_country. In twenty minutes, decide whether approved value is actually at risk, and hand back a corrected rate together with its denominator and every exclusion written down.

Approach

Restate the denominator before querying anything. The current one counts every row, so reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and zero-amount verification attempts (amount_minor = 0) are all sitting in it.
Chart numerator and denominator separately by day. If approved counts are flat and total attempts rose, the rate moved because the denominator grew, which is a different investigation from a rule change and points at a different owner.
Group declines by decline_reason_code and merchant_id. Retry-driven inflation concentrates in a few soft decline codes at a few merchants; a genuine policy change spreads across merchants within one code family.
Collapse retry chains: partition by (card_token_id, merchant_id, amount_minor), keep one attempt per 15-minute window taking the best outcome, and recompute both count-weighted and dollar-weighted rates on the collapsed set.
Convert amount_minor to one reporting currency using each currency's ISO 4217 exponent before any dollar weighting, then cut by channel and issuer_country to confirm nothing is hiding underneath a flat total.
Report both rates side by side with the exclusion list attached, and state which definition the alert should have been built on.

Follow-up

How would you pick the retry-collapsing window when merchants retry on different schedules?
Flat captured value could itself be hiding a mix shift. How do you rule that out?
What monitor would have caught denominator inflation on the day it started, rather than ten days later?

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

Repeat the depth protocol on the second-ranked area with the same six-problem structure.
Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

Re-run the six prompts from day one under the same clock and compare both correctness and time.
Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Describe a time when you had to communicate complex data findings to a non-technical audience.

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

What would you do differently if you ran that project again?
What did you decide not to do, and why?

Retract a published number after finding a currency bug

Medium · error disclosure · minor units · process repair

Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

Approach

Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.

Follow-up

The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
What automated check would have caught this, where would it live, and what would it cost in false alarms?

Recommend a decision whose true outcome matures a year later

Hard · decision under censoring · leading indicators · staged rollout

An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.

Approach

Fix the readout before the rollout, because a readout chosen after the data arrives is a story rather than a decision rule: indicator, window, threshold and reversal condition all go in writing first.
Choose the leading indicator on its measured relationship to the matured outcome in historical vintages rather than on availability. Early delinquency, typically the share reaching dpd_1_29 or missing a first scheduled payment by months_on_book 3, is the usual candidate, and you quantify how well it predicted the 12-month rate across past cohorts.
State the bias and its direction plainly: early delinquency under-represents default that emerges later and is contaminated by servicing and payment-date effects, so treat it as a floor on risk rather than an estimate of it.
Buy identification where full randomisation is unavailable: a narrow randomised approval band around the cutoff, or a staged rollout by channel or region read as a difference-in-differences, with the parallel-trends assumption stated and checked in the pre-period rather than assumed.
Give the executive the binary they asked for with the trigger attached in the same sentence: yes, conditional on the month-3 indicator staying inside a stated band, with an automatic hold if it breaches.

Follow-up

How would you validate that the month-3 indicator predicts the 12-month outcome, and what evidence would invalidate it mid-rollout?
Compliance refuses a randomised band. What is your next-best identification strategy, and what precision do you lose by taking it?