Interview guides · Checkr

Checkr Data Scientist Interview Questions & Guide 2026

Checkr data scientist prep: probability, sampling distributions, and stating the assumption behind every test you run.

The 60-second brief

At Checkr, the Data Scientist role is at the intersection of high-stakes product development and mission-critical infrastructure. You are not just analyzing data; you are building the quantitative backbone that powers fair and transparent background checks for millions of users. Your work directly influences how Checkr scales its AI-driven verification systems, impacting everything from employment opportunities to housing access.

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

Report only matured cohorts for loss metrics
Read vintage curves, not blended portfolio averages
Reconcile amounts in minor units and currency

Overview

In this role, you will tackle complex, ambiguous problems that require both deep technical rigor and a strong business sense. Whether you are optimizing revenue models, driving growth in SMB markets, or refining the unit economics of Checkr’s product suite, your insights serve as the single source of truth for Checkr’s executive leadership, finance, and product teams. You will operate within a high-growth environment where your ability to translate raw data into actionable strategy is paramount.

The role is highly cross-functional. Expect to partner daily with Engineering, Product, RevOps, and Marketing. Your success depends as much on your ability to communicate complex findings to non-technical stakeholders as it does on your technical proficiency.

The Checkr interview process

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

Recruiter Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Deep-Dive

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

Take-Home Assignment

A take-home is graded as an argument, not as a notebook. Somebody reads the submission without you in the room, so every choice has to survive on the page: why the question was framed this way, and what was deliberately left out. The gap between a strong and a weak submission is almost never model quality. It is whether the writeup names the specific question it answers and commits to a recommendation, including what evidence would overturn it. A high-accuracy model attached to no conclusion reads as effort that stopped before the decision.

What to demonstrate

Whether the question you answered is stated outright, and whether it is the question the prompt posed rather than an easier neighbour of it
Whether the recommendation is specific enough to act on, with the uncertainty attached to it instead of parked in a caveats section at the end
Whether analytical choices such as the metric definition, the population filter and the time window are justified in the prose, not merely visible in code

How to prepare

Take a dataset you have already worked with, write the one-paragraph conclusion first, then check whether the analysis you were planning actually supports it and cut whatever does not
Practise stating a metric in one sentence that fixes the population, the time window and the denominator, then confirm your query computes exactly that sentence and nothing adjacent to it
Hand a draft to someone outside the problem and ask them to tell you back what you recommended and why; anything they cannot recover is not on the page yet

Live Case Study

Underneath the business framing, this round is usually asking whether you can turn a fuzzy goal into a quantity that could be computed from data such a business would plausibly hold. That means a metric with a stated numerator, denominator, eligibility rule and time window, plus an honest account of the conditions under which it would mislead you. Answers come apart when a candidate names a familiar metric and never defines it, because every follow-up then lands on an ambiguity that was left open and the candidate has to invent the definition under pressure.

What to demonstrate

Whether a named metric arrives with its denominator, eligibility rule and window attached rather than assumed
Whether the measure follows from the mechanism you proposed, or is a recognisable metric retrofitted to it afterwards
Whether you name a guardrail that would reveal the gain came from somewhere you did not want it to come from
Whether you can say what data the plan requires and what you would settle for if that logging were never implemented

How to prepare

Take five metrics you reach for by reflex and write each as one sentence containing numerator, denominator, eligibility rule and time window. The ones you cannot finish are the ones that will fail under follow-up.
For a product you use daily, write the measurement plan you would propose for a change to it: primary metric, one guardrail, the unit of analysis, and the table the numbers would come from.
Practise the substitution question. For three metrics you like, write what you would measure instead if the event you depend on were not being logged.

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.

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.

Building features from data that postdates the prediction time

Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.

Optimising accuracy on a heavily imbalanced target

State the base rate first, then choose the metric from the relative cost of a false positive against a false negative: precision and recall at the operating threshold, PR-AUC, or expected cost. At a 1 percent positive rate, predicting the majority class for everyone scores 99 percent accuracy and is worthless.

Questions & practice

How do you validate the performance of an NLP model in a production environment?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.
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?

How would you design a pricing model for a new product feature?

Medium · machine learning and modelling

Approach

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.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Can you explain the trade-offs between different machine learning models for a specific classification task?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Write integrity checks for the authorization and settlement lifecycle

Easy · data quality · minor units · fx reconciliation

You are given fct_payment_authorization as a pandas DataFrame with auth_id, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, is_reversal, parent_auth_id, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency and settlement_fx_rate. Write a function returning one row per integrity check with the check name, failing row count, failing share and up to five example auth_id values. Cover at least six checks, one of which reconciles captured_amount_minor against settlement_amount_minor through settlement_fx_rate. Partial capture, zero-amount verification and a decline with no capture are all legitimate and must not be flagged.

Approach

Separate contract violations from observations before writing any code: an approved row carrying a decline_reason_code is structurally impossible, while a capture two days after requested_at is merely slow and belongs in a different severity tier.
Express each check as a boolean mask over the whole frame and collect the masks in a dict, so the summary table is one comprehension over mask.sum() rather than a row loop.
For the reconciliation, leave minor units before comparing: expected = captured_amount_minor / 10exponent[transaction_currency] * settlement_fx_rate * 10exponent[settlement_currency]. Build the exponent table covering zero-decimal and three-decimal currencies instead of assuming two everywhere.
Guard the legitimate cases explicitly so each mask fires only on the genuine contradiction: captured_amount_minor below amount_minor is partial capture, amount_minor of zero on an approved row is account verification, a null captured_at on a declined row is correct.
Sort the output by failing share times a stated severity weight, because a check firing on 0.01 percent of rows can still be the one that breaks a ledger reconciliation.

Worked solution (25 min)

Assert auth_id is unique, then build a currency exponent lookup that includes the zero-decimal and three-decimal currencies present in the data.
Define masks for: approved with non-null decline_reason_code; declined with non-null captured_at; captured_amount_minor above amount_minor with parent_auth_id null; captured_at before requested_at; settled_at before captured_at; is_reversal true with parent_auth_id null; settlement_currency differing from transaction_currency while settlement_fx_rate is null.
Add the exponent-aware reconciliation mask with a tolerance of one minor unit plus a small relative term.
Assemble a frame of check_name, n_failing, pct_failing and up to five sample auth_id values, ordered by severity then share.
Read five flagged rows per check by hand and confirm each is genuinely contradictory before reporting any counts.

Expected result: A DataFrame of at least eight rows, one per check, each with n_failing, pct_failing and example auth_id values. The reconciliation check should fire on a whole currency at once rather than on scattered rows, because an exponent error is systematic while an FX error is not.

Verify it yourself

Every mask returns false on three hand-constructed legitimate rows: a partial capture, a zero-amount verification and a decline with no capture.
Each pct_failing uses the denominator that check actually applies to, with reversals and verifications excluded where they are out of scope.
The reconciliation residual is centred at zero with spread under one minor unit; residuals clustered at exactly 100x or 0.01x mean an exponent is wrong, not a rate.

Follow-up

Which of these would you run as a blocking pipeline assertion and which as a monitored metric, and why?
The FX check fails on 3 percent of rows, all in one settlement currency. How do you decide between a data bug and a rounding convention?
How would you detect that a currency's minor-unit exponent is wrong in your reference table, using only the transaction data?

Customers with no credit application, avoiding the NOT IN trap

Medium · null semantics · anti-join · scd type 2

Count current customers who have never submitted a credit application, broken out by segment. dim_customer is a slowly changing dimension type 2, so restrict to is_current = true, kyc_status = 'verified' and closed_at null. In fct_loan_application, customer_id is null for applicants who were not customers when they applied. Write the anti-join, return segment and customer_count, and state in one line what NOT IN (SELECT customer_id FROM fct_loan_application) returns against this table and why.

Approach

Pin the dimension to one row per customer first: is_current = true already guarantees that, but say so out loud, because forgetting it multiplies every count by the number of attribute versions a customer has accumulated.
Write the anti-join as NOT EXISTS with a correlated predicate on customer_id, which evaluates per row and is unaffected by nulls anywhere in the applications table.
Name the failure explicitly: NOT IN against a nullable column compares each candidate to a set containing NULL, the comparison yields UNKNOWN rather than TRUE, and the whole predicate is therefore never satisfied, so the query returns zero rows.
If NOT IN is required for some reason, add WHERE customer_id IS NOT NULL inside the subquery, which restores the intended semantics, and note that a LEFT JOIN with an IS NULL filter is equally safe.
Group by segment and sanity-check the total against the unfiltered current-customer count minus the count of distinct applying customers.

Follow-up

Rewrite it as a LEFT JOIN with IS NULL and say when you would prefer that form to NOT EXISTS.
How does the answer change if you want customers who never applied as of a historical date rather than today?
The applications table has 40,000 rows with a null customer_id. What are those rows, and are they a data quality problem or a product fact?

Count-weighted and dollar-weighted approval rates on one currency

Easy · aggregation · denominators · minor units

Using fct_payment_authorization, report the trailing 7-day authorization approval rate two ways for transaction_currency = 'EUR': count-weighted, and dollar-weighted on amount_minor. Exclude is_reversal = true, exclude incremental authorizations (parent_auth_id not null), and exclude zero-amount account verifications. auth_result = 'approved' is the numerator; the four declined_* values make up the rest of the denominator. Return channel, attempts, approved_attempts, approval_rate_count and approval_rate_value. State every exclusion and its reason before you write the SELECT.

Approach

Say the denominator out loud first: attempts on a single transaction currency, excluding reversals, incremental authorizations and zero-amount verifications, because none of those is a purchase attempt a merchant is trying to get approved.
Filter requested_at against a half-open interval (>= start AND < end) so the boundary day is neither dropped nor double counted.
Compute both rates in one pass with FILTER clauses: COUNT() FILTER (WHERE auth_result = 'approved') over COUNT(), and SUM(amount_minor) FILTER (WHERE auth_result = 'approved') over SUM(amount_minor).
Cast one side of each ratio to numeric before dividing, since amount_minor and the counts are integers and integer division silently truncates to zero.
Group by channel and sort by the value-weighted rate, then read the gap between the two rates as a statement about where the declines sit rather than as noise.

Worked solution (20 min)

Write the exclusion list as comments above the query: is_reversal = false, parent_auth_id is null, amount_minor > 0, transaction_currency = 'EUR'.
Build a single aggregate query over fct_payment_authorization with a half-open requested_at predicate and those four filters.
Emit attempts, approved_attempts, approval_rate_count and approval_rate_value with FILTER clauses and a numeric cast on the numerator.
Group by channel, order by approval_rate_value ascending so the worst channel is on top.

Expected result: One row per channel present in the window, with attempts and approved_attempts as integers and both rates as numerics strictly between 0 and 1. approval_rate_value is typically lower than approval_rate_count on ecommerce and moto, because declines concentrate in higher-value card-not-present traffic.

Verify it yourself

SUM(attempts) across channels equals a separate COUNT(*) run with the same four filters and no GROUP BY.
Dropping the amount_minor > 0 filter raises attempts and moves approval_rate_count while leaving approval_rate_value unchanged, which confirms the zero-amount rows were only ever affecting the count-weighted figure.
Both rates are strictly less than 1 unless a channel had no declines at all in the window.

Follow-up

The two rates diverge by four points on the ecommerce channel but agree on card_present. What does that tell you, and what would you cut next?
How would you extend this to all currencies without summing amount_minor across them?
Which of the four decline reasons belong in the denominator of a rate you would put in front of a risk team, and which are really the network's problem?

How do you prioritize data science projects when faced with competing requests from Product and Finance?

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

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?

What statistical methods do you use to determine the significance of an A/B test result?

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
Decide the analysis before seeing data, including how long it runs and when you look.
Say whether units interfere with each other, and switch design if they do.

Follow-up

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

If our conversion rate dropped by 5% overnight, how would you investigate the root cause?

Medium

Approach

State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Clarify what is being asked and what a complete answer would contain.

Follow-up

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

A dispute rate that does not fall at the right edge

Medium · right-censoring · development factors · cohort maturity

Your chargeback chart slopes downward over the last four months every time it is refreshed. It is built from fct_card_dispute counted by opened_at, divided by settled transactions in the same month. Using auth_id, requested_at, dispute_stage, opened_at, network_reason_code and outcome, explain what the chart is actually showing, then specify the metric you would publish: attribution, denominator, maturity rule, and how immature periods appear on the chart. State the condition under which you would publish a development-factor estimate instead of a blank.

Approach

Separate the two dates. A dispute belongs to the month of the transaction that caused it, which is requested_at on the linked authorization, not to the month the case was filed. Counting by opened_at mixes old transactions into new months and makes the series unreadable in both directions at once.
Once attribution is fixed, name the censoring. Recent transaction months have not had time to accumulate their disputes, so the right edge falls by construction. The slope will still be there next month, one month further right, which is the diagnostic that distinguishes it from a real improvement.
Set the maturity threshold empirically rather than by folklore. From completed transaction months, compute the share of the final dispute count filed by 30, 60, 90, 120 and 180 days, split by reason-code family. Consumer dispute rights commonly run around 120 days from the transaction or the expected delivery date, but several reason codes run materially longer, so one global cut-off under-covers the slow families.
Specify the published metric: disputes reaching dispute_stage 'first_chargeback' or later, attributed to the transaction month, over settled transactions in that same month, with only months past the measured threshold shown as final.
Decide how immature months appear. Either omit them, or show a chain-ladder style estimate with an interval in visibly different styling, never as a plain point on the same line as final months. Publish the estimate only when the development pattern is stable across recent completed cohorts; if the reason-code mix or the dispute process has shifted, the factors are estimated on a pattern that no longer applies.
Note that the count series and the value series mature at different speeds, since net_loss_minor is signed and known only after the case resolves, so a loss-value chart is stale for longer than a case-count chart of the same data.

Worked solution (30 min)

Join fct_card_dispute to fct_payment_authorization on auth_id, rebuild the series by requested_at month, and plot both versions together.
For transaction months older than twelve months, compute the cumulative share of final disputes filed within 30, 60, 90, 120 and 180 days, split into fraud and non-fraud categories.
Choose the maturity threshold as the age at which cumulative share reaches a stated coverage level, for example 95 percent, for the slowest family you intend to report, and write that level into the definition.
Recompute the last twelve months, marking anything below the threshold as incomplete, and write one line saying what the chart no longer claims.

Expected result: Two series over the same data, one by opened_at and one by transaction month, plus a maturity table of cumulative filing share by age and reason-code family from which a single threshold is chosen and justified, with the most recent months marked incomplete rather than plotted low.

Verify it yourself

Cumulative filing share is non-decreasing in age. If it falls anywhere, cases are being re-dated or re-staged and the stage history log needs checking.
A mature transaction month's rate does not move when the query is re-run a month later, while an immature one rises. If a month you declared mature still moves materially, the threshold is too short.
Fraud-category disputes reach the coverage level at a younger age than delivery-related consumer disputes, so a threshold calibrated on the fast family leaves the right edge still sloping.

Follow-up

Your development factors were estimated over a period containing an authentication policy change. What do you do?
How do you present a month that is 60 days mature to an audience that wants a single number?
Would you attribute a dispute to the authorization month or the settlement month, and when does the difference matter?

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

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

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 model that was performing poorly in terms of latency or accuracy.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

How do you communicate complex technical trade-offs to a non-technical stakeholder, such as a Product Manager?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Quantify the outcome, including what you would not claim credit for.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

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

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?