Interview guides · BlackLocus

BlackLocus Data Scientist Interview Questions & Guide 2026

Data scientist prep for BlackLocus: practice defining a payments and lending metric, picking its guardrails and defending the tradeoff.

The 60-second brief

As a Data Scientist at BlackLocus, you play a vital role in leveraging data to drive strategic decisions and enhance product offerings. Your work directly influences the development and optimization of pricing models, inventory management, and competitive analysis, impacting both the company's bottom line and the experience of users. The complexity and scale of the challenges you tackle, combined with the collaborative nature of the team, make this role not only critical but also intellectually rewarding.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

Focus areas

Report only matured cohorts for loss metrics
Reconcile amounts in minor units and currency
Set fraud thresholds by expected cost

Overview

In this position, you will collaborate closely with product managers and engineers, utilizing cutting-edge methodologies in statistics and machine learning. Your contributions will help shape the direction of innovative solutions that address real-world problems faced by retailers and businesses. The opportunity to work with diverse data sets and the emphasis on data-driven decision-making ensure that your role is both challenging and impactful.

The BlackLocus interview process

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

Phone Screen

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 Assessment

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Onsite Interview

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Where candidates lose points

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

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

Recalibrating an underwriting cutoff on approved and funded applicants only

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

Defining the cohort on a post-treatment condition

Ask how rows entered the table. Filtering on something that treatment itself influences, such as users who finished onboarding or accounts still active at ninety days, breaks comparability between arms; define the population at an entry point that precedes exposure and keep everyone in it.

Accepting a metric definition without asking about the denominator

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

Questions & practice

Write a function to calculate the mean and standard deviation of a list of numbers.

Medium · statistics and probability

Approach

Say what the estimate is of, and over what population it generalises.
Sanity-check the answer against a simple bound or a simulated case.
Translate the result into the decision it informs, in one plain sentence.

Follow-up

Which assumption here is most likely to be violated in practice?
What sample size would you need to detect an effect half this size?

What are some common metrics used to evaluate model performance?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
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.

Follow-up

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

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?

How would you optimize a slow-running query in SQL?

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Check whether any join is one-to-many before aggregating, or the sums inflate.
State the window function and its partition and ordering out loud before writing it.

Follow-up

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

Given a dataset, write code to generate visualizations.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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

What breaks if events arrive late or out of order?
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?

How would you communicate your findings to a non-technical audience?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

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?

What approach would you take to optimize a recommendation system?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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?

Given a dataset, how would you identify trends and anomalies?

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.
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 stay current with trends and advancements in data science?

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 would you design an experiment to test a new pricing strategy?

Medium · a/b testing and experimentation

Approach

Say whether units interfere with each other, and switch design if they do.
Name the randomisation unit first; it decides the variance and what the test can detect.
State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

How do you approach feature selection?

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?

Monitor a rollout daily without inflating false positives

Medium · peeking · sequential testing · alpha spending

A risk-rule change is ramped to 50 percent and the team reads the dashboard every morning for twenty business days, intending to stop the first time the two-sided p-value on the count-weighted approval rate falls below 0.05. Quantify how much that inflates the false positive rate, propose a monitoring scheme that still permits an early stop for harm, and explain what dispute maturity does to the matured fraud basis points guardrail when it is read on day twenty.

Approach

Quantify rather than assert. Repeatedly applying a fixed-horizon test at nominal two-sided 0.05 inflates the family-wise type I error to roughly 8 percent at 2 looks, 14 percent at 5, 19 percent at 10 and about 25 percent at 20 equally spaced looks. Under continuous monitoring with no stopping rule the probability of crossing at some point tends to 1.
Pick a scheme matched to how the team actually behaves. If looks are on a fixed schedule, use a group-sequential design with an alpha-spending function: O'Brien-Fleming spends almost nothing early so the final boundary stays near nominal, which suits a team that mostly wants to ship at the end; Pocock spends evenly and buys genuine early-stopping power at the cost of a stricter final boundary. If looks are truly continuous and ad hoc, use an always-valid confidence sequence such as a mixture sequential probability ratio test, which is valid at every moment in exchange for a larger fixed-horizon sample at equal power.
Treat the harm stop as a separate, asymmetric decision. Stopping a change because it looks harmful costs one abandoned experiment; failing to stop costs real loss every day it runs. Run the guardrail as a one-sided monitor at a looser alpha with its own pre-registered stop rule, and do not spend the primary metric's alpha budget on it.
Fix the schedule before launch: the number of looks, their timing, the boundaries, and the maximum sample. Boundaries computed after the fact from however many times someone opened the dashboard are not a correction.
Separate peeking from immaturity on the fraud guardrail. Disputes on a transaction can be filed for roughly 120 days, and some reason codes run longer, so a day-twenty read covers transactions with at most twenty days of dispute exposure. The number is not low, it is incomplete. Either report only matured transaction months, or apply development factors estimated from completed months and label the result an estimate with its interval.

Worked solution (25 min)

Quote the inflation for the stated plan: twenty looks at nominal two-sided 0.05 gives a family-wise type I error of about 25 percent, so one rule change in four would appear significant with no true effect.
Specify the replacement: five pre-scheduled looks at 20, 40, 60, 80 and 100 percent of planned sample under an O'Brien-Fleming spending function, with z boundaries of approximately 4.56, 3.23, 2.63, 2.28 and 2.04. For comparison, a Pocock design at five looks uses a constant boundary of about 2.41.
Add a one-sided harm monitor on the fraud guardrail and the decline-rate guardrail with its own alpha and its own pre-registered rule, documented before launch.
For the fraud read, restrict to transaction months with at least 120 days of maturity; if none exist yet, present a development-factor estimate from completed months with its uncertainty and mark the recent months incomplete on the chart rather than plotting them as low.
If the boundary is crossed early, report a bias-adjusted effect estimate, because the estimate at a stopping boundary is systematically larger in magnitude than the truth.

Expected result: Twenty daily looks at nominal 0.05 give a family-wise type I error of roughly 25 percent. The recommendation is a pre-registered five-look O'Brien-Fleming group-sequential design with a final z boundary near 2.04, or an always-valid confidence sequence if looks cannot be scheduled, plus a separate one-sided harm monitor. The day-twenty fraud guardrail is reported as incomplete, not as a pass.

Verify it yourself

Simulate the stated stopping rule under the null a few thousand times; the crossing rate should reproduce the quoted inflation, and under the group-sequential boundaries it should land near 5 percent.
The alpha spent across the planned looks under the chosen spending function must sum to the total alpha; if it does not, the boundaries were not computed from a spending function.
Plot the fraud rate by transaction month against dispute maturity. If the curve slopes down at the right edge on historical data where nothing changed, that slope is maturity, not effect.
Compare the effect estimate at the crossing look with the estimate at the planned maximum sample on historical tests; the gap is the stopping bias you owe the sponsor.

Follow-up

Under an O'Brien-Fleming boundary you cross on day three. What do you say about the effect size, and why is the naive point estimate biased?
How would you set the stopping rule for the fraud guardrail given that its true value is not observable inside the test window?
The team argues that they are only looking, not deciding, so peeking is harmless. Under what precise condition is that true, and how would you verify 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

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.

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.

What motivates you to excel in your work?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
State the situation in two sentences and spend the rest on your reasoning.
Quantify the outcome, including what you would not claim credit for.

Follow-up

What did you decide not to do, and why?
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?

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

Easy · scoping · metric definition · denominators

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

Approach

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

Follow-up

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