Interview guides · Citizens

Citizens Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

The Data Scientist role at Citizens is pivotal in transforming raw data into actionable insights that drive business decisions and enhance customer experiences. This position operates at the intersection of technology, analytics, and strategic thinking, leveraging data to influence various aspects of the company's products, services, and overall strategy. As a Data Scientist, you will contribute to projects that involve predictive modeling, statistical analysis, and machine learning, ultimately impacting how Citizens serves its customers and manages risk.

Scope your preparation by the data you would actually touch, because the title will not tell you. A seat that lives in event logs and weekly readouts rewards fluency in aggregation and metric definitions; a seat that owns a model in production rewards fluency in train/serve skew, retraining cadence and drift monitoring. The fastest way to find out which one you are interviewing for is to ask what the team shipped last quarter and what it gets paged about.

Focus areas

Decompose expected loss into PD, LGD, EAD
Set fraud thresholds by expected cost
Reconcile amounts in minor units and currency

Overview

In this role, you will collaborate with cross-functional teams, including engineering, product management, and risk assessments, to solve complex problems using data-driven methodologies. You will engage in tasks ranging from analyzing customer behavior to developing algorithms that optimize business processes. The work is both critical and challenging, requiring a blend of technical expertise and creative problem-solving skills, all while operating within a fast-paced environment that values innovation and customer focus.

The Citizens interview process

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

Recruiter Screening

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
Whether you name what you have not done instead of stretching to cover every line of the posting
Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

HireVue Interview

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

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

Interviews with Managers

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

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.

Reading the most recent months of fraud and dispute rates as final

Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.

Treating a non-significant result as proof of no effect

Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.

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

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

Questions & practice

Implement a function in Python to calculate the mean and standard deviation of a list of numbers.

Medium · statistics and probability

Approach

Sanity-check the answer against a simple bound or a simulated case.
Quantify uncertainty explicitly rather than reporting a point estimate alone.
Translate the result into the decision it informs, in one plain sentence.

Follow-up

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

What metrics would you use to evaluate the performance of a model?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Set a baseline first, so any model has something honest to beat.
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?

Explain a machine learning algorithm you have implemented in the past.

Medium · machine learning and modelling

Approach

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

Follow-up

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

Implement accident-quarter loss ratio at twelve months development

Medium · loss ratio · development age · earned premium

fct_policy_period_monthly arrives as a stack of month-end snapshots: each row carries valuation_month alongside as_of_month, policy_id, product_line, written_premium_minor, earned_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor. Compute the accident-quarter loss ratio at exactly 12 months of development: incurred losses over earned premium, both taken from rows whose as_of_month falls in the accident quarter, read from the snapshot 12 months after that quarter closes. Report quarters that cannot reach that age as incomplete rather than dropping them.

Approach

Derive accident_quarter from as_of_month, then define the evaluation snapshot per quarter as valuation_month equal to the quarter's final month plus twelve months. Every figure in the ratio comes from that one snapshot, not from whichever snapshot happens to be newest.
Numerator is paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor over the accident quarter's rows in that snapshot. Loss adjustment expense may be included or not, but the choice applies to every quarter and is named in an output column.
Denominator is earned_premium_minor over the same rows. Written premium is booked in full at inception, so in a growing book it runs ahead of earned premium and drags the ratio down, with the error reversing when the book shrinks.
Left-join the full quarter list against available valuation months so a quarter with no 12-month snapshot yields status incomplete and a null ratio, instead of disappearing and shortening the series without saying so.
Split by product_line, since both the loss ratio level and the speed of development differ by line, and a blended series moves with mix as much as with experience.

Worked solution (30 min)

Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
Filter rows to those where valuation_month equals the row's target_valuation, then assert each accident_quarter has exactly one distinct valuation_month left.
Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
Recompute one quarter by hand on a five-policy subset and confirm it matches.

Expected result: One row per accident_quarter and product_line with incurred, earned_premium, loss_ratio, an lae_included flag and status in complete or incomplete. Complete quarters end twelve months before the latest valuation_month, so the four or five most recent quarters carry null ratios.

Verify it yourself

Assert exactly one distinct valuation_month per accident quarter after filtering; more than one means the snapshot join fanned out.
For a growing book, earned premium in a quarter is below written premium booked in that quarter; if it is above, either the book is shrinking or the accident-quarter derivation is wrong.
Incomplete quarters carry null ratios and never appear on the same line as complete ones in any chart.
Swapping the denominator to written premium shifts a quarter by L(W - E)/(EW), so the sign of the change is the sign of written minus earned premium in that quarter: the ratio falls where the book grew and rises where it shrank. Verify the swap per quarter against sign(W - E) rather than expecting a uniform move; a series that moves in one direction everywhere only means the book grew in every quarter.

Follow-up

The most recent complete quarter came in four points better than the one before. What do you check before calling it an improvement?
How would you estimate the 12-month figure for a quarter that is only 6 months developed, and how would you label the estimate?
Why can an expense ratio legitimately use a different denominator from the loss ratio in the same presentation?

Write a SQL query to find the top five customers by transaction volume.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Reconcile captured authorizations against the daily settlement total

Easy · reconciliation · currency · rounding

fct_payment_authorization holds captured_amount_minor in transaction_currency, and settlement_amount_minor in settlement_currency with settlement_fx_rate applied at settlement rather than at authorization. The rate is quoted in major units of settlement_currency per major unit of transaction_currency, and dim_currency.minor_unit_exponent carries the ISO 4217 exponent for each code (0, 2 or 3 depending on the currency). Produce a daily reconciliation: for each settled_at date and settlement_currency, return settled_count, total settlement_amount_minor, and the sum of captured_amount_minor converted into settlement minor units. Flag any date and currency pair whose two totals differ by more than one minor unit per settled authorization. Do not sum amounts across currencies anywhere in the output.

Approach

Restrict to rows that actually settled: settled_at is not null and settlement_amount_minor is not null, which is a smaller population than captured rows because a capture can still be in flight.
Truncate settled_at to a date with an explicit time zone so the cut matches the ledger's cut, since settled_at is timestamptz and date_trunc on timestamptz silently uses the session time zone.
Join dim_currency twice, once on transaction_currency and once on settlement_currency, so both exponents are on the row. Minor units are not a common scale: a bare captured_amount_minor * settlement_fx_rate is correct only when the two exponents are equal, and a zero-decimal currency settling into a two-decimal one is wrong by a factor of 100.
Convert per row as ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle)) — minor units to major in the transaction currency, apply the major-per-major rate, then back to minor units in the settlement currency. The collapsed form ROUND(captured_amount_minor::numeric * settlement_fx_rate * POWER(10::numeric, exp_settle - exp_txn)) is the same expression. Round per row and then sum, not SUM(...) * an average rate, because the rate varies row by row and rounding per row is what the settlement file did.
Group by the settlement date and settlement_currency together, never by date alone, and carry the currency into every output column name or row.
Compare the two totals with a tolerance scaled by settled_count, since per-row rounding accumulates linearly in the number of rows rather than being a fixed constant.

Worked solution (25 min)

Filter to settled rows and derive settlement_date from settled_at with an explicit time zone.
Join dim_currency on transaction_currency and again on settlement_currency to pick up exp_txn and exp_settle; fail the run if either is null rather than defaulting to 2.
Aggregate by settlement_date and settlement_currency: COUNT(*), SUM(settlement_amount_minor), and SUM(ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle))).
Add a derived difference column and a boolean flag where ABS(difference) > settled_count.
Order by the flag first and then by settlement_date so the exceptions surface at the top.

Expected result: One row per settlement_date and settlement_currency, carrying settled_count, settlement_total_minor, converted_capture_total_minor, their signed difference, and a boolean exception flag. On clean days the difference is small relative to settled_count and has no consistent sign. Pairs with unequal exponents reconcile exactly as well as equal-exponent pairs; if they do not, the exponent terms are missing rather than the FX rate being wrong.

Verify it yourself

The difference divided by settled_count stays well under one minor unit on unflagged days, which is the expected size of per-row rounding.
Restricted to rows where exp_txn = exp_settle, the exponent-aware conversion and a bare captured_amount_minor * settlement_fx_rate agree exactly; restricted to rows where the exponents differ by 2, the bare form is smaller by exactly a factor of 100. That is the direct test that the dim_currency join is load-bearing.
Re-running with SUM over all currencies at once produces a number that changes when a zero-decimal currency's volume moves, which demonstrates why the cross-currency total is meaningless.
settled_count summed over all dates equals COUNT(*) on the same settled-row filter with no grouping.

Follow-up

A partial capture means captured_amount_minor is less than amount_minor. Where does that show up in this reconciliation, and where does it not?
On one currency pair the converted total is consistently about one hundredth of the settlement total, on every date, while the other pairs reconcile. Which two columns do you inspect first, and what single change fixes it?
The rate is documented as major-per-major. If a feed started publishing it minor-per-minor instead, which pairs would still reconcile and which would break?
How would you present a total across currencies to a finance partner who has asked for one number?

How do you prioritize tasks when managing multiple projects?

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?

How would you approach reducing customer churn using data?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

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?

You have a dataset with various features. Describe how you would determine which features are most important for predicting an outcome.

Medium · product sense and metrics

Approach

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

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?

Read a segment-sliced result with a fading first-week lift

Medium · multiple comparisons · novelty effects · mix shift

A three-week test of an in-app credit-limit-increase offer had one pre-registered primary metric. The readout also slices 24 secondary segment-by-metric cells, of which three are significant at 0.05 with p-values 0.011, 0.028 and 0.041. The treatment effect on the primary was plus 8.1 percent in calendar week one, plus 3.4 percent in week two and plus 1.5 percent in week three. Say which claims survive, what the decay does and does not prove, and what you tell the sponsor.

Approach

Compute what chance alone produces before interpreting any cell. With 24 tests at 0.05 the expected number of false positives is 1.2, the probability of at least one is 1 - 0.95^24 = 0.708, and the probability of three or more under an independence approximation is about 0.12. Three hits is an unremarkable outcome, not a signal.
Apply a stated correction rather than a vibe, and be precise about which one the reported numbers can settle. Holm and Bonferroni both begin at 0.05 / 24 = 0.00208, so the smallest p-value alone decides them. Benjamini-Hochberg sorts all m p-values ascending and finds the largest k with p(k) <= k * q / m; at q = 0.10 and m = 24 the rank-k threshold k / 240 starts at 0.00417 and rises to 0.10 at k = 24, so it is a step-up test over the whole family and its verdict cannot be read off the three cells that happened to fall below 0.05. Ask for all 24 p-values, report the surviving set, which may be empty, and demote the rest to hypotheses for a future test.
Refuse to call the decay novelty until the competing explanations are ruled out. Three things produce a falling weekly effect: a genuine novelty response from existing users that reverts, a mix shift because week three's exposed population is not week one's, and an effect that is correctly one-shot because a limit increase is taken once per customer.
Separate them with concrete cuts. Plot the effect against time since first exposure instead of calendar week, which removes the mix shift. Compare users onboarded during the test, who never saw the previous experience and therefore cannot show novelty, against existing users. Hold back a small fraction of traffic for a long-run read.
Fix the estimand to match the mechanism. For a one-shot offer the meaningful quantity is cumulative take-up per exposed user over a fixed horizon, not a weekly rate, and under that estimand a declining weekly rate is exactly what a working feature looks like.
Give the sponsor one number with its interval, name the decision it supports, and list the exploratory cells separately under a heading that says they are not results.

Worked solution (25 min)

Compute the chance baseline: 0.95^24 = 0.292, so P(at least one false positive) = 0.708 and the expected count is 1.2.
Settle the corrections the reported numbers can settle. Holm and Bonferroni both require the smallest p-value to clear 0.05 / 24 = 0.00208; 0.011 does not, so neither rejects anything, and that conclusion needs only the one number. Benjamini-Hochberg is different: with m = 24 and q = 0.10 the rank-k threshold is k / 240, which runs 0.00417, 0.00833, 0.0125 for the three reported cells (all three fail their own thresholds) but passes 0.05 at k = 13 (0.05417) and reaches 0.10 at k = 24. Because BH rejects the k smallest hypotheses whenever any p(k) clears its threshold, one of the 21 unreported p-values sitting between 0.05 and its own rank threshold would make BH reject every cell down to rank 1, the three included. Demand the full ordered vector of 24 p-values before stating a BH verdict.
Rebuild the weekly series on time since first exposure and overlay new versus existing users, then state which of novelty, mix shift and one-shot mechanics the pattern is consistent with.
Recompute the primary as cumulative take-up per exposed user at a fixed 21-day horizon and report that with its confidence interval as the headline.
Write the sponsor summary: one confirmatory number, the exploratory cells listed as non-results pending the full p-value vector, and a proposed follow-up test for the single most plausible segment hypothesis.

Expected result: Holm and Bonferroni reject nothing, and that follows from the reported numbers alone: the smallest p-value, 0.011, exceeds 0.05 / 24 = 0.00208. Benjamini-Hochberg at q = 0.10 is not decidable from three p-values, because it is a step-up test over all 24 whose rank-k threshold k / 240 passes 0.05 at k = 13, so the correct move is to demand the full ordered vector and name the condition that would flip it rather than assert that nothing survives. Until that vector arrives the three cells are exploratory, not findings. Only the pre-registered primary is confirmatory, restated as cumulative take-up over a fixed horizon. The 8.1 to 1.5 percent decay is not established as novelty; the correct answer names mix shift and the one-shot nature of a limit increase as competing explanations and gives the cuts that separate them.

Verify it yourself

Binomial sanity check: P(3 or more hits out of 24 at 0.05) is about 0.12 under independence, so three hits is unremarkable; correlation among the cells makes the true probability higher still.
BH self-check: the rank-k thresholds k * 0.10 / 24 must run from 0.00417 at k = 1 to 0.10 at k = 24 and cross 0.05 between k = 12 and k = 13. Any BH conclusion that uses only the p-values below 0.05 is ignoring the ranks where the threshold is loosest.
The cumulative take-up curve per exposed user should be monotone increasing and flatten, while the weekly rate falls. Both are the same data, which is the point.
New users during the test should show no decay if the decay is novelty. If they decay the same way, novelty is not the explanation.
Re-run the segment slice on an A/A period: if it also produces two or three significant cells, the slicing procedure itself is the finding.

Follow-up

The three significant cells are all in the same segment. Does that change your reading, and how would you test whether it is a real interaction rather than three correlated slices of one population?
Design the long-term holdback: what size, for how long, and what does it cost you in foregone treatment?
If cumulative take-up is the estimand, what is the right horizon and how do you keep it from being chosen after seeing the data?

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 challenging project you worked on. How did you overcome obstacles?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Close with what you would do differently, concretely.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

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

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?

Disagree with a product manager over an approval-rate target

Medium · metric design · denominators · influence without authority

A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.

Approach

Separate the goal from the metric out loud and agree with the goal first, so the disagreement stays narrow and technical rather than becoming positional.
Demonstrate the failure rather than asserting it: compute the proposed metric and the dollar-weighted collapsed version over the same recent window, and find a period where they moved in opposite directions.
Propose the replacement precisely: sum of approved amount_minor over sum of attempted amount_minor, after collapsing retries to one attempt per card_token_id, merchant_id and amount_minor within a 15-minute window, excluding is_reversal rows and zero-amount verifications, with everything converted to one reporting currency before summing.
Attach the guardrail that makes the target honest: matured first-chargeback rate and net fraud loss in basis points of settled volume, read only on transaction months carrying at least 120 days of maturity.
Give the product manager something back: the replacement metric cuts cleanly by channel and issuer_country, which makes a roadmap of merchant-specific and authentication fixes legible in a way the blended rate never was.

Follow-up

How do you identify a retry chain when the merchant varies the amount slightly between attempts?
The product manager wants a weekly read on the guardrail. What is the earliest defensible signal, and how do you label it?