Interview guides · City National Bank of Florida

City National Bank of Florida Data Scientist Interview Questions & Guide 2026

Causal inference drills for City National Bank of Florida data scientist prep: diff-in-diff, matching, instruments, and the limits of observed data.

The 60-second brief

A Data Scientist at City National Bank of Florida plays a pivotal role in transforming data into actionable insights that drive strategic decision-making and enhance customer experiences. This position is vital as it directly influences the bank's ability to leverage data for optimizing products, improving service delivery, and identifying growth opportunities in a competitive financial landscape. You will work closely with various teams, including finance, marketing, and operations, to analyze complex datasets and provide predictive analytics that inform key business strategies.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Read vintage curves, not blended portfolio averages
Report only matured cohorts for loss metrics
Decompose expected loss into PD, LGD, EAD

Overview

In this role, you will engage in challenging and impactful projects, such as developing machine learning models to predict customer behavior, automating reporting processes, and performing risk analysis. The complexity and scale of the data you will work with offer an exciting opportunity to influence high-stakes decisions that affect the bank's overall performance. You can expect to collaborate with cross-functional teams to address real-world problems, making this role both critical and engaging.

The City National Bank of Florida interview process

City National Bank of Florida candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Phone Screening

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 Interview

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

In-Person Interview

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Where candidates lose points

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

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

Averaging delinquency across a book that is growing

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

Reading an observational correlation as a causal effect

Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.

Dropping rows with missing values without naming the mechanism

Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.

Questions & practice

What metrics do you use to evaluate a model’s performance?

Medium · machine learning and modelling

Approach

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.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Explain bias-variance tradeoff in machine learning models.

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.

Follow-up

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

Simulate false alarms in a merchant chargeback monitoring rule

Medium · simulation · rare events · monitoring thresholds

Baseline matured first-chargeback rate is 12 per 10,000 settled transactions. A monitoring rule alerts when a merchant's observed monthly rate exceeds twice baseline. For monthly settled transaction counts of 500, 2,000, 10,000 and 50,000, simulate the false-alarm probability per merchant-month under the baseline, and the power to detect a merchant whose true rate is 30 per 10,000. Then, for a portfolio of 4,000 merchants split 60, 25, 10 and 5 percent across those four counts, give the expected number of false alarms per month.

Approach

Recognise the rule is a threshold on an integer count, not on a continuous rate. At n = 500, twice baseline is 24 per 10,000, so the first observable value above it is 2 chargebacks, or 40 per 10,000. Derive the trigger count for every n before simulating anything.
Draw binomial counts with numpy at p = 0.0012 and take the share at or above the trigger for the false-alarm rate, then repeat at p = 0.0030 for power. Use at least 200,000 draws per cell so a probability near 0.001 has a usable standard error.
Cross-check every simulated cell against the Poisson approximation with lambda = n*p, which is tight here because p is tiny. A mismatch almost always means the trigger count is off by one.
Weight the per-merchant false-alarm probabilities by the portfolio mix, and report the share of expected alerts contributed by each size band rather than only the total.
Close on the operating consequence: a fixed multiplicative threshold is not a constant false-alarm rate across merchant sizes, so either the threshold scales with n or small merchants need a minimum volume before the rule applies.

Worked solution (30 min)

For each n, compute trigger = floor(2 * 0.0012 * n) + 1 and print the four values before simulating.
Simulate 200,000 binomial draws per n at p = 0.0012 and take the share at or above the trigger.
Repeat at p = 0.0030 and record power for the same triggers.
Compute the Poisson tail 1 - CDF(trigger - 1, lambda = n*p) for both p values and confirm agreement within Monte Carlo error.
Multiply the false-alarm probabilities by 2400, 1000, 400 and 200 merchants and sum.

Expected result: Trigger counts of 2, 5, 25 and 121. False-alarm probability roughly 12 percent at n = 500, roughly 10 percent at n = 2,000, under 0.1 percent at n = 10,000 and effectively zero at n = 50,000. Power against 30 per 10,000 roughly 44, 72, 85 and above 99 percent. Expected false alarms about 390 per month, with over 99 percent of them coming from the two smallest bands.

Verify it yourself

The four trigger counts are derived, not guessed: each is the smallest integer strictly above 0.0024n, giving 2, 5, 25 and 121. Derive them in exact or integer arithmetic, because 20.0012*10000 evaluates to 23.999999999999996 in binary floating point and floor-plus-one silently returns 24.
Simulated probabilities match the Poisson tail at lambda = n*p within about two Monte Carlo standard errors.
Power exceeds the false-alarm rate at every n; if it does not, the null and alternative rates are swapped.
Doubling the draw count moves each probability by less than its own simulated standard error.

Follow-up

How would you set a threshold that holds the false-alarm rate roughly constant across merchant size?
The rule reads the transaction month, but disputes arrive for up to 120 days afterwards. What does that do to the alert and how would you fix it?
What does a month of these false alarms cost, and how would you decide whether it is worth paying?

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?

Monthly delinquency roll rates with honest exiting-account denominators

Hard · roll rates · window functions · self joins

From fct_loan_performance_monthly, compute month-over-month roll rates: for each as_of_month_end and delinquency_bucket, the share of loans in that bucket that are in a worse state at the following month end. Use this disposition convention and apply it without exception: charge-off is the terminal state one rank past dpd_90_plus, so a loan that charges off has rolled forward, not exited; loans that leave the book any other way — prepaid in full, sold, matured, or simply absent at the next month end — are exited. Bucket order is current, dpd_1_29, dpd_30_59, dpd_60_89, dpd_90_plus, then charged_off. Return from_month, from_bucket, accounts, rolled_forward, cured, stayed, exited and roll_rate, and show that the four dispositions sum to accounts on every row.

Approach

Map the five delinquency_bucket values to ranks 0 through 4 in a small inline VALUES list, and reserve rank 5 for charged_off, because 'worse' is an ordering statement and enum text does not sort that way reliably. Read charge-off from charge_off_flag on the next month's row rather than from delinquency_bucket, which keeps carrying whatever delinquency value the loan held when it was written off.
Use LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) OVER (PARTITION BY loan_id ORDER BY as_of_month_end), then verify the leading month end is exactly one month after the current one, since a loan missing a month would otherwise appear to cure or roll across a gap it never traversed.
Classify each loan-month into exactly one of four labels off the adjacent next row, whose rank is 5 when its charge_off_flag is true and its mapped bucket rank otherwise. rolled_forward: the next rank is strictly greater. cured: strictly lower. stayed: equal. exited: there is no adjacent next row at all, which is where prepayment, sale, maturity and a gap in the monthly history land.
Define rolled_forward as 'any strictly worse rank', not 'rank plus one'. Under normal monthly aging the only reachable worse state is the next bucket, so the two definitions coincide — except at charge-off, where a loan can go from dpd_60_89 straight to rank 5. A strict plus-one test leaves those loan-months matching no branch and breaks the exhaustiveness invariant.
Be explicit about why charge-off is a roll rather than an exit: routed to exited, dpd_90_plus has no worse state left, so its roll_rate is identically zero by construction — arithmetic, not a credit finding. At rank 5 the dpd_90_plus row reports roll-to-loss, which is the number a loss forecast actually consumes.
Aggregate by from_month and from_bucket and require that rolled_forward + cured + stayed + exited equals accounts on every row, which is the invariant that proves the classification is exhaustive and mutually exclusive.

Worked solution (45 min)

CTE ranks: an inline VALUES mapping of the five delinquency_bucket values to ranks 0 through 4, joined onto the monthly rows. Rank 5 is not in the map; it is assigned during classification.
CTE leads: add LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) partitioned by loan_id ordered by as_of_month_end.
CTE classified: derive next_rank as CASE WHEN next_charge_off_flag THEN 5 ELSE next_bucket_rank END, then a CASE expression producing exactly one label per loan-month — exited when the next month end is null or is not the current month end plus one month, otherwise rolled_forward / cured / stayed by comparing next_rank to bucket_rank.
Aggregate to from_month and from_bucket with COUNT(*) and four FILTER counts, then compute roll_rate as rolled_forward over accounts with a numeric cast.
Assert the exhaustiveness invariant in a final HAVING or a separate verification query before trusting any number.

Expected result: One row per month and originating bucket, with the four disposition counts summing to accounts on every row and roll_rate between 0 and 1. Roll rates climb across current, dpd_1_29, dpd_30_59 and dpd_60_89, because each successive bucket holds loans that have already failed to cure. Do not expect that climb to continue into dpd_90_plus: that bucket is a catch-all holding several monthly cohorts at once until the charge-off policy retires them, so its roll-to-loss rate is set by the policy horizon — under a 180-day policy roughly a third of the bucket rolls in a given month — and usually sits below the dpd_60_89 rate. Cure out of dpd_90_plus is well under ten percent.

Verify it yourself

rolled_forward + cured + stayed + exited equals accounts for every row, with no exceptions.
SUM(accounts) for a given from_month equals the count of loan rows at that month end, so no loan-month was dropped or counted twice.
Removing the adjacency test changes the cure and roll counts, and the size of that change is exactly the number of loans with a gap in their monthly history.
The rolled_forward count for from_bucket dpd_90_plus in month M is at least the number of loans whose first charge_off_flag = true row falls at month end M+1. If it is materially lower, charge-off is being routed into exited.
roll_rate for dpd_90_plus is strictly greater than zero in any month with charge-offs. A flat zero on that bucket across every month is the signature of charge-off classified as an exit, not of an unusually stable book.
No loan-month is counted as both rolled_forward and exited: a loan with a charged-off row at M+1 has an adjacent next row, so it can never reach the exited branch.

Follow-up

Project the next three months of dpd_90_plus inflow from these roll rates. What assumption does that projection make, and when does it break?
A restructure resets days_past_due to zero. What does that do to the cure rate out of dpd_60_89, and how would you separate a real cure from a reset?
Argue the other convention: prepayment in exited but charge-off also in exited, with roll rates reported only for the four non-terminal buckets. What does that series answer better, and what does it lose?
The charge-off policy changed from 180 to 120 days. Show where that appears in this table and what it does to the dpd_90_plus roll rate specifically.

Walk me through your process of developing a data-driven recommendation system.

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

Describe a challenging data problem you faced and how you solved it.

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.

Follow-up

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

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

Medium · product sense and metrics

Approach

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

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you stay updated with the latest trends in data science?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Fix the population and the time window before naming any metric.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

What is the difference between supervised and unsupervised learning?

Medium

Approach

Work from the decision backwards to the evidence you would need.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.

Follow-up

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

Build the metric tree for a new instalment loan

Medium · metric tree · vintage analysis · leading indicators

A new instalment loan product launches next quarter. Build the metric tree from fct_loan_application and fct_loan_performance_monthly: one primary metric, the intermediate metrics that explain it, and the guardrails. The product owner wants a go or no-go call six weeks after the first disbursements, while the primary outcome is defined at twelve months on book. Name the leading indicator you would accept for the six-week call, state its bias in one sentence, and say what you would refuse to conclude from it.

Approach

Put the primary where the trade is real: risk-adjusted margin per 1,000 units of principal originated, evaluated at months_on_book = 12, charging funding cost at the internal transfer rate. Approval rate and origination volume can both be moved in a week by lowering the cutoff, and neither charges anything for the consequence.
Lay the tree out by layer so a movement can be localised: approval rate on a decision-reaching denominator (decision in ('approve','decline'), excluding withdrawn and expired) feeds offer-to-funded conversion within 30 days by offered_apr_bps band, which feeds the vintage 90+ rate at months_on_book = 12, which feeds risk-adjusted yield on average balances. Each layer's denominator is the numerator of the layer above.
Choose guardrails that catch the opposite failure to the primary's. A margin target is trivially met by shrinking the book to its safest slice, so report approval rate within each model_pd_12m band, where that collapse shows up first, alongside adverse-impact monitoring of the decision rule under compliance review.
For the six-week call, accept first-payment behaviour: the share of funded loans missing the first scheduled payment, or reaching days_past_due at or above 30 by months_on_book = 3. Estimate its relationship to the 12-month 90+ rate from completed vintages of the nearest comparable product, and report the dispersion of that ratio across those vintages as the uncertainty rather than quoting a point estimate.
State the bias in one sentence: early default is loaded toward application fraud and income misstatement and is close to blind to affordability deterioration that develops over months, which is the failure mode a new product with a new population is most likely to have, and the historical ratio breaks exactly when the population or the macro environment changes.
Say what six weeks cannot answer: nothing about the 12-month level in absolute terms, and nothing whatsoever about the applicants the policy declined.

Worked solution (35 min)

Write each metric in the tree as numerator, denominator, exclusions and window, four lines each, before writing any SQL.
Build a vintage table from fct_loan_performance_monthly with origination_month as rows and months_on_book as columns for an existing comparable product, and read the diagonal as calendar time.
For the last eight to twelve complete vintages, compute both the 30+ rate at months_on_book = 3 and the 90+ rate at months_on_book = 12, take the ratio, and record its minimum, median and maximum.
Write the six-week readout using that ratio range as an interval, with the one-sentence bias statement attached to the number itself rather than to a footnote.

Expected result: A four-layer tree whose primary is risk-adjusted margin at months_on_book = 12, plus a six-week readout that converts an observed three-month 30+ rate into a range for the twelve-month 90+ rate using the historical ratio's dispersion, labelled as an estimate with its bias stated inline.

Verify it yourself

Every denominator in the tree is the numerator of the layer above it. If one is not, a movement cannot be attributed to a layer.
The ratio between the three-month and twelve-month rates varies across vintages. If your data shows it constant, confirm you are not comparing cohorts at unequal maturity.
Restructured loans enter the 90+ numerator on their pre-restructure worst state, because restructuring resets days_past_due and would otherwise erase the event.

Follow-up

The first vintage looks excellent at three months on book. What would make you distrust it?
How do restructured loans enter your twelve-month numerator?
Which metric in this tree would you refuse to put on a weekly dashboard, and why?

Manual review rate doubles on the day two changes ship

Hard · definition change · model version · population stability

Manual review rate on fct_loan_application, defined as decided_by = 'manual' over decisions in ('approve','decline'), stepped from 9 to 18 percent on a single day. That day a new model_version went live, and the same release changed which referral paths are recorded as manual. Columns: submitted_at, channel, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. Attribute the step across definition, model and population, with a number against each.

Approach

Establish the shape before the cause. A definition or code change produces a step at the deploy timestamp; a population change produces a ramp. Plot at hourly granularity either side of the release and read which one this is.
Dual-run the definition. Recompute the metric under both the old and the new recording rules across several months of history. If the historical series under the new rule sits near 18 percent throughout, that part of the step is definitional and should be restated, not investigated.
Hold the model constant and decompose the residual by policy_rule_hits identifier. A referral rule keyed to a score threshold shows its own step, and naming the rule is what makes the finding actionable to the policy owner.
Test the score, not the applicants. Compare model_pd_12m distributions by model_version on the same applications where shadow scores exist; otherwise compare adjacent weeks with a population stability index, treating the customary 0.1 and 0.25 marks as heuristics rather than tests, and assess calibration separately from ranking.
Test the applicants, not the score. Compare channel mix, bureau_score distribution and null-bureau share across the boundary. If those are stable, population is not a driver and should be reported as approximately zero rather than left unquantified.
Deliver three components that sum to the observed step, each with the team that owns it.

Follow-up

If the new model is better calibrated, is a higher referral rate the correct outcome or a threshold nobody retuned?
How would you have caught the recording change before it reached the dashboard?
What is the swap set here, and which swap group would you inspect first?

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

The project you argued against

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

How do you handle missing data in a dataset?

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Pick a story where you drove the decision, not one where you observed it.
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?

How would you explain your findings to a non-technical stakeholder?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
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.

Follow-up

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

Defend a vintage finding that contradicts the portfolio dashboard

Medium · vintage analysis · mix shift · stakeholder pushback

The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.

Approach

Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.

Follow-up

Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?