Interview guides · Blend360

Blend360 Data Scientist Interview Questions & Guide 2026

Case practice for Blend360 data scientist candidates: scope a vague prompt into a measurable question before you analyze it.

The 60-second brief

As a Data Scientist at Blend360, you are positioned at the intersection of data analysis and strategic decision-making. This role is critical as it directly impacts how the organization leverages data to inform product development, optimize operational efficiencies, and enhance user experiences. You will work with a diverse set of data sources, employing advanced statistical methods and machine learning algorithms to extract insights that drive business growth.

Nearly every loop contains a round whose deliverable is a recommendation to someone non-technical. Practise stating a conclusion, the confidence attached to it, and the cost of being wrong in each direction, because that triple is the artifact being graded.

Focus areas

Decompose expected loss into PD, LGD, EAD
Read vintage curves, not blended portfolio averages
Set fraud thresholds by expected cost

Overview

The work you do as a Data Scientist is both challenging and rewarding. You will engage with cross-functional teams, including engineering and product management, to identify complex problems and develop data-driven solutions. The ability to translate analytical findings into actionable strategies is vital, as your insights will shape the direction of key projects and initiatives. Expect to contribute to impactful projects that address real-world challenges, ensuring that data-driven decisions are at the forefront of Blend360's operations.

The Blend360 interview process

Blend360 candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Online Assessment

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What to demonstrate

Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows

Technical Interviews

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

Behavioral Interviews

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

Case Studies

A case has a fixed clock, and a good deal of what is being scored is how you spend it. Thirty to forty-five minutes buys one pass across the whole problem or a deep read of one part of it, and choosing between those is the work rather than a compromise forced on you. Announce the shape early: the structure you are using, the branch you think carries the decision, and what you are setting aside. An answer that is thorough for the first third and silent on the recommendation reads worse than one that is rougher throughout and lands.

What to demonstrate

Whether a visible structure appears in the opening minutes and survives the rest of the case
Whether the depth goes to the branch that carries the decision, rather than the branch you find most comfortable
Whether you say what you are leaving out and why, instead of quietly omitting it and hoping nobody asks

How to prepare

After each practice case, write down the branches you chose not to open and the reason for each, then check whether you said any of them out loud while the case was running. A branch you only cut privately reads to the interviewer as one you missed.
Redo a case you have already worked in half the time, deciding in advance which single branch you keep, then compare which version a listener would find more useful.
Write a two-sentence opening you can reuse, holding the restated question and your plan for the available time, and deliver it within the first ninety seconds of every practice run.

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.

Ending an analysis without a recommendation or next step

Close with what you would do and what would change your mind, stated as a condition you can check later. If the evidence is genuinely inconclusive, recommend the specific next measurement and say what it costs in time or exposure.

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Questions & practice

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

Medium · statistics and probability

Approach

Quantify uncertainty explicitly rather than reporting a point estimate alone.
Sanity-check the answer against a simple bound or a simulated case.
Say what the estimate is of, and over what population it generalises.

Follow-up

How would you explain this result to someone who does not know statistics?
Which assumption here is most likely to be violated in practice?

How would you optimize a machine learning model for better performance?

Medium · machine learning and modelling

Approach

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

Follow-up

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

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?

Given a dataset, how would you handle missing values?

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
Compute rates by summing numerator and denominator separately, never by averaging rates.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

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

Write a SQL query to find the second highest salary from a table.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Vintage ninety-plus rate at twelve months on book

Medium · vintage analysis · window functions · right censoring

From fct_loan_performance_monthly, build a vintage table keyed on origination_month: the share of each cohort that ever reached days_past_due of 90 or more, or charge_off_flag = true, at or before months_on_book = 12. Restructuring resets days_past_due, so for any loan with restructured_flag true, evaluate only the month ends strictly before its first restructured month. Return origination_month, loans_funded, bad_loans and bad_rate. Exclude any cohort that does not yet have a months_on_book = 12 observation for every loan still on book.

Approach

Establish the cohort denominator from the first month end each loan appears at, months_on_book = 0, so a loan is counted once in its origination_month rather than once per monthly row.
Find each loan's first restructured month with MIN(as_of_month_end) FILTER (WHERE restructured_flag) OVER (PARTITION BY loan_id), or the equivalent grouped subquery, and keep it null for loans never restructured.
Flag a loan bad if any row with months_on_book <= 12 and as_of_month_end earlier than that first restructured month has days_past_due >= 90 or charge_off_flag = true, which is what 'pre-restructure worst state' means in practice.
Gate maturity by requiring the cohort's newest month end to be at least 12 months after origination_month, and report immature cohorts as incomplete rather than letting them appear at a flattering low rate.
Aggregate to one row per origination_month and read the column downward, not across calendar time, because the whole point is comparing cohorts at equal age.

Worked solution (35 min)

CTE 1: per loan_id, derive origination_month, the first restructured month end, and the maximum months_on_book observed.
CTE 2: join back to the monthly rows, filter to months_on_book <= 12 and to month ends before any restructure, then flag bad with a BOOL_OR.
CTE 3: aggregate to origination_month with COUNT() as loans_funded and COUNT() FILTER (WHERE is_bad) as bad_loans.
Apply the maturity gate and compute bad_rate with a numeric cast.

Expected result: One row per matured origination_month with loans_funded, bad_loans and a bad_rate between 0 and 1. The most recent immature cohorts are absent from the output entirely rather than present with a small bad_rate.

Verify it yourself

SUM(loans_funded) across reported cohorts equals COUNT(DISTINCT loan_id) restricted to those same origination months.
Recomputing without the months_on_book cap raises bad_rate monotonically for every cohort, and raises it most for the oldest, which is the seasoning effect made visible.
No cohort in the output has a maximum months_on_book below 12.

Follow-up

Should a restructure inside 12 months count as bad in its own right? Argue both sides and say what you would actually ship.
A loan that prepaid in full at month 4 never had a chance to go 90 days past due. In or out of the denominator, and why?
The 2025-11 cohort is two points worse at month 12 than its neighbours. What three queries do you run before you call it a credit-quality change?

What metrics would you use to evaluate the success of a data-driven project?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Fix the population and the time window before naming any metric.
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?

How do you prioritize your tasks when managing multiple deadlines?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.
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 actionable insights?

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?

Describe how you would design an A/B test for a new product feature.

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
Say whether units interfere with each other, and switch design if they do.
Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

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

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.
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?

Test a referral rule when reviewers are a shared queue

Medium · interference · switchback · cluster randomisation

A proposed risk rule raises the share of ecommerce authorizations routed to manual review. Reviewers work a shared pool of queues serving both arms, so extra referrals from treatment lengthen the wait for control cases too. The current plan randomises by customer_id and reads decision latency plus matured fraud basis points. Explain why that design is biased and in which direction, propose a design that is not, and say what governs its power.

Approach

Name the violated assumption precisely: a unit's outcome depends on other units' assignments through the shared reviewer capacity, so the stable unit treatment value assumption fails. Customer-level randomisation then estimates a contrast between a degraded treatment and a degraded control, not between treatment and the status quo.
State the direction. Treatment pushes work into the shared queue, control absorbs part of that wait, so the measured latency difference understates the true effect of full rollout. The test can look acceptable while the rolled-out state is materially worse, which is the expensive failure mode here.
Move randomisation up to a unit that contains the interference. Either cluster-randomise whole queues or sites, or run a switchback that flips the rule for an entire queue over time blocks. Switchback is usually the better choice here because queue count is small and each queue serves as its own control, removing between-queue heterogeneity.
Specify the switchback concretely. Block length should be several times the queue sojourn time; discard a burn-in after each switch equal to the 95th percentile sojourn so carryover cases from the previous regime do not contaminate the new block; balance assignment within each day so the daily arrival pattern cannot correlate with arm.
Analyse at the randomisation unit. Aggregate to queue-block means, include queue and time-block fixed effects, and use randomisation inference or a wild cluster bootstrap rather than cluster-robust standard errors, which are anti-conservative with few clusters. Check residual autocorrelation between adjacent blocks; if it is material, widen the blocks or model it.
Separate the two readouts by maturity. Latency and referral precision at case close are available inside the test window; matured fraud basis points require at least 120 days of dispute maturity from the transaction month, so it is a deferred confirmatory read and must not be presented as a low number on immature cohorts.

Worked solution (30 min)

Write the interference channel explicitly: reviewer capacity is fixed per queue per hour, arrivals are the sum of both arms, so control's wait is a function of treatment's assignment.
Choose a queue-by-four-hour-block switchback across 14 queues for 30 days, giving 14 * 6 * 30 = 2,520 blocks, with within-day balanced assignment and a burn-in equal to the 95th percentile sojourn discarded at each switch.
Compute power at the block level. With a residual block-level standard deviation of decision latency of 6 minutes after removing queue and day-of-week fixed effects, and half the blocks per arm, MDE = 2.8016 * sqrt(2 * 36 / 1260).
State the caveat that adjacent blocks are autocorrelated, so the effective block count is below 2,520 and the analytic MDE is a floor; estimate the inflation from historical block-to-block autocorrelation before committing.
Split the readout: latency and referral precision as the in-window primary, matured fraud basis points as a deferred read at 120 days with immature months marked incomplete rather than plotted.

Expected result: A queue-by-time-block switchback with burn-in exclusion and within-day balanced assignment, analysed on block means with queue and time fixed effects and randomisation inference. With 2,520 blocks and a 6-minute residual block standard deviation, the analytic MDE on decision latency is 2.8016 * sqrt(72 / 1260) = 2.8016 * 0.2390 = 0.67 minutes, stated as a floor pending the autocorrelation correction. Power is governed by block count and between-block residual variance, not by transaction volume.

Verify it yourself

An A/A switchback on historical data should produce block-level differences centred on zero with a standard deviation matching the residual block standard deviation used in the power calculation.
Plot the effect against time since the last switch. If it is not flat after the burn-in window, the burn-in is too short and carryover is still present.
Confirm that arm assignment is orthogonal to hour of day and day of week in the realised schedule, since queue load is strongly periodic.
Cluster count, not row count, should appear in the degrees of freedom of the final inference; if the standard error implies thousands of independent observations, the analysis is at the wrong grain.

Follow-up

How do you set the block length when the queue sojourn time itself changes under treatment?
You have 14 queues and 30 days. Compare a queue-level cluster design against a queue-day switchback on power and on what each can estimate.
The shared resource is capacity. What happens to your estimate if reviewers work faster when the queue is long?

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

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

Day 1 — Breadth pass: query fluency

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

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

Day 2 — Breadth pass: statistics and inference

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

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

Day 3 — Breadth pass: modelling

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

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

Day 4 — Breadth pass: product judgement

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

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

Day 5 — Depth, first area

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

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

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

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

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

Day 7 — Integration and re-measurement

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

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

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

What motivates you to work in data science?

Medium · behavioural and stakeholder questions

Approach

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.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

How did you know the outcome was caused by your change?
What did you decide not to do, and why?

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?

Explain an incomplete dispute chart to a non-technical executive

Easy · dispute maturity · stakeholder communication · right-censoring

A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.

Approach

Lead with the mechanism in the listener's own terms, not with the statistical name for it: a dispute is attributed to the month the transaction happened, but it can be filed up to roughly 120 days later, so recent months contain only the disputes filed so far.
Show completeness rather than arguing about the rate: for each transaction month, plot the share of its eventual disputes already filed, estimated from months that are fully matured. The last three months will sit visibly below 100 percent.
Replace the chart with two artefacts: a matured series that stops 120 days back and is labelled final, and a development-factor estimate for the immature months drawn as a dashed range and labelled an estimate.
Hand over one sentence the executive can repeat without you in the room: the recent months look better because the disputes have not arrived yet, not because fewer will arrive.
Offer a weekly signal they can watch instead, such as the risk-score mix of approved volume or the decline-rule hit rate, and state up front what it does and does not predict.

Follow-up

The deck ships tomorrow regardless. What exactly goes on the slide, and what wording do you insist on?
How would you estimate the development factors, and how would you notice if they had shifted?