Interview guides · Bajaj Finance

Bajaj Finance Data Scientist Interview Questions & Guide 2026

Bajaj Finance Data Scientist interview guide: reported rounds, practice questions with worked solutions and a 7-day prep plan.

The 60-second brief

A Data Scientist at Bajaj Finance sits at the intersection of complex financial modeling and large-scale consumer product delivery. As one of India’s most diversified non-banking financial companies, the organization relies on data to drive critical decisions across its massive portfolio of loans, insurance, and investment products. You will not just be building models; you will be solving high-stakes problems like fraud detection, credit risk assessment, and personalized recommendation systems that touch millions of users.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

Separate authorization, settlement and dispute outcomes cleanly
Set fraud thresholds by expected cost
Read vintage curves, not blended portfolio averages

Overview

This role is highly collaborative and product-oriented. You will work alongside engineers, product managers, and business stakeholders to translate raw data into actionable intelligence. Whether you are optimizing search relevance or deploying production-grade AI models, your work directly impacts the financial stability and user experience of a top-tier fintech leader. Expect a fast-paced environment where technical rigor, such as implementing RAG systems or managing distributed ML pipelines, is balanced by a deep need for business-centric problem-solving.

The Bajaj Finance interview process

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

Technical Screening

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

Coding Assessment

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Deep-Dive Discussions

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Final Management Rounds

A loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.

What to demonstrate

Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience

How to prepare

Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story

Where candidates lose points

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.

Using written premium as the denominator of a loss ratio

Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.

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.

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

Questions & practice

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.

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?

Estimate a delinquency roll-rate matrix and project twelve months

Hard · roll rates · markov chain · survivorship

fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.

Approach

Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.

Worked solution (45 min)

Sort by loan_id and as_of_month_end, shift to form (from_state, to_state) pairs, and flag pairs whose month gap is not exactly one.
For each loan's final row, assign the absorbing destination from charge_off_flag or prepaid_in_full_flag, and list loans that vanish with neither as an exception count to report.
Apply the restructure rule, then build the 7 by 7 count matrix with a cross-tabulation over ordered state categories and row-normalise it.
Assert row sums equal one and absorbing rows are the identity, then take the current month's bucket distribution as a row vector.
Multiply twelve times, report the charged_off component, and repeat from an all-current vector for comparison.

Expected result: A 7 by 7 row-stochastic matrix with identity rows for charged_off and prepaid, a current-to-current diagonal typically above 0.95, roll rates rising with bucket depth, and a 12-month projected charge-off share of a few percentage points from an all-current start and materially higher from the actual book.

Verify it yourself

Every row sums to one within floating-point tolerance, and both absorbing rows are exactly the identity.
No mass sits in cells that skip a bucket upward, such as dpd_1_29 to dpd_60_89 in one month; if there is, the pairs are not actually consecutive months.
The projection from an all-current start is strictly below the projection from the real book distribution.
Backtest by estimating on months up to t minus 12 and comparing the projected distribution with the realised one at t; a systematic optimistic gap points at dropped exits or at seasoning, not at noise.

Follow-up

How would you validate the projection against what actually happened, and over what window?
The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
When would you prefer a vintage curve to a roll-rate projection, and why?

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.

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 two datasets, what is the most efficient way to join them in Python while handling missing values?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Check whether any join is one-to-many before aggregating, or the sums inflate.
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?

Write a query to identify the top-performing students in each subject from a multi-subject score table.

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
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.

Follow-up

How would you verify this result without re-running the same query?
What breaks if events arrive late or out of order?

How would you categorize a massive volume of call logs (e.g., 1 crore records) using Python?

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing 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 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?

If a key product metric, such as daily loan application volume, suddenly drops, how would you go about diagnosing the root cause?

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.
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?
How would you detect that the metric is being gamed rather than genuinely improving?

How would you design a metric to measure the success of a new personalized search feature on our mobile app?

Medium · product sense and metrics

Approach

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

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 work when you have multiple competing requests from different business units?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Name one primary metric, then the guardrail that stops it being gamed.
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?

What are the common pitfalls you might encounter when designing an A/B test for a financial product?

Medium · a/b testing and experimentation

Approach

Say whether units interfere with each other, and switch design if they do.
Name the randomisation unit first; it decides the variance and what the test can detect.
Name the guardrails that would stop a launch even on a positive primary result.

Follow-up

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

How do you determine the statistical significance of an experiment when the sample size is limited?

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
Name the guardrails that would stop a launch even on a positive primary result.
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?

Estimate the marginal effect of approval at a score cutoff

Hard · regression discontinuity · instrumental variables · selection

Policy auto-approves applications with bureau_score at or above 660 and routes 640 to 659 to manual review, which approves about 35 percent of them. You cannot randomise approvals. Using fct_loan_application and a bureau-sourced panel that reports 12-month serious delinquency on any trade line for approved and declined applicants alike, estimate the effect of approval at the margin. State the identifying assumptions you would test rather than assert, the estimator and its tuning choices, and exactly what the estimate does and does not license.

Approach

Set the design up as a fuzzy regression discontinuity, because crossing 660 changes approval probability sharply but not from zero to one. Let Z = 1{bureau_score >= 660} be the instrument, approval be the treatment, and 12-month serious delinquency be the outcome. The estimate is the ratio of the jump in the outcome to the jump in approval probability, which is two-stage least squares with Z as the instrument and identifies a local average treatment effect for compliers at the cutoff.
Insist on the bureau panel as the outcome source. Funded loans exist on both sides of 660 only because manual review approves some applicants below it, and those are selected on whatever the reviewer saw; an outcome defined only on funded loans would reintroduce exactly the selection the design is meant to remove.
Test the assumptions instead of listing them. Check density continuity of bureau_score at 660 with a McCrary-style or local-polynomial density test, since brokers and applicants can sometimes trigger a bureau refresh and a heaped density at the threshold kills the design. Check that pre-determined covariates, including declared_annual_income_minor, channel, product_code and model_version, are continuous at the cutoff. Check policy_rule_hits and the pricing table for any other rule that fires at exactly 660, because a second discontinuity at the same point is not separable from the first.
Choose the estimator deliberately. Local linear regression with a triangular kernel and an MSE-optimal bandwidth, with bias-corrected robust confidence intervals. Do not fit a high-order global polynomial, which puts weight on observations far from the cutoff and produces artefacts at the boundary. Because bureau_score is an integer, the running variable has mass points, so cluster standard errors by score value or use an approach designed for discrete running variables.
Respect outcome maturity. Include only application cohorts with a full 12 months of bureau observation; a recent cohort with partial observation will look cleaner and will drag the estimate.
State the limits in the deliverable. The estimate is the effect at 660 on applicants whose approval status is determined by the threshold. It does not license moving the cutoff to 620, because both the first stage and the outcome relationship differ away from 660 and because a policy change at scale shifts the applicant mix. Report bandwidth sensitivity alongside the point estimate.

Worked solution (40 min)

Assemble the application-level panel: bureau_score, decision, funded status, and the bureau-sourced 12-month serious delinquency flag, restricted to cohorts with 12 full months of outcome observation.
Plot the first stage, being approval probability against bureau_score in one-point bins, and confirm a visible jump of roughly 0.65 at 660 (about 1.00 above against about 0.35 below).
Plot the reduced form, being delinquency against bureau_score in the same bins, and read the jump at 660 off a local linear fit on each side.
Estimate the Wald ratio as reduced form over first stage, equivalently two-stage least squares with Z = 1{score >= 660}, using a triangular kernel, an MSE-optimal bandwidth, bias-corrected robust intervals, and standard errors clustered by integer score.
Run the assumption battery: density continuity at 660, covariate continuity, placebo cutoffs at 650 and 670, and bandwidth sensitivity at 0.5x and 2x the selected bandwidth.
Write the result as a complier average effect at 660 with its interval, its bandwidth sensitivity table, and an explicit sentence on what it does not license.

Expected result: A fuzzy RDD estimate delivered as the Wald ratio, jump in 12-month serious delinquency divided by a first-stage jump in approval probability of about 0.65, fitted by local linear regression with a triangular kernel at an MSE-optimal bandwidth, with robust bias-corrected intervals and standard errors clustered on the integer score. It is reported as a local average treatment effect for compliers at 660 only, accompanied by the density test, the covariate-continuity table and the bandwidth sensitivity, with an explicit statement that it does not support moving the cutoff.

Verify it yourself

Reduced form divided by first stage must equal the two-stage least squares coefficient to numerical precision; if it does not, the sample or the bandwidth differs between the two fits.
Placebo cutoffs at 650 and 670 should show no jump in either the first stage or the outcome.
Pre-determined covariates should be continuous at 660; a jump in income or channel mix at the threshold means applicants or brokers are sorting and the design is compromised.
The point estimate should be stable when the bandwidth is halved and doubled; large movement means the local linear fit is being driven by observations far from the cutoff.

Follow-up

Manual reviewers below the cutoff see documents the score does not. What does that do to the monotonicity assumption, and how would you look for defiers?
You are offered a randomised approval band across 650 to 659 for one quarter. What does it buy you that the discontinuity does not, and how would you price the expected loss of running it?
The density test shows a modest pile-up just above 660. What are the possible mechanisms, and which of them leave the design usable?

Settled volume jumps after a new acceptance corridor launches

Medium · minor units · currency conversion · reconciliation

Settled payment volume per active customer rose 12 percent in the month a new acceptance corridor went live. The pipeline sums settlement_amount_minor from fct_payment_authorization and divides by 100 to get major units. Columns available: amount_minor, transaction_currency, captured_amount_minor, settlement_amount_minor, settlement_currency, settlement_fx_rate, settled_at, is_reversal, issuer_country, acquirer_country. Confirm or refute the 12 percent, and specify exactly the conversion the metric should be using.

Approach

Split the month-over-month increase by settlement_currency. If one currency carries nearly all of it while its transaction count barely moved, the problem is arithmetic rather than demand, and that is a two-minute check.
Look up each currency's ISO 4217 exponent. Dividing a three-decimal currency by 100 instead of 1000 overstates it tenfold; dividing a zero-decimal currency by 100 understates it hundredfold. Neither is a rounding issue.
Rewrite the conversion to scale settlement_amount_minor by ten to the power of that currency's exponent, then convert to the reporting currency. Do not reuse settlement_fx_rate for this; it is the rate applied between transaction and settlement currency at settlement time, not a reporting-currency rate.
Check the other half of the series too. settlement_amount_minor is denominated in settlement_currency while amount_minor is in transaction_currency, so any series that mixes the two is uninterpretable regardless of the exponent fix.
Exclude reversals and refunds, recompute, then reconcile the corrected total to the settlement ledger for the same period and show the tie-out in the write-up.
Recompute the per-customer denominator on the corrected month, since a new corridor adds customers as well as volume and the ratio can move either way once the numerator is right.

Follow-up

Where else in the warehouse does a hardcoded divide-by-100 appear, and how would you find every instance?
What test would fail the build the next time a currency with a different exponent is onboarded?
The reporting-currency rate is itself a choice. Which rate, on which date, and why does finance care about the answer?

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.

What the behavioural round is actually scoring

Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.

Tell me about a time you identified a flaw in a model or experiment design; how did you handle it?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

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

Easy · scoping · metric definition · denominators

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

Approach

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

Follow-up

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

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?