At Koalafi, the Data Scientist role sits at the critical intersection of advanced analytics, risk management, and business strategy. As a high-growth fintech company specializing in consumer finance and lease-to-own solutions, Koalafi relies on its data team to make precise, real-time decisions regarding credit risk, underwriting, and merchant partnerships. Rather than focusing solely on building isolated, highly complex machine learning models, data scientists here are expected to drive tangible business outcomes by translating data into actionable financial strategies.
This position is highly impactful because your insights directly influence the company's revenue growth, portfolio health, and customer acquisition strategies. You will work closely with product, engineering, and operations teams to design frameworks that evaluate consumer risk, optimize pricing structures, and improve the overall efficiency of the lending platform. The challenge lies in balancing mathematical rigor with commercial pragmatism—ensuring that the models and analytical tools developed can survive and thrive in dynamic market conditions.
Koalafi's Data Scientist role leans heavily toward business analytics and credit risk strategy. Expect a strong focus on business cases rather than pure machine learning engineering.
Recruiter Screen
reportedA 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
Hiring Manager Interview
reportedExpect a live problem with pieces of it missing, closer to a conversation than an exam. A metric moved, or somebody wants to know whether a change worked, and you are asked how you would find out. The manager is watching the first ninety seconds, specifically whether you establish what decision hangs on the answer before you start proposing methods. Candidates who open with a technique get steered back. Once the decision is clear, describe what the data would look like if the story were true, and say what you would accept as evidence that it is not.
What to demonstrate
- Whether you fix the decision the analysis serves before choosing an approach
- How you continue when you are told the data you just asked for does not exist
- Whether you state what would change your mind, not only what would confirm the hypothesis you started with
- How you size an effect before you have measured it
How to prepare
- Take a metric you know well and practise explaining in under two minutes the four things that could have moved it and how you would separate them
- Pick a recent launch or experiment and write the single number you would ask for first, plus what you would conclude if it came back flat
- Practise being interrupted: have someone remove a data source halfway through your answer and carry on without restarting
Take-Home Assignment
reportedYour submission is read asynchronously by someone who cannot ask you a clarifying question, and who will usually skim it before reading it properly. That changes what good looks like. The conclusion belongs near the top with the supporting analysis beneath it, and the code should run start to finish on a clean machine without a manual step you forgot to document. A reviewer forced to reconstruct your reasoning from the order of notebook cells is already discounting the work. The submissions that land are the ones where a busy reader gets the answer immediately and can verify it if they want to.
What to demonstrate
- Whether the answer arrives before the methodology, so a reader who stops after the first page still has the recommendation
- Whether the code runs end to end from the submitted files, with dependencies and data paths declared rather than assumed
- Whether each chart is legible on its own, carrying axis labels and units, and exists to support a claim made in the text
How to prepare
- Restructure a past analysis so the opening paragraph holds the recommendation and the number behind it, then check that nothing later in the document quietly contradicts it
- Copy your own submission into an empty directory, run it in a clean environment, and fix everything that breaks; hidden local state is caught here or by the reviewer
- For every chart, write the one sentence it is meant to prove, and delete the chart if you cannot write that sentence
Super Day
reportedWhere a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.
What to demonstrate
- Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
- Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
- Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
- Whether you ask what decision is actually on the table before explaining anything
How to prepare
- Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
- For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
- Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else
PracHub editorial advice for the preparation topics above.
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.
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.
Reporting a mean for a heavy-tailed metric without saying what it hides
For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.
Reading experiment results before checking the arm split
Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Solve a probability puzzle: If a consumer has a certain baseline proba…
Solve a probability puzzle: If a consumer has a certain baseline probability of defaulting, how does that probability change when we introduce a positive payment history indicator?
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
- Write down the assumption the method needs before you use the method.
Follow-up
- Which assumption here is most likely to be violated in practice?
- What sample size would you need to detect an effect half this size?
Walk me through the mathematical concept behind a logistic regression …
Walk me through the mathematical concept behind a logistic regression model. How do you interpret the coefficients in a business context?
Approach
- Set a baseline first, so any model has something honest to beat.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- Where could label leakage enter this setup?
- What would you monitor after launch to know the model is still valid?
Build a vintage delinquency table without pivot or unstack
fct_loan_performance_monthly gives loan_id, origination_month, months_on_book, days_past_due, charge_off_flag and restructured_flag. Produce a DataFrame with one row per origination_month and columns for months_on_book 0 through 12, each cell holding the share of that vintage's funded loans that had ever reached 90 or more days past due, or charge-off, by that age. You may not use pivot, pivot_table, crosstab or unstack. Cells for ages a cohort has not yet reached must be NaN rather than zero.
Approach
- Define the per-row indicator as days_past_due >= 90 or charge_off_flag, then take a cumulative maximum of it per loan ordered by months_on_book, because the metric is reached-by-age-m, not in-that-state-at-age-m.
- Deal with restructuring before the cumulative max. Restructuring resets days_past_due, so a restructured loan re-enters at current and, without the cumulative maximum carrying its pre-restructure worst state, reads as a cure.
- Fix the denominator once as the count of distinct loan_id per origination_month across the whole cohort. Prepaid and charged-off loans stop producing rows, so a denominator recomputed at each age silently shrinks exactly where losses land.
- Aggregate with groupby(['origination_month','months_on_book'])['ever_90'].sum(), then pre-build the output frame indexed by sorted origination months with integer columns 0 to 12 and assign from the grouped Series by .loc on its index.
- Mask cells beyond each cohort's maximum observed months_on_book so an immature cell reads NaN instead of an artificially low rate.
Worked solution 30 min
- Sort by loan_id and months_on_book, build the ever_90 indicator, then apply groupby('loan_id')['ever_90'].cummax().
- Compute cohort_size as the distinct loan_id count per origination_month, before any filtering on age.
- Group the cumulative indicator by origination_month and months_on_book and sum it to get the numerator per cell.
- Create the output frame with the sorted origination months as index and range(0, 13) as columns, fill it from the grouped Series, and divide each row by its cohort_size.
- Compute each cohort's maximum observed months_on_book and set every cell to the right of it to NaN.
Follow-up
- Two adjacent vintages diverge at months_on_book 6. How would you separate seasoning, mix shift and a genuine credit-quality change?
- The three most recent vintages look best on this table. What do you check before saying so?
- How does the table change if charge-off policy moved from 180 to 120 days past due partway through the series?
Vintage ninety-plus rate at twelve months on book
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.
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?
Count-weighted and dollar-weighted approval rates on one currency
Using fct_payment_authorization, report the trailing 7-day authorization approval rate two ways for transaction_currency = 'EUR': count-weighted, and dollar-weighted on amount_minor. Exclude is_reversal = true, exclude incremental authorizations (parent_auth_id not null), and exclude zero-amount account verifications. auth_result = 'approved' is the numerator; the four declined_* values make up the rest of the denominator. Return channel, attempts, approved_attempts, approval_rate_count and approval_rate_value. State every exclusion and its reason before you write the SELECT.
Approach
- Say the denominator out loud first: attempts on a single transaction currency, excluding reversals, incremental authorizations and zero-amount verifications, because none of those is a purchase attempt a merchant is trying to get approved.
- Filter requested_at against a half-open interval (>= start AND < end) so the boundary day is neither dropped nor double counted.
- Compute both rates in one pass with FILTER clauses: COUNT() FILTER (WHERE auth_result = 'approved') over COUNT(), and SUM(amount_minor) FILTER (WHERE auth_result = 'approved') over SUM(amount_minor).
- Cast one side of each ratio to numeric before dividing, since amount_minor and the counts are integers and integer division silently truncates to zero.
- Group by channel and sort by the value-weighted rate, then read the gap between the two rates as a statement about where the declines sit rather than as noise.
Worked solution 20 min
- Write the exclusion list as comments above the query: is_reversal = false, parent_auth_id is null, amount_minor > 0, transaction_currency = 'EUR'.
- Build a single aggregate query over fct_payment_authorization with a half-open requested_at predicate and those four filters.
- Emit attempts, approved_attempts, approval_rate_count and approval_rate_value with FILTER clauses and a numeric cast on the numerator.
- Group by channel, order by approval_rate_value ascending so the worst channel is on top.
Follow-up
- The two rates diverge by four points on the ecommerce channel but agree on card_present. What does that tell you, and what would you cut next?
- How would you extend this to all currencies without summing amount_minor across them?
- Which of the four decline reasons belong in the denominator of a rate you would put in front of a risk team, and which are really the network's problem?
Imagine a scenario where a merchant partner's transaction volume sudde…
Imagine a scenario where a merchant partner's transaction volume suddenly drops. Walk me through how you would diagnose the root cause using data.
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- 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.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How would you evaluate the credit risk of a consumer who has a limited…
How would you evaluate the credit risk of a consumer who has a limited or non-traditional credit history?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- 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.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How would you measure the trade-off between increasing approval rates …
How would you measure the trade-off between increasing approval rates and managing the resulting default rates?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- 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 do you prioritize your work when faced with multiple urgent reques…
How do you prioritize your work when faced with multiple urgent requests from different business units?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
Explain how you would set up an A/B test to evaluate a new underwritin…
Explain how you would set up an A/B test to evaluate a new underwriting algorithm. How do you determine the sample size and statistical significance?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- 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.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Price a false decline when the label does not exist
Your expected-cost threshold needs a figure for what a false decline costs beyond the margin on the blocked transaction. fct_payment_authorization records the decline, nothing records what the customer did next elsewhere, and declined transactions never produce a fraud outcome, so neither side of the error is directly observable. Propose the measurement: the proxy you would build from the tables you have, the design that yields an unbiased estimate for at least part of the score range, the bias in each, and the single sentence you would attach to the number when it reaches a pricing decision.
Approach
- Write down what is unobservable and why. The counterfactual spend of a customer who was not declined, and the fraud label on any transaction the rule blocked, are both missing because of the decision itself. Missingness that depends on the decision is not fixed by matching on observed covariates.
- Build the observational proxy anyway and be specific. For customers receiving a first risk-rule decline in a window, compare settled volume and active status over the following 30 and 90 days against customers matched on pre-period settled volume, tenure, segment and channel mix who attempted a comparable transaction and were approved. The bias runs one way: matching conditions on having attempted something that scored near the cutoff, and part of the declined group are genuine fraudsters whose disappearance is a saving rather than a loss, so the estimate overstates the damage.
- Buy one unbiased local estimate. Hold a small random share of authorizations inside a defined risk_score band out of the decline rule and approve them, sizing the sample in advance from the expected fraud rate in that band so the cost of the experiment is known before it runs. The result is unbiased for that band only, and it is simultaneously the only source of fraud labels in the declined region.
- Bound the extrapolation instead of hiding it. Run the holdout in two or three adjacent bands and report the spread. If the effect is flat across bands a constant is defensible; if it is steep, quote band-specific figures and decline to supply a single number.
- Handle the window. Attrition after a decline can resolve over months, so a 90-day window truncates it and the randomised estimate is a lower bound on long-run damage at the same time as the observational version is an upper bound. Stating both directions is what makes the number safe to use.
- Write the sentence that travels with the number: what it is (an estimate from a randomised holdout in one score band over a 90-day window), what it is not (a measurement anywhere else on the score range), and which way it is likely to be wrong.
Worked solution 40 min
- Define the decline cohort and the matched comparison cohort precisely, including matching variables and the pre-period window, and produce the 30-day and 90-day settled-volume difference.
- Decompose the declined cohort into customers who never transact again and customers who transact less, since fraudsters concentrate in the first group and that split tells you how much of the estimate is contamination.
- Write the holdout design: score band, sample share, expected fraud rate in band, expected cost of running it, and the run length needed to detect an effect large enough to change the cutoff.
- Recompute p* = C_FP / (C_FP + C_FN) at the top and bottom of your estimated cost range and state whether the range changes the cutoff you would set.
- Write the one-sentence caveat that will be quoted alongside the number in the pricing decision.
Follow-up
- Compliance and finance both object to deliberately approving transactions you believe are fraudulent. What is your answer, and how do you size the holdout?
- Your interval spans the decision boundary. What do you recommend?
- How would you detect that this number has gone stale?
Settled volume jumps after a new acceptance corridor launches
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?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Diagnostic, 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Largest 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.
Practice prompt ↗Practice prompt ↗03Largest 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.
Practice prompt ↗Practice prompt ↗04Second 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗05The 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.
Practice prompt ↗Practice prompt ↗06Retest 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.
Practice prompt ↗Practice prompt ↗07Full 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
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.
Tell me about a time when you disagreed with a product manager or busi…
Tell me about a time when you disagreed with a product manager or business leader regarding a data-driven decision. How did you resolve the conflict?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that project again?
State honestly what your cutoff change actually contributed
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Approach
- Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
- Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
- Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
- Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
- State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.
Follow-up
- The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
- What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?
Retract a published number after finding a currency bug
Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.
Approach
- Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
- Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
- Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
- Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
- Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.
Follow-up
- The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
- What automated check would have caught this, where would it live, and what would it cost in false alarms?
- 01
Tell me about a time when you disagreed with a product manager or business leader regarding a data-driven decision. How did you resolve the conflict?
- 02
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
- 03
Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.
Is this an official Koalafi interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Koalafi. Rounds and questions reflect what candidates have reported, not a process Koalafi has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical is the Data Scientist interview at Koalafi?
The interview is highly analytical but focuses more on mathematical logic, probability, and business case-solving than on software engineering or deep learning architectures. You will need to demonstrate strong SQL and statistical knowledge, but your ability to solve business-oriented math problems is the primary differentiator.
PracHub interview research ↗What is the hybrid work policy for this position?
The role typically requires candidates to be onsite 2 days a week at one of the company's corporate locations, such as Richmond, VA or Arlington, VA. Candidates should clarify the specific location expectations with their recruiter early in the process.
PracHub interview research ↗How should I prepare for the case study portion of the interview?
Focus on consumer finance fundamentals. Understand how lending companies generate revenue, manage credit risk, and evaluate portfolio health. Practice structuring ambiguous problems and explaining your logical process step-by-step.
PracHub interview research ↗What is the typical timeline for the hiring process?
The process can take anywhere from three to six weeks from the initial recruiter screen to the final offer. Because of the intensive nature of the take-home assignment and the coordination required for the Super Day, candidates should expect a thorough and deliberate evaluation.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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