At Marshmallow, the Data Scientist role is not just about building models; it is about fundamentally rethinking how insurance works. You will be joining a mission-driven team that leverages technology to provide fairer, more inclusive access to insurance products. Your work will directly influence Marshmallow's pricing models, risk assessment strategies, and the overall customer experience for thousands of people who have been historically underserved by traditional insurers.
This position sits at the intersection of complex data, actuarial science, and product innovation. You will be tasked with transforming raw, messy data into actionable insights that drive business strategy and operational efficiency. Because Marshmallow operates in a highly regulated and data-intensive industry, your ability to balance technical rigor with business outcomes is what will set you apart. You will collaborate closely with engineering, product, and operations teams to deploy models that are both performant and ethical, ensuring that Marshmallow's growth is built on a foundation of sound data science practices.
Initial Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Deep-Dives
reportedA 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
Live Coding Session
reportedMuch 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
Case-Study Session
reportedBecause the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.
What to demonstrate
- Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
- Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
- Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs
How to prepare
- For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
- Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
- For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.
Behavioral Interview
reportedBehavioural 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.
Final Assessment
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.
Assuming a model is fair because protected attributes are not among its inputs
Postcode, device, tenure, income proxies and even transaction patterns correlate with protected characteristics, so a model can produce a disparate outcome without ever reading the attribute. Credit decisions additionally carry an explainability obligation in many jurisdictions, since a denial has to be accompanied by its principal reasons, which constrains model form and feature engineering rather than being a reporting afterthought. Treating fairness testing and reason-code generation as design constraints from the first model version is far cheaper than retrofitting them to a deployed one.
Reading the most recent months of fraud and dispute rates as final
Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.
Extrapolating a first-week lift inflated by novelty effects
Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.
Naming a model class before naming the deployment constraints
Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you prioritize between model accuracy and model interpretabilit…
How do you prioritize between model accuracy and model interpretability?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
How would you design a pricing model for a new market segment with lim…
How would you design a pricing model for a new market segment with limited historical data?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- What would you monitor after launch to know the model is still valid?
How do you measure the performance of a model that has a long feedback…
How do you measure the performance of a model that has a long feedback loop?
Approach
- 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.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Estimate a delinquency roll-rate matrix and project twelve months
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.
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?
Reconcile captured authorizations against the daily settlement total
fct_payment_authorization holds captured_amount_minor in transaction_currency, and settlement_amount_minor in settlement_currency with settlement_fx_rate applied at settlement rather than at authorization. The rate is quoted in major units of settlement_currency per major unit of transaction_currency, and dim_currency.minor_unit_exponent carries the ISO 4217 exponent for each code (0, 2 or 3 depending on the currency). Produce a daily reconciliation: for each settled_at date and settlement_currency, return settled_count, total settlement_amount_minor, and the sum of captured_amount_minor converted into settlement minor units. Flag any date and currency pair whose two totals differ by more than one minor unit per settled authorization. Do not sum amounts across currencies anywhere in the output.
Approach
- Restrict to rows that actually settled: settled_at is not null and settlement_amount_minor is not null, which is a smaller population than captured rows because a capture can still be in flight.
- Truncate settled_at to a date with an explicit time zone so the cut matches the ledger's cut, since settled_at is timestamptz and date_trunc on timestamptz silently uses the session time zone.
- Join dim_currency twice, once on transaction_currency and once on settlement_currency, so both exponents are on the row. Minor units are not a common scale: a bare captured_amount_minor * settlement_fx_rate is correct only when the two exponents are equal, and a zero-decimal currency settling into a two-decimal one is wrong by a factor of 100.
- Convert per row as ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle)) — minor units to major in the transaction currency, apply the major-per-major rate, then back to minor units in the settlement currency. The collapsed form ROUND(captured_amount_minor::numeric * settlement_fx_rate * POWER(10::numeric, exp_settle - exp_txn)) is the same expression. Round per row and then sum, not SUM(...) * an average rate, because the rate varies row by row and rounding per row is what the settlement file did.
- Group by the settlement date and settlement_currency together, never by date alone, and carry the currency into every output column name or row.
- Compare the two totals with a tolerance scaled by settled_count, since per-row rounding accumulates linearly in the number of rows rather than being a fixed constant.
Follow-up
- A partial capture means captured_amount_minor is less than amount_minor. Where does that show up in this reconciliation, and where does it not?
- On one currency pair the converted total is consistently about one hundredth of the settlement total, on every date, while the other pairs reconcile. Which two columns do you inspect first, and what single change fixes it?
- The rate is documented as major-per-major. If a feed started publishing it minor-per-minor instead, which pairs would still reconcile and which would break?
- How would you present a total across currencies to a finance partner who has asked for one number?
Attribute chargebacks to transaction month without fanning out
Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.
Approach
- Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
- Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
- Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
- Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
- Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.
Worked solution 30 min
- CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
- CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
- LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
- Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.
Follow-up
- Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
- Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
- How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?
If our churn rate increases unexpectedly, how would you go about diagn…
If our churn rate increases unexpectedly, how would you go about diagnosing the root cause?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- 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?
Walk us through a project where your initial hypothesis was proven wro…
Walk us through a project where your initial hypothesis was proven wrong by the data.
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 handle imbalanced datasets in a fraud detection context?
How would you handle imbalanced datasets in a fraud detection context?
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Work from the decision backwards to the evidence you would need.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Design an authorization approval rate a team cannot game
A payments team is given authorization approval rate as its quarterly target, defined as approved authorizations over all authorizations in fct_payment_authorization. Using auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, auth_result, is_reversal, parent_auth_id, channel, issuer_country and requested_at, list three ways the team can raise that number without a single additional customer purchase succeeding. Then write the definition you would put on the dashboard instead, with its exclusions, and name the one guardrail you would always read beside it.
Approach
- Enumerate the rows in the table that are not purchases: zero-amount account verifications, reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and merchant retries of a declined attempt. Each is a lever on the numerator or the denominator that no customer would ever notice.
- Name the mix levers. Recurring and card-present traffic approve at higher rates than ecommerce and cross-border, so routing changes or a marketing push into an easy channel move the blended rate with no change in decisioning quality at all.
- Work the retry arithmetic rather than asserting it. A declined purchase that is retried three times and succeeds on the fourth contributes one approval and four denominator rows, which is below a healthy blended rate, so suppressing retries raises the published number while some purchases that would have succeeded on a retry now simply fail.
- Replace the definition: dollar-weighted, retry chains collapsed to a single attempt per (card_token_id, merchant_id, amount_minor) within a 15-minute window, excluding reversals, zero-amount verifications and incremental authorizations, with all amounts converted into one reporting currency before summing because amount_minor is in the minor unit of transaction_currency and that unit is not always two decimals. Cut by channel and issuer_country so mix shift shows up as mix shift.
- Guardrail: net fraud loss in basis points of settled volume, read only on transaction months with at least 120 days of dispute maturity. The fastest way to approve more is to decline less risk, and that bill arrives two to four months later.
Worked solution 25 min
- Compute the naive rate for one week: approved rows over all rows.
- Apply the exclusions one at a time (zero-amount, is_reversal, parent_auth_id not null), recording the rate after each, so you can say what each row type was worth to the headline number.
- Add a retry-collapsing CTE keyed on (card_token_id, merchant_id, amount_minor) over a 15-minute window, keeping the last attempt in each chain, and recompute.
- Recompute dollar-weighted in a single reporting currency, then split by channel and issuer_country.
- Write the final one-line definition with its exclusions, plus the guardrail sentence.
Follow-up
- The blended rate is flat but ecommerce cross-border fell 200 basis points. How do you present that?
- Why weight by amount rather than by count, and when does the weighted version mislead?
- Your 15-minute retry window merges two genuine purchases of the same amount at the same merchant. How would you bound that error?
Portfolio delinquency improving while the loan book doubles
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?
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 ↗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.
How do you handle disagreements with product managers regarding model …
How do you handle disagreements with product managers regarding model deployment timelines?
Approach
- Close with what you would do differently, concretely.
- Name the disagreement or constraint, and how you resolved it with evidence.
- 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?
Describe a time you received constructive feedback on your code or mod…
Describe a time you received constructive feedback on your code or model.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
- Close with what you would do differently, concretely.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Defend a vintage finding that contradicts the portfolio dashboard
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
Approach
- Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
- Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
- Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
- Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
- Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.
Follow-up
- Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
- Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?
- 01
How do you handle disagreements with product managers regarding model deployment timelines?
- 02
Describe a time you received constructive feedback on your code or model.
- 03
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
Is this an official Marshmallow interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Marshmallow. Rounds and questions reflect what candidates have reported, not a process Marshmallow has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
They are designed to be practical, not academic. Candidates report that the questions focus on problems you would actually encounter on the job rather than obscure trivia.
PracHub interview research ↗Is the culture collaborative or competitive?
Marshmallow is highly collaborative. Candidates describe a culture of open debate and shared ownership of problems.
PracHub interview research ↗What is the typical timeline for the interview process?
From the initial screen to the final decision, most candidates complete the process within 3–4 weeks, depending on availability.
PracHub interview research ↗Do I need to be an expert in insurance?
No. Candidates report that domain knowledge is a bonus, while foundational data science skills and the ability to learn quickly carry more weight.
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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