As a Data Scientist at Plymouth Rock Assurance, you play a pivotal role in shaping the analytical landscape of the organization. Your expertise in statistical modeling, machine learning, and data analysis directly influences the company's strategic decisions and enhances its insurance products. The insights you glean from data not only help in risk assessment and pricing strategies but also in tailoring customer experiences, thereby driving business growth and improving client satisfaction.
In this role, you will collaborate with diverse teams, including product development, marketing, and operations, to address complex challenges and harness data for actionable insights. This position is critical as it contributes to the company’s mission of providing innovative insurance solutions. Whether you are enhancing predictive models or analyzing customer behavior, your contributions will have a tangible impact on both the organization and its customers.
Expect to work on a variety of projects, from optimizing underwriting processes to developing tools that facilitate better decision-making. The complexity and scale of the data you handle will provide a stimulating environment for professional growth. As a, you will be at the forefront of data-driven initiatives, making this a uniquely rewarding opportunity.
Initial Screening Interview
reportedData Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.
What to demonstrate
- Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
- Whether you name what you have not done instead of stretching to cover every line of the posting
- Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage
How to prepare
- Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
- Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
- Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
In-Depth Technical Interview
reportedThis 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
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.
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.
Writing SQL without stating NULL and tie-breaking behaviour
Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.
Never asking what decision the analysis will inform
Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you explain the concept of p-value and its significance?
Can you explain the concept of p-value and its significance?
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- Write down the assumption the method needs before you use the method.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Explain how to implement a decision tree from scratch.
Explain how to implement a decision tree from scratch.
Approach
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- What would you monitor after launch to know the model is still valid?
- How would you choose the decision threshold, and who owns that choice?
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?
Solve a LeetCode-style problem related to data structures.
Solve a LeetCode-style problem related to data structures.
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.
- Say which table is the grain you start from, and join outward from it.
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 function to calculate the factorial of a number.
Write a function to calculate the factorial of a number.
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?
- How does the query change if the join becomes one-to-many?
Monthly delinquency roll rates with honest exiting-account denominators
From fct_loan_performance_monthly, compute month-over-month roll rates: for each as_of_month_end and delinquency_bucket, the share of loans in that bucket that are in a worse state at the following month end. Use this disposition convention and apply it without exception: charge-off is the terminal state one rank past dpd_90_plus, so a loan that charges off has rolled forward, not exited; loans that leave the book any other way — prepaid in full, sold, matured, or simply absent at the next month end — are exited. Bucket order is current, dpd_1_29, dpd_30_59, dpd_60_89, dpd_90_plus, then charged_off. Return from_month, from_bucket, accounts, rolled_forward, cured, stayed, exited and roll_rate, and show that the four dispositions sum to accounts on every row.
Approach
- Map the five delinquency_bucket values to ranks 0 through 4 in a small inline VALUES list, and reserve rank 5 for charged_off, because 'worse' is an ordering statement and enum text does not sort that way reliably. Read charge-off from charge_off_flag on the next month's row rather than from delinquency_bucket, which keeps carrying whatever delinquency value the loan held when it was written off.
- Use LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) OVER (PARTITION BY loan_id ORDER BY as_of_month_end), then verify the leading month end is exactly one month after the current one, since a loan missing a month would otherwise appear to cure or roll across a gap it never traversed.
- Classify each loan-month into exactly one of four labels off the adjacent next row, whose rank is 5 when its charge_off_flag is true and its mapped bucket rank otherwise. rolled_forward: the next rank is strictly greater. cured: strictly lower. stayed: equal. exited: there is no adjacent next row at all, which is where prepayment, sale, maturity and a gap in the monthly history land.
- Define rolled_forward as 'any strictly worse rank', not 'rank plus one'. Under normal monthly aging the only reachable worse state is the next bucket, so the two definitions coincide — except at charge-off, where a loan can go from dpd_60_89 straight to rank 5. A strict plus-one test leaves those loan-months matching no branch and breaks the exhaustiveness invariant.
- Be explicit about why charge-off is a roll rather than an exit: routed to exited, dpd_90_plus has no worse state left, so its roll_rate is identically zero by construction — arithmetic, not a credit finding. At rank 5 the dpd_90_plus row reports roll-to-loss, which is the number a loss forecast actually consumes.
- Aggregate by from_month and from_bucket and require that rolled_forward + cured + stayed + exited equals accounts on every row, which is the invariant that proves the classification is exhaustive and mutually exclusive.
Worked solution 45 min
- CTE ranks: an inline VALUES mapping of the five delinquency_bucket values to ranks 0 through 4, joined onto the monthly rows. Rank 5 is not in the map; it is assigned during classification.
- CTE leads: add LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) partitioned by loan_id ordered by as_of_month_end.
- CTE classified: derive next_rank as CASE WHEN next_charge_off_flag THEN 5 ELSE next_bucket_rank END, then a CASE expression producing exactly one label per loan-month — exited when the next month end is null or is not the current month end plus one month, otherwise rolled_forward / cured / stayed by comparing next_rank to bucket_rank.
- Aggregate to from_month and from_bucket with COUNT(*) and four FILTER counts, then compute roll_rate as rolled_forward over accounts with a numeric cast.
- Assert the exhaustiveness invariant in a final HAVING or a separate verification query before trusting any number.
Follow-up
- Project the next three months of dpd_90_plus inflow from these roll rates. What assumption does that projection make, and when does it break?
- A restructure resets days_past_due to zero. What does that do to the cure rate out of dpd_60_89, and how would you separate a real cure from a reset?
- Argue the other convention: prepayment in exited but charge-off also in exited, with roll rates reported only for the four non-terminal buckets. What does that series answer better, and what does it lose?
- The charge-off policy changed from 180 to 120 days. Show where that appears in this table and what it does to the dpd_90_plus roll rate specifically.
Discuss how you would approach estimating the lifetime value of a cust…
Discuss how you would approach estimating the lifetime value of a customer.
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?
Given a dataset of customer complaints, how would you approach identif…
Given a dataset of customer complaints, how would you approach identifying the root cause of dissatisfaction?
Approach
- Fix the population and the time window before naming any metric.
- Restate the decision this analysis has to support, and who acts on the answer.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
If tasked with reducing churn rates, what data would you analyze and w…
If tasked with reducing churn rates, what data would you analyze and why?
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.
- 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?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How do you prioritize tasks when faced with multiple deadlines?
How do you prioritize tasks when faced with multiple deadlines?
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.
- 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?
How would you design an A/B test for a new insurance product?
How would you design an A/B test for a new insurance product?
Approach
- Say whether units interfere with each other, and switch design if they do.
- Decide the analysis before seeing data, including how long it runs and when you look.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
Explain the difference between supervised and unsupervised learning.
Explain the difference between supervised and unsupervised learning.
Approach
- Clarify what is being asked and what a complete answer would contain.
- Work from the decision backwards to the evidence you would need.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Diagnose a sample ratio mismatch before reading the result
A two-week test of a new risk rule logged 512,340 exposures in treatment and 508,110 in control against an intended 50/50 split. The dashboard reports a 1.8 percent relative lift in the count-weighted authorization approval rate, p equals 0.004. Exposures are written by the events pipeline at the point the rule is evaluated. Before anyone reads the lift, test whether the split is consistent with 50/50, state your verdict, rank the mechanisms that would produce this imbalance, and say what you do with the two weeks of data.
Approach
- Run the test rather than eyeballing the ratio: a chi-square goodness-of-fit on one degree of freedom against the expected 50/50, or equivalently a binomial test on the treatment share. A 0.42 percent imbalance looks trivial and is not, because at a million exposures the null distribution is extremely tight.
- Declare the result uninterpretable rather than adjusting it. An SRM means units are missing from one arm conditional on something, and the something is usually correlated with the outcome, so reweighting or trimming does not restore exchangeability; it just hides the selection.
- Localise the mismatch before hypothesising about it: recompute the split by day, by channel, by issuer_country and by whether the rule actually fired. An SRM confined to one slice points straight at the code path that produced it.
- Rank mechanisms by how often they are the cause here. First, exposure logged downstream of a step the treatment changes, so any arm-specific drop-off before the log line silently deletes units. Second, assignment keyed on something mutable or nullable, such as card_token_id across a reissue or a null customer_id on guest traffic. Third, bot or fraud filtering applied after assignment and hitting arms unequally. Fourth, retry rows deduplicated after assignment rather than before. Fifth, a ramp or a rollback that moved mid-test.
- Close the loop with an A/A run on the fixed pipeline before rerunning the A/B, and add a continuous SRM check with an alert threshold so the next occurrence is caught on day one instead of at readout.
Worked solution 15 min
- Total exposures N = 1,020,450, so expected per arm E = 510,225 and the absolute deviation is 2,115 in each direction.
- Chi-square = 2 * 2115^2 / 510225 = 2 * 4,473,225 / 510,225 = 2 * 8.767 = 17.53 on one degree of freedom.
- Convert: sqrt(17.53) = 4.19 standard deviations, so p is about 3e-5. The observed treatment share is 0.50208 against a null standard error of 0.000495, which is the same statement in another form.
- Declare SRM, withhold the 1.8 percent lift from any decision, and produce the per-day and per-channel split table that localises the cause.
Follow-up
- Suppose the mismatch is entirely in one issuer_country and the treatment adds a 3-D Secure step there. What is the most likely code path, and what does that imply about the sign of the observed lift?
- What SRM alert threshold would you set for a daily check, and how do you keep it from firing constantly across many concurrent tests?
- If the split is exactly 50/50 but the two arms have different distributions of mcc and channel, is that an SRM? What is it, and what do you do about it?
Fraud losses appear to halve in recent transaction months
A weekly chart attributes fct_card_dispute cases to the requested_at month of the linked fct_payment_authorization row. The two most recent months show the first-chargeback rate falling by half, and a risk rule shipped six weeks ago. Columns: dispute_id, auth_id, dispute_category, dispute_stage, opened_at, disputed_amount_minor, liability_shift_flag, outcome, net_loss_minor, resolved_at. Decide whether the rule worked, and produce the version of the chart you would sign your name to.
Approach
- Separate the two dates explicitly. opened_at is when a case was filed, requested_at is when the transaction happened. Attributing by transaction month is the right causal choice and is exactly what makes the newest months structurally incomplete.
- Measure the filing lag rather than assuming it: the distribution of opened_at minus requested_at over fully developed months, split by dispute_category, and the age at which around 95 percent of cases have arrived.
- Build a development triangle of transaction month by months of development on cumulative case counts, and estimate age-to-age factors from the columns that are complete.
- Develop the immature months with those factors and plot the result as an estimate with a visible band, kept visually distinct from the matured series rather than blended into it.
- State the assumption the method needs: a stable development pattern across cohorts. A change in filing behaviour, merchant mix or the dispute team's own backlog breaks it, so inspect factor stability down each column before relying on the estimate.
- Only then evaluate the rule, comparing pre-change and post-change cohorts at equal development age.
Follow-up
- What leading indicator would you accept while the cohort matures, and what is its known bias?
- How does liability_shift_flag change which disputes you should expect to see in the first place?
- If the rule also blocked good transactions, where does that cost appear, and is any of it in this chart?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.
Describe a challenging data project you worked on. What was your appro…
Describe a challenging data project you worked on. What was your approach?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Explain an incomplete dispute chart to a non-technical executive
A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.
Approach
- Lead with the mechanism in the listener's own terms, not with the statistical name for it: a dispute is attributed to the month the transaction happened, but it can be filed up to roughly 120 days later, so recent months contain only the disputes filed so far.
- Show completeness rather than arguing about the rate: for each transaction month, plot the share of its eventual disputes already filed, estimated from months that are fully matured. The last three months will sit visibly below 100 percent.
- Replace the chart with two artefacts: a matured series that stops 120 days back and is labelled final, and a development-factor estimate for the immature months drawn as a dashed range and labelled an estimate.
- Hand over one sentence the executive can repeat without you in the room: the recent months look better because the disputes have not arrived yet, not because fewer will arrive.
- Offer a weekly signal they can watch instead, such as the risk-score mix of approved volume or the decline-rule hit rate, and state up front what it does and does not predict.
Follow-up
- The deck ships tomorrow regardless. What exactly goes on the slide, and what wording do you insist on?
- How would you estimate the development factors, and how would you notice if they had shifted?
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?
- 01
Describe a challenging data project you worked on. What was your approach?
- 02
A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.
- 03
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.
Is this an official Plymouth Rock Assurance Corporation interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Plymouth Rock Assurance Corporation. Rounds and questions reflect what candidates have reported, not a process Plymouth Rock Assurance Corporation has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical interview difficulty for this role?
Expect a moderate level of difficulty, with a focus on both technical and behavioral aspects. Preparation in statistics and coding will be essential.
PracHub interview research ↗How much preparation time should I anticipate?
Candidates often find that dedicating several weeks to review key concepts and practice coding challenges is beneficial.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate a deep understanding of data science principles, articulate their thought processes clearly, and effectively communicate their findings.
PracHub interview research ↗What is the culture like at Plymouth Rock Assurance?
The company values teamwork, innovation, and a customer-centric approach. Collaboration across teams is encouraged, fostering an inclusive environment.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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