Interview guides · Blue Cross and Blue Shield of Kansas

Blue Cross and Blue Shield of Kansas Data Scientist Interview Questions & Guide 2026

Causal inference drills for Blue Cross and Blue Shield of Kansas data scientist prep: diff-in-diff, matching, instruments, and the limits of observed data.

The 60-second brief

As a Data Scientist at Blue Cross and Blue Shield of Kansas, your role is pivotal in harnessing data to drive informed decision-making, improve health outcomes, and enhance operational efficiencies. This position is integral to the organization’s mission of providing high-quality healthcare services while managing risks effectively. By leveraging data analytics, machine learning, and statistical modeling, you will contribute directly to initiatives that impact members’ health and the strategic direction of the organization.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Separate censoring from events in time-to-event models
Correct for claims runout before reporting recent months
Build member-month denominators from overlapping coverage spans

Overview

In this role, you will collaborate with cross-functional teams, including healthcare professionals, product managers, and IT specialists, to analyze health data, develop predictive models, and generate actionable insights. Your contributions will influence various products and services, helping to address complex challenges in healthcare delivery and cost management. Expect to engage with large datasets, tackle intricate problems, and deliver solutions that matter, making this role both challenging and rewarding.

The Blue Cross and Blue Shield of Kansas interview process

Blue Cross and Blue Shield of Kansas candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Data 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

Interviews with Hiring Manager

Much of this round runs on your own history, but the manager is not collecting a project list. They are working out what it is like when something goes wrong on your watch: how late the bad news tends to arrive, and whether a number you hand over has been checked by anyone including you. That is why the strongest material is a project where you can describe the part that did not work and what it cost. A result you cannot take full responsibility for, however clean, gives them nothing to trust you with afterwards.

What to demonstrate

Whether you volunteer the limits of a result you are proud of, or wait to be pushed onto them
How errors surfaced in your past work, and whether you or somebody else found them
Whether the scope you claim matches the level of detail you can still produce about it
What you did the first time a stakeholder acted on something of yours that turned out to be wrong

How to prepare

Rebuild one headline figure from memory down to the join and the filter, so a question about the denominator does not stall the conversation
For each project you raise, write the sentence you would say to someone who had already acted on a number that later turned out wrong
Mark which parts of a project were yours and which belonged to other people, and state that boundary yourself before anyone asks

Interviews with Team Members

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Treating clinical measurements as missing at random

A lab result, a vital sign, or a screening exists because someone ordered it, and ordering tracks suspicion of disease, visit frequency, and site workflow. Imputing the mean or dropping incomplete rows biases the population estimate and can flip the sign of an association, because the untested are systematically healthier or systematically disengaged. The presence indicator is often more predictive than the value, which is a warning sign rather than a feature win: a model that learns test ordering will not transfer to a site with different protocols.

Immortal time in adherence, treatment, and enrolment definitions

Classifying members as adherent, treated, or programme-enrolled requires them to survive and stay covered long enough to accumulate the defining events. That guaranteed event-free interval is assigned to the exposed group, so the exposure looks protective for reasons that have nothing to do with the treatment. Adherence studies are the classic case: measuring 12-month proportion of days covered and then comparing mortality builds survival into the exposure definition. Use time-varying exposure or a landmark analysis with the classification window excluded from follow-up.

Reaching for a model before the target metric exists

Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.

Writing SQL without stating NULL and tie-breaking behaviour

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

Questions & practice

What algorithms would you consider for predicting patient outcomes?

Medium · machine learning and modelling

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.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

Simulate how ignoring clustering inflates the false positive rate

Medium · simulation · design effect · type i error

Simulate a provider-level comparison with no true effect. 40 providers, 60 members each, a continuous outcome with total variance 1 and intraclass correlation 0.02. Randomise providers 20 to 20, not members. Over 5,000 replications, report the share of replications where a two-sample t-test run on all 2,400 individual observations, ignoring provider, returns p below 0.05. Report the same share when the unit of analysis is the 40 provider means. Then state how the first number follows from the design effect 1 + (m - 1) x ICC.

Approach

Decompose the variance explicitly rather than tuning it: provider variance = ICC = 0.02, residual variance = 1 - ICC = 0.98. Draw u_j once per provider and e_ij per member, so the outcome is u_j + e_ij and the correlation between two members of one provider is 0.02 by construction.
Randomise at the provider level. That is the entire mechanism: the treatment indicator is constant within a provider, so in any single replicate the provider intercepts are confounded with the arm, and the naive test reads that confounding as signal.
Vectorise over replications with a (reps, providers, members) array. 5,000 x 2,400 normals is trivial in numpy, and a Python loop is what tempts people to cut replications to 500 and report a number with Monte Carlo noise of plus or minus 0.017.
Predict the answer before running it: design effect = 1 + (60 - 1) x 0.02 = 2.18, naive standard errors are too small by sqrt(2.18) = 1.476, so the rejection rate at nominal 0.05 is about 2 x (1 - Phi(1.96 / 1.476)) = 0.18. Agreement between prediction and simulation is what makes the result a demonstration rather than an anecdote.
Run the provider-mean analysis as the control. Recovering 0.05 there proves the generator is correct and isolates the failure to the analysis unit.

Worked solution (30 min)

Draw u with shape (reps, 40) from N(0, sqrt(0.02)) and e with shape (reps, 40, 60) from N(0, sqrt(0.98)); y = u[:, :, None] + e.
Assign providers 0-19 to one arm and 20-39 to the other; because there is no true effect, a fixed split is valid and removes one source of Monte Carlo noise.
Naive test: flatten each arm to 1,200 observations and compute a two-sample t statistic per replication with vectorised means and pooled variance.
Cluster test: average within provider to 40 values, then run the same two-sample t on 20 versus 20.
Report both rejection shares and compare the naive one against 2 x (1 - Phi(1.96 / sqrt(2.18))).

Expected result: The naive individual-level test rejects in roughly 18 percent of replications, against a nominal 5 percent. Monte Carlo standard error at 5,000 replications is about 0.005, so anything in 0.17 to 0.19 is consistent. The provider-mean analysis rejects in about 5 percent, within roughly 0.003.

Verify it yourself

Set ICC to 0 and both analyses return about 0.05. If the naive one does not, the data generator is wrong, not the test.
Raise members per provider from 60 to 120 with ICC fixed and the naive rate rises, because the design effect grows linearly in cluster size.
The provider-mean analysis stays near 0.05 at every ICC you try, which is the definition of a valid test.
The simulated naive rate lands within about 0.01 of 2 x (1 - Phi(1.96 / sqrt(design effect))).

Follow-up

How many providers would you need for 80 percent power on a 0.2 SD difference with 60 members each and this ICC?
Cluster sizes are unequal in reality. The design effect approximation becomes 1 + ((1 + CV^2) x mbar - 1) x ICC. Which direction does that move your sample size and why?
The intervention cannot be withheld from any provider. Sketch a design that still yields a defensible estimate.

Build a claims lag triangle and complete recent months

Medium · chain ladder · runout · pivot

medical_claim_line has claim_id, claim_version, frequency_code, service_start_date, paid_date, allowed_amount, claim_status. Using 24 fully mature incurred months, build a development triangle of allowed dollars by incurred month and payment lag in whole months, derive cumulative completion factors by chain ladder, then estimate ultimate allowed for the three most recent incurred months at a stated paid-through date. Deduplicate to surviving claim versions before building the triangle. Return incurred_month, paid_to_date, completion_factor, estimated_ultimate.

Approach

Deduplicate to surviving claim versions and keep paid lines first. A triangle built on all versions develops on adjustment churn rather than on payment timing, and replacements arrive late, so the distortion concentrates in exactly the tail you are trying to estimate.
Compute lag as a whole-month difference between incurred month and paid month, not as day difference divided by 30. Ragged month lengths otherwise shuffle identical claims between lag buckets depending on which month they fall in.
Pivot to incurred_month by lag, cumulate along lag, then form age-to-age factors as the ratio of the column L+1 total to the column L total, summed over incurred months mature enough to have both. Volume weighting is the chain ladder; a simple mean of per-month ratios lets one low-volume month dominate the factor.
Chain the age-to-age factors from each lag to ultimate and invert: completion factor at lag L is the reciprocal of the product of factors from L onward. Apply as estimated_ultimate = paid_to_date / completion_factor.
State the assumption you have just made, which is that the development pattern is stable. It is not, after a claims-system migration, a network change or a processing backlog, and the most recent month's factor is the least reliable because it rests on the fewest observations while carrying the largest adjustment.

Follow-up

A processing backlog means the last two months developed slower than history. What does your estimate do, and how would you detect that before reporting?
Pharmacy, professional and inpatient facility develop on different clocks. How do you split the triangle, and what breaks if the service mix shifts?
What happens to a PMPM series if someone reports an unadjusted recent month, and in which direction does the error point?

Reconstruct drug coverage intervals and compute proportion of days covered

Hard · gaps and islands · window functions · adherence

pharmacy_claim holds fill_id, member_id, ndc_code, therapeutic_class_code, fill_date, days_supply, reversal_flag and reversed_fill_id. Early refills overlap, so days_supply must be laid end to end: each fill's coverage starts at the later of its fill_date and the previous interval's end. Both rows of a reversed pair are excluded. For one therapeutic_class_code over 2025-01-01 to 2025-06-30, return per member the proportion of days covered, defined as covered days divided by days from first fill_date to window end, plus the share of members at or above 0.80. Require at least two fills and 91 days of follow-up.

Approach

Clean the fills first. Drop rows with reversal_flag TRUE and also the fills they point at through reversed_fill_id. Dropping only the reversal row leaves an original fill counted as exposure that never reached the member. Use NOT EXISTS for that second step, since reversed_fill_id is nullable and NOT IN would return nothing.
Shift, never stack. The shifted end is covered_end_i = GREATEST(fill_date_i, covered_end_{i-1}) + days_supply_i, which looks recursive but has a closed form: with cum_i the running sum of days_supply through fill i, covered_end_i = cum_i + MAX over j <= i of (fill_date_j - cum_{j-1}). That is SUM(days_supply) OVER (ORDER BY fill_date ROWS UNBOUNDED PRECEDING) plus a running MAX of an anchor column, so it is two window functions and no recursive CTE.
Derive covered_start_i as GREATEST(fill_date_i, LAG(covered_end)). The intervals are disjoint by construction, so covered days is a plain sum of lengths and never needs a distinct day grid.
Clip intervals to the window end before summing, and decide explicitly whether to clip at disenrollment as well. Supply that runs past the window must not inflate the numerator.
Use the denominator the spec names, first fill_date to window end, and hold it constant across classes. A fixed-window denominator yields a different and usually lower number, and mixing the two across classes makes the comparison meaningless.
Apply the inclusion rules at member level after the intervals are built, then compute the share at or above 0.80 with the qualifying member count beside it.

Worked solution (40 min)

Filter to the therapeutic_class_code and window, then remove reversal rows and their originals with NOT EXISTS.
Per member ordered by fill_date, compute cum_supply and anchor = fill_date - (cum_supply - days_supply), then covered_end = cum_supply + MAX(anchor) OVER (ORDER BY fill_date ROWS UNBOUNDED PRECEDING).
Compute covered_start = GREATEST(fill_date, LAG(covered_end) OVER (ORDER BY fill_date)), clip both ends to the window, and drop intervals that collapse.
Sum interval lengths as covered_days, compute follow_up_days from first fill_date to window end, and divide.
Apply the two-fill and 91-day rules, then aggregate the share at or above 0.80.

Expected result: One row per qualifying member with covered_days, follow_up_days and pdc in [0, 1], plus a summary row with qualifying members and the share at or above 0.80. A member with 30-day fills on 2025-01-01, 2025-01-20 and 2025-04-01 is covered 2025-01-01 to 2025-03-01 and 2025-04-01 to 2025-04-30, giving 60 + 30 = 90 covered days over 181 follow-up days and a PDC of 0.497.

Verify it yourself

No pdc exceeds 1.0 before any clipping. A value above 1 proves supplies were stacked instead of shifted.
On a twenty-member sample, the summed interval lengths equal the distinct covered day count from a slow day-grid version.
A member with one fill is excluded by the two-fill rule rather than appearing with a PDC of 1.0.
Re-running with reversals left in raises covered days for exactly the members who have a reversed_fill_id, and for no one else.

Follow-up

A member is hospitalised for twelve days mid-window, when the facility supplies medication. Should those days count as covered, and what do published adherence measures do about them?
Someone wants to classify adherence over twelve months and then compare mortality from month zero. What is wrong with that design and what fixes it?
Two different ndc_codes in the same therapeutic class overlap. Is that stacking or switching, and how does your interval logic treat each case?

Turn overlapping coverage spans into fractional member-month denominators

Medium · intervals · gaps and islands · denominators

member_enrollment holds span_id, member_id, plan_id, product_type, effective_date and termination_date, which is NULL while the span is active. A member can hold several spans in a year, and back-dated plan changes make spans overlap. Return one row per calendar month of 2025 with total member-months, where a member contributes covered days in the month divided by that month's length, and a day covered by two overlapping spans counts once. Both span endpoints are inclusive. Collapse overlaps with window functions before joining a month calendar, and do not assume one span per member.

Approach

Normalise the open end first. COALESCE(termination_date, DATE '9999-12-31') or clip to the reporting end, because a NULL termination_date makes every BETWEEN comparison unknown and drops exactly the members who are still covered.
Merge overlapping spans per member before the calendar join. Order by effective_date, carry a running MAX of prior termination_date with a frame ending one row before, and open a new island where effective_date exceeds that running max plus one day, which keeps adjacent spans contiguous rather than splitting them.
Clip each merged island to the reporting year, then cross join to a twelve-row month calendar and keep pairs that intersect.
Compute covered days as LEAST(island_end, month_end) minus GREATEST(island_start, month_start) plus one, and divide by the actual length of that month rather than a flat 30, so February and the 31-day months are not distorted.
Sum the fractions by month. Merging after the calendar join also works but forces a DISTINCT over member-days, which stops scaling at population size.

Follow-up

Where does a continuous-enrolment filter with an allowable 45-day gap belong, and how does that change the island boundary rule?
One human appears under two member_ids after a master person index merge. What does that do to member-months, and how would you detect it?
How do you treat a span that terminates on the day it starts, and what does that row usually represent?

Discuss how you would communicate complex data findings to a non-technical audience.

Medium · product sense and metrics

Approach

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

Present a case where you had to make a data-driven recommendation under uncertainty.

Medium · product sense and metrics

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.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

What steps would you take to design a study to evaluate a new health intervention?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
How would you detect that the metric is being gamed rather than genuinely improving?

How do you prioritize tasks when managing multiple projects?

Medium · product sense and metrics

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.
State what result would change your recommendation, so the answer is falsifiable.

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?

Discuss the ethical considerations of using healthcare data.

Medium

Approach

Say what you would check first and why it is the highest-information step.
Work from the decision backwards to the evidence you would need.
Clarify what is being asked and what a complete answer would contain.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Specify a readmission rate two parties will settle on

Easy · measure specification · exclusions · observed over expected · runout

A payer and a hospital group are writing a shared-savings term that settles on 30-day unplanned readmissions. You have encounter (encounter_id, patient_id, member_id, facility_id, encounter_type, admit_ts, discharge_ts, admission_type, discharge_disposition, is_planned_admission, index_encounter_id, drg_code, principal_diagnosis_code) and member_enrollment coverage spans. Produce the measure specification both sides sign: numerator, denominator, exclusion list, observation window, and the paid-through date at which a discharge month becomes reportable. State whether settlement is on the raw rate or an observed-over-expected ratio, and defend the choice.

Approach

Fix the unit of analysis first: the denominator is eligible index stays, not members and not patients. Say out loud that one member can contribute several index stays in a period, and that a readmission is itself eligible to become an index stay, because leaving that unstated is where two implementations diverge.
Write the exclusion list as a waterfall with a count at each step rather than a sentence: planned admissions (is_planned_admission TRUE), discharge_disposition in expired, hospice and ama, and stays whose discharge_disposition is transfer_acute, which are the first leg of one episode rather than an index stay that ended.
Add the exposure condition the encounter table cannot express on its own: an index stay only counts if the member has coverage in member_enrollment for the full 30 days after discharge_ts, with the allowable gap written in days. Without it, a disenrollment reads as a clean outcome.
Define the numerator join precisely: the earliest later inpatient encounter with is_planned_admission FALSE and admit_ts within 30 days of the index discharge_ts, at any facility_id, capped at one readmission per index stay so a single unstable member cannot dominate a facility's rate.
Set the maturity rule. Inpatient facility claims adjudicate slowest of any service type, so name a paid-through lag, put the paid-through date on the report header, and state that a discharge month is restated until it matures.
Settle on observed-over-expected and say why: raw rates confound case mix with care, and the expected model is fitted on age, sex and comorbidities drawn from the 12 months of claims before admit_ts, excluding diagnoses first coded during the index stay so complications of that stay do not inflate the facility's own expectation.

Worked solution (25 min)

Write the eligible-index waterfall as a numbered list before touching any logic, with a placeholder count beside each line so the reviewer can see how much of the raw discharge volume each exclusion removes.
Write the numerator rule in one sentence including the tie-break for two admissions on the same day, and the one-readmission-per-index cap.
State the coverage requirement as a concrete filter: covered on every day from discharge_ts to discharge_ts + 30, allowable gap 0 days, evaluated against member_enrollment spans rather than a single active flag.
Name the paid-through lag and write the restatement policy: a discharge month is provisional until it matures and is republished at every refresh until then.
Write two sentences on why settlement is on O/E, including what risk adjustment does not fix, namely unrecorded severity and differences in social support at discharge.

Expected result: A one-page specification containing: an exclusion waterfall with a count at every step; a numerator counting at most one unplanned acute readmission per index stay within 30 days of discharge_ts at any facility; a 30-day continuous-coverage requirement with the allowable gap stated in days; a named paid-through lag with a restatement policy; and settlement on the observed-over-expected ratio with a confidence interval, on the stated ground that raw rates are not comparable across facilities with different case mix.

Verify it yourself

Recompute the raw rate with and without the transfer_acute exclusion. A facility that receives transfers should move materially; if nothing moves, the transfer legs are not being detected at all.
Compare the numerator count to the count of distinct encounters serving as a readmission. They should be equal; a larger numerator means one readmission is being credited to two index stays.
Re-run the same discharge month at two paid-through dates 60 days apart. A stable rate means the lag is long enough; a rate that climbs means it is not.
Confirm every excluded stay lands in exactly one exclusion bucket so the waterfall sums back to the raw discharge count.

Follow-up

A member transfers from one acute facility to another on day two of the stay. One episode or two, and which facility owns the outcome?
The hospital starts admitting borderline patients to observation status instead of inpatient. What happens to your numerator, your denominator and the rate, and which companion metric would show it?
Why would you refuse to report this rate for a facility with 40 eligible index stays in a quarter?

Avoidable admissions fell the quarter a programme launched

Hard · mix shift · regression to the mean · reclassification · seasonality

Ambulatory care sensitive admissions per 1,000 member-years fell 18 percent in the quarter a care-management programme launched, enrolling the top 2 percent of members by prior-year spend. You have encounter, member_enrollment including a product line added mid-quarter, and medical_claim_line. Emergency visits per 1,000 rose 9 percent over the same quarter and observation stays rose as well. Produce a decomposition that states how much of the 18 percent is attributable to the programme, and state the design you would need before claiming any of it.

Approach

Rebuild the denominator from coverage spans as member-years, counting partial coverage fractionally, and recompute the rate with and without the product line that entered mid-quarter. A newly added, structurally healthier population inflates the denominator and deflates every rate built on it without a single admission being avoided.
Freeze the numerator's code list to one version. The ambulatory care sensitive list is defined on ICD-10-CM principal diagnosis codes, and in the United States that code set is updated effective October 1, so a quarter spanning that boundary can gain or lose qualifying codes. Recompute both quarters against a single code set version.
Decompose the remaining change with a Kitagawa split across product_type and prior-year risk decile: the mix term is the population getting younger or healthier, the within term is the only place a programme effect can live.
Test reclassification directly. An inpatient stay recorded as an observation stay is not an avoided admission. Compute total acute contacts per 1,000 member-years as inpatient plus observation plus emergency, and check whether that total moved at all. If it is flat while inpatient falls, care was relabelled and deflected, not prevented.
Compare to the same quarter of the prior year rather than the adjacent quarter. Several ambulatory care sensitive conditions, including heart failure decompensation and bacterial pneumonia, are strongly seasonal, so a quarter-over-quarter comparison confounds the programme with the calendar.
Address selection last and explicitly. The enrolled cohort was chosen on an extreme of prior-year spend, so it regresses toward the mean with no intervention at all, because the selecting year captured both chronic severity and one-off events. A pre-post design on this cohort will report savings every time. The minimum credible design is a concurrent comparison group selected by the same rule in the same period, or a regression discontinuity at the 2 percent threshold, with a parallel-trends check on at least four pre-periods.

Follow-up

The parallel-trends check fails on two of the four pre-periods. What do you do, without abandoning the question?
Express the effect you can defend as an absolute risk difference and a number needed to treat, with the baseline rate stated. Why is that more useful here than a relative reduction?
The programme cannot be withheld from anyone. Sketch a rollout design that still identifies an effect, and say what secular trend does to the estimate.

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

Bring numbers, including the ones you are unsure of

An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.

Explain a time when you used statistical methods to solve a problem.

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Quantify the outcome, including what you would not claim credit for.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What would you do differently if you ran that project again?
How did you know the outcome was caused by your change?

How do you handle failure or setbacks in your projects?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
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 null result against a programme sponsor

Medium · regression to the mean · difference-in-differences · stakeholder management

A care management programme enrolled the top 1 percent of members by prior-year allowed spend. The sponsor's deck shows allowed PMPM for enrollees falling 34 percent from the year before enrollment to the year after, and asks for budget to triple the programme. You rebuild the evaluation with a concurrent comparison group selected by the identical spend rule in the same period. The difference-in-differences estimate is a 3 percent reduction with a confidence interval spanning zero. Deliverable: how you present this, to whom, in what order, and what you propose next.

Approach

Name the probe: whether you can deliver a finding that costs somebody their programme without softening it into uselessness or creating an adversary who routes around you next time.
Lead with the mechanism, not the verdict. Show the comparison group's own unadjusted drop, which will be large, because a cohort selected on an extreme of the outcome regresses toward the mean whether or not anyone intervenes. The sponsor's 34 percent is mostly that, and it is a property of the selection rule rather than a criticism of their clinicians.
Give the sponsor the finding privately before it appears in any deck their leadership sees. Being surprised in a room is what turns a methods disagreement into a political one.
State the estimate with its interval and say what it rules out as well as what it fails to establish. A 3 percent point estimate whose interval crosses zero is not evidence of no effect; it is insufficient power to separate a modest effect from none, and those are different claims.
Arrive with a design rather than only an objection. Propose a regression discontinuity at the enrollment threshold if the rule is applied sharply, or a randomised rollout across the next wave of eligible members, and state the sample size needed to detect the effect size the sponsor believes in.

Follow-up

The sponsor says withholding the programme from a comparison group is unethical. What do you propose instead?
Leadership wants one number for the board next week. What do you give them?
What result would change your mind and make you believe the 34 percent?