Interview guides · Cardinal Health

Cardinal Health Data Scientist Interview Questions & Guide 2026

Cardinal Health data scientist prep: probability, sampling distributions, and stating the assumption behind every test you run.

The 60-second brief

As a Data Scientist at Cardinal Health, you play a pivotal role in harnessing data to drive informed decision-making across the organization. This position not only involves analyzing complex datasets but also directly influences product development, operational efficiency, and customer satisfaction within the healthcare sector. Your insights will support teams in creating innovative solutions that enhance patient care and optimize supply chain processes, making your contributions vital to both the company and the communities it serves.

Ask early whether the loop includes an asynchronous take-home or a timed live case, because the two are graded on different things. A take-home is read as an artifact: the question you decided to answer, what you did about missing or malformed records, and a conclusion stated plainly enough for someone to act on. A reviewer who cannot rerun your notebook discounts the result whatever score is printed in it. Hold to the stated time box and write down what you would have done with more of it, since the follow-up round is usually a live defence of the same work.

Focus areas

Risk-adjust outcomes before comparing providers or panels
Reconstruct drug exposure intervals from dispensing records
Minimise protected health information in every extract

Overview

In this role, you will work closely with cross-functional teams, including product managers, engineers, and clinical experts, to tackle some of the industry's most pressing challenges. Whether it’s developing predictive models to forecast demand or analyzing patient outcomes to improve quality of care, your work will have a significant impact on the strategic direction of Cardinal Health. Expect a dynamic environment where your analytical skills will not only be valued but are essential for driving meaningful change.

The Cardinal Health interview process

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

Phone Screen

Whoever 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

Take-Home Assessment

Your submission is read asynchronously by someone who cannot ask you a clarifying question, and who will usually skim it before reading it properly. That changes what good looks like. The conclusion belongs near the top with the supporting analysis beneath it, and the code should run start to finish on a clean machine without a manual step you forgot to document. A reviewer forced to reconstruct your reasoning from the order of notebook cells is already discounting the work. The submissions that land are the ones where a busy reader gets the answer immediately and can verify it if they want to.

What to demonstrate

Whether the answer arrives before the methodology, so a reader who stops after the first page still has the recommendation
Whether the code runs end to end from the submitted files, with dependencies and data paths declared rather than assumed
Whether each chart is legible on its own, carrying axis labels and units, and exists to support a claim made in the text

How to prepare

Restructure a past analysis so the opening paragraph holds the recommendation and the number behind it, then check that nothing later in the document quietly contradicts it
Copy your own submission into an empty directory, run it in a clean environment, and fix everything that breaks; hidden local state is caught here or by the reviewer
For every chart, write the one sentence it is meant to prove, and delete the chart if you cannot write that sentence

Technical Interviews

A 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

Final Discussions

Where a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.

What to demonstrate

Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered

How to prepare

Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one

Where candidates lose points

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.

Rates built on member counts rather than exposure

Members join and leave mid-period, so dividing events by distinct members mixes a person covered for 30 days with one covered for 365. New joiners also have artificially low observed utilisation because their claims have not arrived yet and because care takes time to initiate. Denominators must be member-months or member-years, and comparative quality measures usually need a continuous-enrolment requirement with an explicit allowable gap, stated in days.

Generalising beyond the population the sample actually supports

State the frame the sample was drawn from and where it diverges from the population you want to talk about: time window, platform, geography, opt-in. If a group is excluded from the frame, either weight to known margins or narrow the claim rather than quietly extending it.

Dropping rows with missing values without naming the mechanism

Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.

Questions & practice

Write a function to calculate the mean and standard deviation of a dataset.

Medium · statistics and probability

Approach

Sanity-check the answer against a simple bound or a simulated case.
Quantify uncertainty explicitly rather than reporting a point estimate alone.
Write down the assumption the method needs before you use the method.

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?

Provide an example of how you would optimize a given algorithm.

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Describe a project where you implemented a predictive model.

Medium · machine learning and modelling

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?
How would you choose the decision threshold, and who owns that choice?

Sessionise transfer chains into episodes, then count readmissions

Hard · sessionisation · cumsum grouping · readmission

encounter has encounter_id, patient_id, facility_id, encounter_type, admit_ts, discharge_ts, admission_type, discharge_disposition, is_planned_admission, principal_diagnosis_code. Chain acute-to-acute transfers into episodes of care: an inpatient encounter discharged as transfer_acute, followed by another inpatient encounter for the same patient at a different facility admitting within 24 hours of that discharge, belongs to the same episode. Then compute the 30-day unplanned readmission rate over episodes, excluding index episodes that are planned, that end in expired, hospice or against medical advice, or that are still open. Return encounter_id to episode_id, plus the rate.

Approach

Restrict to inpatient encounters for the chaining step and sort by patient_id, admit_ts. Observation and emergency encounters are not acute inpatient stays and pooling them changes both the chain and the denominator.
Build a boolean continues flag: the previous row is the same patient, its discharge_disposition is transfer_acute, its facility_id differs from this row's, and admit_ts minus previous discharge_ts is between 0 and 24 hours. Episode id = cumsum of the negation of that flag. This is the vectorised form of a sessionisation loop and is why the pattern generalises to any event stream.
Collapse to episode grain taking first admit_ts, last discharge_ts, last discharge_disposition, and any() of is_planned_admission. The episode inherits its exit state, not its entry state, which is why a transfer chain ending at home is one home discharge rather than two transfers.
Apply index exclusions at episode grain. Dropping episodes that end in death is not optional: a dead patient cannot be readmitted, so leaving them in inflates the denominator and depresses the rate for exactly the panels caring for the sickest members.
For each surviving index episode, find the next unplanned acute inpatient episode for that patient admitting within 30 days of the index discharge_ts. Count events at episode grain, since one patient can contribute several index episodes. Report the raw rate and say plainly that it is not comparable across panels without risk adjustment, which is what the observed-over-expected form exists for.

Worked solution (45 min)

Filter to encounter_type 'inpatient', sort by patient_id and admit_ts, and shift discharge_ts, discharge_disposition, facility_id and patient_id by one row.
Compute continues as same patient AND prior disposition is transfer_acute AND facility differs AND 0 <= (admit_ts - prior discharge_ts) <= 24 hours; episode_id = (~continues).cumsum().
Aggregate to episodes: first admit_ts, last discharge_ts, last discharge_disposition, any planned flag, patient_id.
Drop index-ineligible episodes: planned, disposition in expired, hospice or ama, and null discharge_ts.
For each eligible index episode, search the same patient's later episodes for an unplanned admit_ts within 30 days of index discharge_ts; a merge_asof on patient with a forward direction and a 30-day tolerance does this without a nested loop.
Rate = index episodes with an event divided by eligible index episodes; return the encounter-to-episode map alongside it.

Expected result: Two outputs: an encounter_id to episode_id map, and one rate. Worked case: encounter A discharged transfer_acute at 10:00, encounter B admitted 14:00 the same day at a different facility and discharged home on day 5, encounter C an unplanned admission on day 20. A and B form one episode, C is a readmission of it, and the patient contributes one eligible index episode and one event, not two indexes and possibly two events.

Verify it yourself

Every closed episode ends in a disposition other than transfer_acute; an episode ending in transfer_acute means a chain was cut early.
Episode count is less than or equal to inpatient encounter count, with equality only when the sample contains no transfers.
Turning the chaining rule off raises the readmission rate. The direction of that change is the proof your chaining fired at all.
No index episode's matched readmission is an encounter already inside that same episode.

Follow-up

discharge_ts is null on an open stay in the middle of a chain. What does your flag do, and what should it do?
A patient is transferred out and then back to the originating facility within 24 hours. Should that chain, and does your facility_id condition handle it?
Is a readmission itself eligible to serve as a later index episode? Say what you chose and what the choice does to the rate.

Compute allowed PMPM by incurred month without span fan-out

Hard · fan-out · many-to-many · runout

Report allowed PMPM by incurred month and product_type for 2025 from medical_claim_line (member_id, claim_id, claim_version, frequency_code, service_start_date, allowed_amount, claim_status), pharmacy_claim (member_id, fill_date, allowed_amount, reversal_flag, reversed_fill_id), member_enrollment (span_id, member_id, product_type, effective_date, termination_date) and member_risk_score (member_id, score_year, prospective_risk_score), which holds at most one row per member per score year. The numerator is allowed_amount on surviving claim versions plus unreversed fills, attributed to the product_type in force on the service or fill date. The denominator is member-months. Also return a risk-adjusted PMPM for each cell: allowed PMPM divided by the member-month-weighted mean prospective_risk_score for score_year 2025 over the members making up that cell's denominator. A colleague joined claim lines to member_enrollment on member_id alone and the annual total came back 1.4 times the finance figure. Diagnose that, then produce the correct monthly series.

Approach

Name the defect precisely. member_enrollment is one row per span, so joining on member_id alone is one-to-many and duplicates every claim line once per span that member ever held. The inflation factor is the spend-weighted mean span count per member, which is why 1.4 is plausible and why it is not a constant you can divide back out.
Make the join functional: member_id AND service date between effective_date and COALESCE(termination_date, DATE '9999-12-31'). That yields at most one span per line unless spans genuinely overlap, in which case pick a documented precedence rule, most recently effective wins being the usual one, and prove uniqueness with a duplicate check rather than asserting it.
Aggregate numerator and denominator separately and join them on (month, product_type). Computing both sides across one join is the second route to inflation, because the member-month denominator fans out over claim lines just as easily as the dollars fan out over spans.
Clean each numerator source on its own terms: surviving claim versions with voids removed and claim_status 'paid' on the medical side, reversal rows and their originals removed on the pharmacy side.
Apply completion before anyone reads a trend. State the paid-through date and either withhold the three most recent incurred months or apply completion factors, since the lag differs by service type and the incomplete tail reads as a cost improvement and a utilisation drop at once.
Risk-adjust last, and weight the score the way the denominator is weighted. The divisor for a cell is SUM(member_months * prospective_risk_score) / SUM(member_months) over that cell's members, not a plain AVG over distinct members: a member covered two months must not count the same as one covered twelve. member_risk_score is member-level, so join it after the member-month grain is fixed, or it becomes a second fan-out.
State the rule for members with no 2025 member_risk_score row: keep them in member_months, leave them out of the weighted mean, and publish the scored share of member-months beside the adjusted series. A thin score table moves the adjusted number while the raw one sits still, and without that share nobody can tell the two apart.

Worked solution (45 min)

CTE med: surviving claim version per claim_id, voids dropped, claim_status 'paid', service_start_date in 2025.
CTE rx: fills with reversal_flag FALSE and not referenced by any reversed_fill_id, via NOT EXISTS, fill_date in 2025.
Attribute each numerator row to one span with the date-bounded join, after asserting that row counts before and after the join are equal.
CTE denom: member-months by month, product_type and member_id from spans alone, with fractional partial months and overlapping spans collapsed first.
Join numerator to denominator aggregated on (incurred_month, product_type) and divide for allowed_pmpm.
Join denom to member_risk_score on member_id for score_year 2025, compute the cell divisor as SUM(member_months * prospective_risk_score) / SUM(member_months) over scored members, carry the scored share of member-months, and divide allowed_pmpm by the divisor for risk_adjusted_pmpm.
Withhold the three most recent incurred months and label the paid-through date on every row.

Expected result: One row per (incurred_month, product_type) with allowed_medical, allowed_pharmacy, member_months, allowed_pmpm, mean_risk_score, scored_member_month_share and risk_adjusted_pmpm, covering January through September when the paid-through date is year end and three months are withheld. Row count of the claim-line set is identical before and after the enrollment join.

Verify it yourself

Claim-line row count before and after the span join is unchanged. An increase is fan-out; a decrease is a service date with no covering span, which is a real finding about eligibility gaps.
SUM(allowed_amount) across product_types for a month equals the ungrouped monthly total computed without touching member_enrollment at all.
member_months for a month never exceeds the count of distinct members covered at any point in that month.
mean_risk_score for a cell lies between the minimum and maximum prospective_risk_score among its scored members, and risk_adjusted_pmpm equals allowed_pmpm exactly when that mean is 1.0. If the weighted mean equals a plain AVG over distinct scored members in every cell, the member-month weights were never applied.
A downward step in the last reported months means the runout holdback was not applied; the withheld tail should not be visible in the series at all.

Follow-up

A member holds two overlapping spans under different product_types on the service date. Which one gets the dollar, and how do you keep the member-month denominator consistent with that choice?
Write the check that proves the span join is one-to-one rather than assuming it.
Finance reports on paid_date and you reported on service_start_date. Reconcile one month between the two bases.

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.

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?

Describe your thought process when faced with a complex data problem.

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.
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?

If given a dataset with customer feedback, how would you analyze it to improve service delivery?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

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

How do you prioritize tasks when handling multiple projects?

Medium · product sense and metrics

Approach

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

Follow-up

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

How would you approach a situation where you need to predict future sales trends for a new product?

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.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
Which segment would you cut first, and what would that rule out?

Explain the difference between supervised and unsupervised learning.

Medium

Approach

State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Clarify what is being asked and what a complete answer would contain.

Follow-up

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

Set the decision rule when cost and denial guardrails conflict

Medium · guardrail conflict · denials · non-inferiority bound · audit sampling

Utilization management is asked to cut risk-adjusted allowed PMPM, and its lever is prior authorization and first-pass denial on advanced imaging and specialty drugs. The scorecard guardrail is first-pass denial rate from medical_claim_line (claim_status, denial_reason_code, claim_version, received_date) paired with the overturn rate on appealed denials. The two move in opposite directions by construction. Write the decision rule: which metric binds, at what pre-declared threshold, who adjudicates a breach, and what happens on breach. Do not resolve the conflict by weighting both into one score.

Approach

Say why a composite is the wrong instrument: the weights get chosen after someone has seen which way the quarter went, and any weighted sum lets a large enough cost win purchase an unbounded increase in wrong denials. A guardrail is a constraint, not a term in an objective.
Sharpen what the guardrail is actually protecting. A higher denial rate is not by itself a harm, since some denied claims should be denied. The harm is denials that should not have been made, so split first-pass denials by denial_reason_code into administratively correctable ones (eligibility, coding, missing documentation) and clinical-necessity ones, and put the bound only on the clinical group.
Fix the measurement of the guardrail before setting its threshold: count the denominator at claim_version 1 so replacements (frequency_code 7) and voids (8) are not double counted, hold the month back for 60 days of adjudication runout, and report by received_date.
Get the direction of the appeal-selection bias right. Appeals are filed on the denials the appellant already believes are wrong, by the members and providers with the staffing, literacy and dollar threshold to file at all, and an appeal usually arrives carrying documentation the first adjudication never had. The overturn rate on appealed denials therefore runs well above the wrong-denial rate across all denials, often by a multiple, and it moves with filing behaviour as much as with adjudication quality. The unbiased population estimate is a blind re-review of a random sample of all denied clinical-necessity lines each quarter, judged on the record as it stood at first adjudication; the appeal series is a fast indicator read against its own history between audits, not a second estimate of the same quantity.
Write the rule as a bound with a consequence: risk-adjusted allowed PMPM is the primary and binds only while the audited wrong-denial rate stays within a pre-declared absolute margin of its trailing four-quarter mean. Breach stops the program's expansion rather than discounting its score, and the margin is set before the quarter opens.
Name the adjudicator and the cadence: a clinical owner outside the utilization function, reviewing at the stated paid-through date, with the breach consequence written down before any result is known.

Worked solution (25 min)

Restate the guardrail metric properly: numerator is claim lines denied at first adjudication, denominator is lines received in the month counted at claim_version 1, held 60 days, broken out by denial_reason_code group.
Split denial_reason_code into administratively correctable and clinical-necessity groups and say which function owns each, since the two have different fixes and different harms.
Write the appeal-selection argument in two sentences, including which way it runs, and specify the random blind re-review: the frame it is drawn from, namely all denied clinical-necessity lines rather than appealed ones, plus sample size, who reviews, what record they are shown, blinding, and quarterly cadence.
State the bound as an absolute margin on the audited wrong-denial rate against its trailing four-quarter mean, with the margin fixed before the period.
Write the breach consequence and the named adjudicator, and note that both are recorded before results exist.

Expected result: A written rule in which risk-adjusted allowed PMPM is primary; the binding guardrail is the audited wrong-denial rate on clinical-necessity denials, estimated from a blind random re-review of all denied clinical-necessity lines, with the appeal overturn rate kept as a between-audit indicator that runs above the audited rate and never sets the bound; the bound is a pre-declared absolute margin against a trailing four-quarter mean; a clinical owner outside utilization management adjudicates; and a breach halts expansion rather than adjusting a score.

Verify it yourself

Recount the denominator with and without version dedup. If the two differ by more than a percent or two, the incumbent number has been counting resubmissions.
Re-run a closed month at two extract dates 60 days apart. A moving denial rate means the runout hold is too short.
Compare the audited wrong-denial rate to the appeal overturn rate for the same quarter. The overturn rate should be the higher of the two, usually by a multiple, because appeals are filed on the denials most likely to be wrong and often add documentation the first adjudication never saw. An audit rate at or above the overturn rate is the anomaly: check that the sample was drawn from all denied clinical-necessity lines rather than only appealed ones, and that the reviewers were blinded and shown the record as it stood at first adjudication.
Confirm the margin, the adjudicator and the consequence are all recorded with a timestamp before the period's data was available.

Follow-up

Denials rose and the overturn rate fell. Give the benign reading and the bad reading, and the one number that separates them.
What happens to your bound if the appeal filing rate per denial drops 30 percent in the same quarter?
How large a random audit sample do you need to detect a 2 percentage point rise in wrong denials off a 6 percent base, and what does that cost in clinician review hours?

Protocol deviations fell as new sites came online

Hard · reporting lag · denominator mix · clustering

Protocol deviations per 100 completed visits across a multi-site study fell from 4.6 to 2.9 over two months while randomisation accelerated. study_subject_visit carries study_id, site_id, subject_id, visit_number, visit_name, planned_visit_date, actual_visit_date, visit_window_days, visit_status, protocol_deviation_flag, deviation_severity, data_entry_ts and site_activation_date. Operations wants to credit a retraining rollout that began in that window. Establish whether deviation behaviour changed, and deliver a version of the series that is safe to review monthly.

Approach

Treat deviation flags as late-arriving data. Build a lag triangle of data_entry_ts minus actual_visit_date, because deviations are largely identified during monitoring review weeks after the visit, so the two most recent months are structurally undercounted in exactly the way an improvement looks.
Hold back the months that are not developed, or apply development factors fitted on fully developed months, and restate. Do this before any explanation involving training.
Attack the denominator. It counts visits with visit_status 'completed', while out_of_window and missed visits are excluded even though out-of-window attendance is itself a deviation in most protocols, so a site that pushes visits out of window lowers the rate twice over.
Standardise the visit mix. Newly activated sites contribute mostly screening and baseline visits, which carry fewer procedures and fewer opportunities to deviate than later treatment visits, so compute the rate within visit_number strata and reweight to a fixed mix before comparing months.
Split major from minor deviation_severity and report them separately. A single major deviation can remove a subject from the per-protocol analysis set while ten minor ones may not, so a pooled rate can fall while the consequential series rises.
Before attributing anything to training, model at the right level: subjects are nested in sites, so use a random intercept per site or cluster-robust standard errors. With average cluster size m and intracluster correlation rho the variance is understated by the design effect 1 + (m - 1) times rho, which at m = 60 and rho = 0.02 is about 2.2, so naive intervals are roughly 1.5 times too narrow.

Follow-up

Design a defensible evaluation of the retraining rollout, given it was deployed site by site and cannot be withheld.
Your reweighted series is flat but major deviations rose. What do you escalate and to whom?
How would you decide the holdback period, and how would you communicate a provisional number without it being quoted as final?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Describe a situation where you had to persuade stakeholders to adopt your recommendations.

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Quantify the outcome, including what you would not claim credit for.
Name the disagreement or constraint, and how you resolved it with evidence.

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 cost series to a non-technical executive

Medium · claims runout · uncertainty · executive communication

An executive opens a dashboard showing risk-adjusted allowed PMPM down 6 percent across the last three incurred months and asks whether the cost initiative is working. You know pharmacy adjudicates within days, professional claims within weeks, and inpatient facility claims over months, so those three months are both undercounted and mix-shifted toward cheap services. There is also a genuine movement of roughly 1.5 percent in the older, complete months. Deliverable: a two-minute spoken answer, the change you make to the chart, and what you commit to telling them and when.

Approach

The probe is whether you can be honest about uncertainty without sounding evasive. Executives read hedging as not knowing, so the answer has to end in a commitment.
Separate the two claims immediately. The 6 percent is an artefact of incomplete data, and there is a smaller real movement in the months that are complete. Delivering only the debunk leaves them holding nothing.
Explain the lag in their terms: the bills for the most expensive care arrive last, so an incomplete month always looks cheap and always looks like it is improving. Keep the phrase completion factor out of the first pass.
Fix the chart instead of explaining around it. Shade or omit the incomplete months, print the paid-through date on the axis, and overlay the same series as it looked at equivalent maturity a year earlier so the shape is comparable rather than merely lower.
Close with a date and a magnitude. Commit to a restated figure once runout matures, say roughly how far you expect the 6 percent to shrink, and name what you would need to see to call the initiative working.

Follow-up

They ask for your best guess today, knowing it is provisional. What do you say?
The restated number comes back at 1 percent. How do you handle having flagged the 6 percent at all?
How do you stop this dashboard producing the same conversation next quarter?

Own an analysis that shipped wrong and was acted on

Easy · retrospective · ownership · data quality

Describe an analysis you delivered that was wrong, where someone acted on it before the error surfaced. Pick something with a mechanism you can name, not a typo. Cover what the number claimed, what was actually true, how much time passed before it was caught, who caught it, and what you told the people who had already acted. Deliverable: a five-minute account that ends with the specific control you added afterwards, plus one occasion since when that control has fired.

Approach

Read the probe: the interviewer is testing whether you detect your own errors and whether the correction was structural. Pick an error with a nameable mechanism (a denominator built from distinct members instead of member-months, a series read before claims runout completed, a cohort selected on the outcome) so the story has a diagnosis rather than a mood.
Open with the decision that was made on your number, not with the bug. The severity of an analytic error is measured in the action it caused: a rate filing, a staffing plan, a programme expansion.
State the counterfactual number. 'The true figure was 14.1 admissions per 1,000 member-years, not 9.8' is a fact; 'it was off by a lot' is not.
Describe the detection path honestly, including whether you found it or somebody else did. Say how long it took, because time to detection is the part you actually control next time.
Close on the control, and make it something that protects a reader who has never met you: a unit test on the denominator, a paid-through date stamped on every chart, a required exclusions block in the spec template. Name one later date it caught something.

Follow-up

What would have had to be true for you to catch this before delivery rather than after?
Did the stakeholder change how much they trust your work, and did you want them to?
Give an example of a control you added that turned out to be the wrong control.