Interview guides · Labcorp

Labcorp Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

As a Data Scientist at Labcorp, you occupy a critical position at the intersection of advanced analytics and global healthcare. You are responsible for transforming complex, high-dimensional clinical and laboratory data into actionable insights that directly influence patient outcomes, operational efficiency, and the trajectory of clinical drug development. Your work is fundamental to the Labcorp mission of improving health and improving lives, requiring you to bridge the gap between technical rigor and clinical application.

Product-sense cases reward reasoning from a mechanism to a testable prediction. Reciting every metric you can name reads as pattern matching; naming the single quantity that would move if your explanation were true reads as thinking.

Focus areas

Separate censoring from events in time-to-event models
Build member-month denominators from overlapping coverage spans
Reconstruct drug exposure intervals from dispensing records

Overview

You will likely engage with large-scale datasets, including CDISC, SDTM, and ADaM standards, while supporting both early and late-phase statistical analysis. The role demands more than just modeling proficiency; it requires a deep understanding of ICH guidelines and Good Clinical Practice (GCP). Whether you are optimizing trial designs or refining diagnostic algorithms, your contributions will be central to the strategic initiatives that maintain as a leader in the life sciences industry.

The role is highly interdisciplinary. Expect to collaborate with clinical trial managers, biostatisticians, and software engineers, making your ability to communicate complex findings to non-technical stakeholders a key differentiator.

The Labcorp interview process

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

Recruiter Screen

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

Virtual Interviews

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Final Round Assessments

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What to demonstrate

Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
Whether you ask what decision is actually on the table before explaining anything

How to prepare

Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else

Where candidates lose points

Ignoring clustering by provider, facility, or site

Patients within a provider panel, and subjects within a trial site, are correlated on both case mix and practice pattern. Standard errors computed as if observations were independent are too narrow by roughly the design effect, 1 + (m - 1) * ICC, where m is the average cluster size. With 50 patients per provider and an intracluster correlation of only 0.02, variance is understated by about a factor of two, which manufactures significant provider differences out of noise. Cluster-robust errors or a random intercept per provider, and cluster-level randomisation when you design the test, are the corrections.

Reading the most recent months of a claims-based series as real

Claims incur before they are reported and paid, so recent incurred months are systematically undercounted until runout completes. The lag is not uniform: pharmacy adjudicates in days, professional claims in weeks, inpatient facility claims in months. That means recent data is both too low and mix-shifted toward cheap services, which reads as a cost improvement and a utilisation drop at once. The fix is to hold the last three incurred months back or apply completion factors, and to state the paid-through date on every chart.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

Naming a model class before naming the deployment constraints

Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.

Questions & practice

Proportion of days covered with shifted, truncated refill intervals

Hard · interval merging · adherence · path dependence

pharmacy_claim has fill_id, member_id, therapeutic_class_code, fill_date, days_supply, reversal_flag, reversed_fill_id. For one therapeutic class and a fixed 12-month window, compute proportion of days covered per member: distinct days on which a dispensed days_supply covers the day, divided by days from the member's first in-window fill through the window end. An early refill shifts coverage forward rather than stacking, and coverage is truncated at the window end. Drop both rows of every reversed pair. Return member_id, pdc, and the share at pdc >= 0.80 among members with at least 2 fills and at least 91 days of follow-up.

Approach

Remove reversals as pairs first. Drop every row with reversal_flag true, and also drop the fill_ids those rows point at through reversed_fill_id. Dropping only the flagged row leaves a dispense that was never collected in the exposure.
Walk fills per member in fill_date order carrying a cursor: start = max(fill_date, previous_end + 1 day), end = start + days_supply - 1. The shift is path dependent, so a plain cumsum over days_supply does not reproduce it. Use itertools.accumulate or a per-member loop over numpy arrays, not a row-wise apply over the whole frame.
Truncate the last interval at the window end before measuring. Without truncation a 90-day fill dispensed on the final day pushes the covered-day count past the denominator and PDC above 1.0.
Use the denominator the definition states: first in-window fill_date through window end, inclusive. Not a flat 365, and not first fill to last fill, which is a different metric that rewards early discontinuation.
Apply the eligibility filter before computing the >= 0.80 share, and report the size of that denominator next to the share. A share without its denominator is not reviewable.

Follow-up

How does PDC differ from medication possession ratio, and which of the two can exceed 1.0?
A member switches to a different ingredient inside the same therapeutic class mid-window. Should the intervals chain, and what does that do to the class-level number?
Members who die or lose coverage mid-window get a short denominator and often a high PDC. If you then compare mortality by adherence category, what bias have you built in and how do you remove it?

Collapse claim versions before summing allowed amounts by member

Easy · claim versioning · groupby · deduplication

You are given medical_claim_line as a pandas DataFrame: claim_line_id, claim_id, claim_version, frequency_code (1 original, 7 replacement, 8 void), member_id, service_start_date, procedure_code, allowed_amount, claim_status. Return total allowed_amount per member for service dates in one stated calendar month, counting each claim once at its surviving version and excluding any claim whose surviving version is a void. Multi-line claims must keep every line of that surviving version. Output columns: member_id, allowed_amount, sorted descending. Do not deduplicate on claim_id alone.

Approach

Separate the two grains out loud before writing code: versions live at the claim level, dollars live at the line level. Every bug in this exercise comes from mixing them.
Compute the surviving version per claim with a groupby transform of max over claim_version, then keep the lines whose claim_version equals that value. A transform keeps the frame at line grain, which a sort plus head(1) does not.
Drop whole claims whose surviving version carries frequency_code 8. A void is not a zero-dollar line, it retracts the claim, so filtering rows rather than claims leaves the prior version's lines behind.
Filter to claim_status 'paid' and state the assumption: denied, pended and reversed lines carry an allowed_amount that never became a liability, so including them overstates cost.
Restrict service_start_date to the month boundaries, then groupby member_id and sum. Sort descending and return.

Follow-up

A claim has versions 1, 7, 7 where the second replacement has fewer lines than the first. What does your code return and is that correct?
How would you handle a claim whose lines span two calendar months at the boundary of the reporting window?
The same logic has to run over 400 million lines in a warehouse. What changes, and what stays the same?

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.

Deduplicate claim versions before totalling allowed amounts

Easy · dedup · window functions · claim versions

medical_claim_line holds one row per service line per claim version: claim_line_id, claim_id, claim_version, frequency_code (1 original, 7 replacement, 8 void), member_id, service_start_date, procedure_code, allowed_amount, claim_status and adjudicated_at. Adjusted claims appear more than once, so summing every row double counts them. Return total allowed_amount by procedure_code for service_start_date in Q1 2025, counting only lines with claim_status 'paid' on the surviving version of each claim, which is the highest claim_version per claim_id, and dropping the claim entirely when that surviving version carries frequency_code 8. Resolve survivorship with a window function, not a self-join.

Approach

Resolve survivorship at claim_id, not claim_line_id. Carry MAX(claim_version) OVER (PARTITION BY claim_id) alongside every line and keep lines where claim_version equals it, because a replacement version can contain a different number of lines than the original.
Read the frequency_code of the surviving version only. A frequency_code 8 removes the claim outright; it does not revert payment to the prior version, so a void must delete the claim rather than promote version n-1.
Apply claim_status and the date filter after survivorship is resolved. Filtering paid lines first can strip the surviving version and silently elect a superseded one.
Group by procedure_code and sum allowed_amount, then state the paid-through date on the output because Q1 amounts keep moving until adjudication runout completes.
Sanity check the shrinkage: report how many claim_ids and how many dollars the dedup removed, so the reviewer can see the step did something.

Follow-up

Two rows share a claim_id and claim_version but differ on adjudicated_at. What do you do, and what does that imply about the extract?
How does the query change if the request is allowed_amount by paid_date rather than service_start_date, and which basis does a finance reconciliation want?
A void arrives for a claim already published in a closed month. How do you restate without rewriting history in the warehouse?

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.

Are you familiar with CDISC, SDTM, and ADaM data structures?

Medium

Approach

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.
Work from the decision backwards to the evidence you would need.

Follow-up

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

How do you ensure compliance with ICH guidelines and GCP in your data workflows?

Medium

Approach

Work from the decision backwards to the evidence you would need.
State your assumptions explicitly before working the problem.
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?

Redesign an adherence metric an outreach team cannot quietly inflate

Medium · gaming · adherence · denominator design · threshold bunching

An outreach team is paid on the share of members reaching proportion of days covered of 0.80 or higher in a therapeutic class, computed from pharmacy_claim (member_id, therapeutic_class_code, fill_date, days_supply, refill_number, reversal_flag, reversed_fill_id). The denominator is members with at least two fills in the class and at least 91 days of follow-up, over a 12-month window. Name three ways the team raises this number without any member taking more medication, then redesign the metric and its guardrails so each lever is closed or visible.

Approach

Attack the denominator first, because it is the cheapest lever. A member who fills once and abandons never enters the denominator at all, so the worst outcome in the class is invisible, and any effort spent on first-fill abandonment lowers the reported share by moving those members in. The incentive runs backwards.
Attack the window arithmetic second. With the denominator running from first fill to window end, a member whose first fill lands late in the year has a short denominator that is trivially easy to cover, so steering new starts to the last quarter raises the metric mechanically. Report by index quarter or fix the window and require the index fill in the first quarter.
Attack the threshold third. Days of supply dispensed late in the window count only up to the window end, but a fill placed 60 days before close, on a member whose last on-hand day has already passed, adds up to 60 covered days, about 0.16 of a 365-day denominator. That alone moves a member from 0.77 to 0.93 with no change in what anyone swallows.
Close the dispensing-side hole: a fill that is billed and reversed is not exposure. Exclude both rows of a reversed pair via reversal_flag and reversed_fill_id, and collapse overlapping fills by pushing each interval start to the later of fill_date and the prior interval's end, so early refills shift coverage forward rather than stacking.
Redesign the scorecard as a pair rather than a single number: the threshold share stays as the headline, and mean PDC plus the share at 0.90 or higher travel with it, because a distribution cannot be bunched at a threshold without showing it.
Add the guardrails that make each lever visible: first-fill abandonment rate (fills with refill_number 0 never followed by a second fill, plus reversed first fills), the share of covered days originating from fills in the final 60 days of the window, and the distribution of PDC in the bands just below and just above 0.80.

Worked solution (30 min)

Write the exposure-interval construction explicitly: drop reversed pairs, sort fills per member and class by fill_date, set each interval start to the greater of fill_date and the prior interval end, and truncate the final interval at the window end.
Write the denominator in two variants, the incumbent at-least-two-fills rule and an index-fill rule requiring the first fill in the first quarter with at least 273 days of follow-up, and state what each includes that the other does not.
Enumerate the three gaming levers with the arithmetic that makes each one work, including the 60-day end-of-window fill computed against a 365-day denominator.
Specify the headline metric plus the two companions (mean PDC, share at 0.90 or higher) and the three guardrails, each with numerator, denominator and window.
Write one sentence stating that dispensing is a proxy for taking and that the proxy overstates adherence in the direction of anyone who fills and does not take.

Expected result: A redesign in which: reversed fills are excluded and overlaps shift forward rather than stack; the denominator is anchored on an index fill in a fixed window so late starters cannot buy a short denominator; the threshold share is reported with mean PDC and the share at 0.90 or higher; and three guardrails ship with it, namely first-fill abandonment rate, the share of covered days contributed by fills in the last 60 days of the window, and the PDC distribution in the bands immediately below and above 0.80.

Verify it yourself

Hand-compute PDC for a member with fills on day 1, day 25 and day 200 at 30 days supply each in a 365-day window. Overlapping the day-25 fill forward gives 90 covered days and PDC of about 0.25; stacking incorrectly gives 90 days too but misplaces them, so check the interval endpoints, not just the total.
Compare the member count under both denominator rules. The index-fill rule should be strictly smaller or equal on early starters and should pull in first-fill abandoners the incumbent rule excluded.
Plot the PDC histogram around 0.80. A smooth distribution is consistent with real behaviour; a spike in the first band above the threshold is not.
Sum covered days sourced from fills in the final 60 days of the window across members who cross the threshold. A large share concentrated there is the end-of-window blitz showing up directly.

Follow-up

The share at 0.80 or higher rose four points and mean PDC rose 0.4 points. What is your reading, and what do you look at next?
Would you pay on 0.80 at all, or on the continuous measure? Say what each choice costs you.
Possession is not ingestion. Name a measurable consequence in another table that would corroborate a real adherence improvement, and state its own weakness.

Pooled persistency fell but no one lost coverage

Medium · interval sql · mix shift · cohort seasonality

Pooled 12-month enrollment persistency fell from 0.78 to 0.66 for the cohort anchored in a single month. member_enrollment carries span_id, member_id, plan_id, product_type, effective_date, termination_date, termination_reason_code, dual_eligible_flag and region_code. The metric allows at most one gap of 45 days or fewer. Product mix in that anchor month was not stable. Determine how much of the fall is real disenrollment rather than span bookkeeping or mix, and deliver a persistency series a contract owner could act on.

Approach

Inspect the query's unit of analysis first. Spans are not members, and a benefit or plan change closes one span and opens the next on the same day, so any logic that treats a non-null termination_date as loss of coverage counts an administrative event as churn.
Rebuild member-level coverage by unioning all spans per member and merging adjacent or overlapping ones, then apply the 45-day gap rule to the merged intervals rather than per span.
Cut the terminations by termination_reason_code. plan_change and eligibility_redetermination have entirely different meanings for a contract, and death is not churn you can address.
Split by product_type and stop pooling. Commercial open enrollment and managed medicaid redetermination produce structurally different churn on different calendars, and the pooled average of the two is a number that describes nobody.
Compare the anchor month to the same month in the prior year rather than to the adjacent month, because enrollment is strongly seasonal and an anchor month sitting on an open-enrollment or redetermination boundary is not comparable to its neighbour.
Apply the Kitagawa decomposition across product_type to separate how much of the 12 point fall is mix, meaning the anchor cohort simply contained more of a high-churn product, from how much is within-product deterioration.

Follow-up

Why 45 days, and what would change if the allowable gap were 30 or 60?
A member terminates and re-enrolls 90 days later under a different plan_id. Are they retained? Defend your answer against the opposite one.
What guardrail would catch someone improving persistency by declining to enroll members likely to churn?

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.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

Tell me about yourself and your journey in data science.

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

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

Why are you interested in joining Labcorp specifically?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
State the situation in two sentences and spend the rest on your reasoning.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

How did you know the outcome was caused by your change?
What did you decide not to do, and why?

How do you handle population sets and randomization in clinical trials?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Close with what you would do differently, concretely.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

What did you decide not to do, and why?
How did you know the outcome was caused by your change?

How does this role align with your long-term career goals?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
State the situation in two sentences and spend the rest on your reasoning.
Quantify the outcome, including what you would not claim credit for.

Follow-up

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