Interview guides · Experian Health

Experian Health Data Scientist Interview Questions & Guide 2026

Data scientist prep for Experian Health candidates: drill A/B design, power and minimum detectable effect on original exercises.

The 60-second brief

As a Data Scientist at Experian Health, you sit at the critical intersection of advanced analytics and healthcare technology. Your work directly impacts how healthcare providers, payers, and pharmacies manage information, improve patient outcomes, and optimize financial performance. By leveraging massive, complex datasets, you translate raw information into actionable intelligence that helps streamline the healthcare revenue cycle and patient access.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

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

Overview

You will be expected to move beyond simple model building; the role requires a deep understanding of how your algorithms influence real-world clinical and business decisions. Whether you are developing predictive models for patient risk, automating administrative workflows, or deploying machine learning solutions in production environments, your contributions are fundamental to the company’s goal of creating a more efficient and transparent healthcare ecosystem.

The role is highly collaborative. You will frequently interface with cross-functional teams, including Product Managers who may have varying levels of technical expertise, requiring you to translate complex data science concepts into clear business value.

The Experian Health interview process

Experian Health 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

Technical Assessment

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Panel Interviews

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Where candidates lose points

Pre-post evaluation on a high-cost or high-risk cohort

Cohorts selected on an extreme value of the outcome regress toward the mean on their own. Members identified as the top 1 percent of spend in one year spend far less the next, whether or not anyone intervenes, because the selecting year captured both chronic severity and one-off events. A pre-post design on such a cohort will report savings every time. A concurrent comparison group selected by the same rule in the same period, or a regression discontinuity at the selection threshold, is the minimum credible design.

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.

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

Crediting a treatment for regression to the mean

Selecting a group because it is extreme (lowest-engagement users, accounts having their worst month, the bottom decile of a score) moves that group's expected next-period value back toward the average even under no treatment, by exactly as much as the selecting measure is imperfectly correlated with its own later value. Compare against units that met the same selection rule and went untreated, or use two pre-periods so the bounce-back is visible before the intervention starts. A pre-post number on a group chosen for being extreme measures the selection rule, not the treatment.

Questions & practice

How do you evaluate model performance using metrics like AUC-ROC?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

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

When and why would you choose deep learning over traditional machine learning models?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.
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?

Four data-quality rules over corrected and cancelled lab results

Easy · data quality · record supersession · unit drift

lab_result has result_id, order_id, patient_id, loinc_code, value_numeric, value_text, units, reference_low, reference_high, specimen_collected_ts, resulted_ts, result_status (preliminary, final, corrected, cancelled), supersedes_result_id. Return one row per rule with a failing count, a failing share, and one example result_id, for these rules: more than one surviving row per (order_id, loinc_code) after resolving corrections and dropping cancelled; resulted_ts earlier than specimen_collected_ts; a numeric result whose unit is not the modal unit for its loinc_code; value_numeric and value_text both null on a row with result_status 'final'.

Approach

Resolve the correction chain before anything else. Drop result_status 'cancelled', then within each (order_id, loinc_code) keep the row whose result_id appears in no other row's supersedes_result_id. That terminal node is the surviving value and the rule holds even if a correction is loaded with a backdated timestamp.
Build each rule as a boolean mask over a named frame rather than as a filtered copy, so the failing share has an explicit, per-rule denominator. Rule three is eligible only on rows with a non-null value_numeric; rule four only on final rows.
For the unit rule, derive the modal unit per loinc_code from the data instead of hardcoding an expected unit. The failure being hunted is one analyte reported in two units whose values differ by a fixed factor, which no range check catches because both sets of numbers look plausible.
Emit a tidy frame with columns rule, eligible_rows, failing_rows, failing_share, example_result_id. A printed report cannot be diffed between runs; a frame can be stored and alerted on.
Order the output by failing_share descending so the run has a lead finding rather than four equal-weight lines.

Worked solution (25 min)

Drop cancelled rows, then compute the set of result_ids referenced by supersedes_result_id and keep rows not in that set.
Rule one: group the survivors by (order_id, loinc_code) and count rows above 1.
Rule two: mask on resulted_ts < specimen_collected_ts over rows where both timestamps are present.
Rule three: compute the mode of units per loinc_code over rows with non-null value_numeric, then mask rows whose unit differs from it.
Rule four: mask rows with result_status 'final' and both value columns null. Assemble the four results into one frame with counts, shares and an example id.

Expected result: A four-row frame. After the resolver, rule one should report zero failing groups on a well-formed extract; a non-zero count there means the supersession chain branches or a duplicate load occurred, and that is the finding to lead with rather than a percentage.

Verify it yourself

Running the resolver a second time on its own output is a no-op, and exactly one row survives per (order_id, loinc_code).
No result_id appearing in any supersedes_result_id survives the resolver.
Seed one row with a converted unit for an analyte otherwise reported in a single unit and confirm rule three catches exactly that row and nothing else.
Each rule's failing_share uses its own eligible_rows, so a rule with no eligible rows returns share 0 rather than a division error.

Follow-up

The modal-unit rule fires on 8 percent of one analyte. How do you decide whether that is a unit-conversion bug or a second legitimate assay?
What would you add to catch a value that is inside its reference range but physiologically impossible?
Which of these four rules should block a downstream pipeline and which should only alert, and why?

How do you ensure your code is scalable and production-ready?

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

How would you verify this result without re-running the same query?
How does the query change if the join becomes one-to-many?

Can you solve these two coding problems (typically LeetCode easy-medium level)?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Check whether any join is one-to-many before aggregating, or the sums inflate.
State the window function and its partition and ordering out loud before writing it.

Follow-up

How does the query change if the join becomes one-to-many?
How would you verify this result without re-running the same query?

Explain how you would optimize a query to handle large-scale datasets.

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which table is the grain you start from, and join outward from it.

Follow-up

How does the query change if the join becomes one-to-many?
What breaks if events arrive late or out of order?

What is your process for integrating RAG (Retrieval-Augmented Generation) into a data pipeline?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.

Follow-up

How would you verify this result without re-running the same query?
How does the query change if the join becomes one-to-many?

Find inpatient encounters with no resulted lab during the stay

Medium · anti-join · null semantics · event matching

encounter holds encounter_id, patient_id, encounter_type, admit_ts, discharge_ts and facility_id. lab_result holds result_id, order_id, patient_id, encounter_id (NULL for outpatient standing orders), loinc_code, specimen_collected_ts, resulted_ts and result_status. Return every inpatient encounter discharged in 2025 with no final or corrected lab_result linked to it. A colleague wrote WHERE encounter_id NOT IN (SELECT encounter_id FROM lab_result) and got zero rows back. Say why, write the correct query, then say what changes when labs must instead be matched on patient_id with the specimen collected between admit_ts and discharge_ts.

Approach

Name the mechanism rather than the symptom. x NOT IN (subquery) expands to x <> y1 AND x <> y2 AND ..., and a single NULL y makes that chain UNKNOWN, never TRUE. Since lab_result.encounter_id is nullable by design for standing orders, this query can only ever return zero rows.
Rewrite as NOT EXISTS, or LEFT JOIN with an IS NULL filter. Both treat a NULL key as no match rather than as unknown. Adding IS NOT NULL inside the NOT IN subquery also works, but NOT EXISTS is the habit that survives someone making another column nullable later.
Put the result_status predicate inside the correlated subquery or the ON clause, not in an outer WHERE. Outside, it turns the anti-join back into an inner join and an encounter whose only labs were cancelled disappears instead of qualifying.
For the timestamp variant, match on patient_id with specimen_collected_ts inside the stay rather than resulted_ts. A specimen drawn an hour before discharge can result the next day, and anchoring on resulted_ts would wrongly call that stay lab-free.
Restrict to encounter_type 'inpatient' and discharge_ts in 2025, and report open encounters with a NULL discharge_ts as a separate count instead of letting the filter swallow them.

Worked solution (20 min)

Demonstrate the cause: SELECT COUNT(*) FROM lab_result WHERE encounter_id IS NULL returns a non-zero number.
Write the NOT EXISTS version with result_status IN ('final','corrected') inside the correlated subquery.
Write the LEFT JOIN version with the same predicate in the ON clause and WHERE l.result_id IS NULL, then compare counts.
Build the timestamp variant joining on patient_id with specimen_collected_ts >= admit_ts AND < discharge_ts.
Report both counts side by side and explain which encounters differ between the two definitions.

Expected result: One row per qualifying inpatient encounter with encounter_id, patient_id, facility_id, admit_ts and discharge_ts. The count is strictly below total 2025 inpatient discharges and strictly above zero, while the NOT IN version returns exactly zero rows.

Verify it yourself

NOT EXISTS and LEFT JOIN with IS NULL return identical row counts.
Adding AND encounter_id IS NOT NULL inside the NOT IN subquery reproduces the NOT EXISTS count exactly.
No returned encounter_id appears anywhere in lab_result with result_status in ('final','corrected').
The patient-and-timestamp variant returns fewer encounters than the encounter_id variant, since it also catches labs whose encounter link is missing.

Follow-up

Write the LEFT JOIN form and say exactly where the result_status predicate must sit for the two forms to agree.
How would you separate encounters whose only labs were cancelled from encounters with no lab rows at all?
Some encounters carry member_id NULL because the person index did not match. What does that do to a payer-side version of this measure?

Choose the primary metric for a high-risk care management program

Medium · regression to the mean · guardrails · comparison group · risk adjustment

A care-management team enrolls members in the top 1 percent of prospective_risk_score and wants one number it is judged on each quarter. The proposed metric is allowed PMPM for enrolled members, built from medical_claim_line.allowed_amount and pharmacy_claim.allowed_amount over member-months from member_enrollment. Propose the primary metric, two guardrails, and the reporting cadence. In three sentences, explain why the proposed metric will show large savings in year one even if the program has no effect, and what design change removes that.

Approach

Name the failure before proposing anything. Members are selected on an extreme value of a noisy quantity, and the selecting year captured chronic severity plus one-off events that do not repeat. The cohort's spend falls the following year whether or not anyone intervenes, so a pre-post comparison on this cohort reports savings every time.
Quantify it rather than asserting it: compute the selection-year and following-year allowed PMPM for last year's top-1-percent cohort and for the whole book. The cohort's decline is usually several times the population's, and that gap is the free saving any pre-post design will bank.
Rebuild the metric as a difference, not a level. Primary: the difference-in-differences in risk-adjusted allowed PMPM between enrolled members and a concurrent comparison group selected by the identical rule in the same period, drawn from capacity overflow, wait-list, or members just below the selection threshold.
Handle the exposure denominator honestly. Member-months come from coverage spans, partial months count fractionally, and death and disenrollment are not neutral: a member who dies stops accruing cost and member-months together, so state whether decedents stay in the analysis and for how long.
Pick guardrails that catch the two bad ways cost falls: ambulatory care sensitive admissions per 1,000 member-years, and emergency department visits per 1,000 member-years, both risk-adjusted. A cost drop from care that was avoided rather than delivered shows up in one of those two within a quarter or two.
Set the cadence to incurred quarters reported at a stated paid-through date, withholding or completion-factoring the three most recent incurred months, and say that the quarter is restated until runout completes.

Follow-up

The comparison group is wait-listed members. What is the argument that they are not comparable, and what would you show to answer it?
If the difference-in-differences estimate is negative but the parallel-trends check on the two pre-periods fails, what do you report?
Enrollment into the program takes 45 days from identification. How does that interval create immortal time, and what do you do about it?

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?

Readmission rate rose while every facility improved

Medium · simpsons paradox · mix shift · risk adjustment

Quarter over quarter the 30-day unplanned readmission rate for an attributed population rose from 14.2 to 15.1 percent, yet all eleven facilities in encounter show a flat or falling rate over the same period. You have encounter (facility_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, is_planned_admission, index_encounter_id) and twelve months of prior medical_claim_line for comorbidities. Explain the direction reversal numerically, and state what the population-level number should be reported as instead.

Approach

Confirm the reversal is arithmetic, not a definition difference, by recomputing both the facility rates and the pooled rate from the same query with the same index-stay exclusions.
Decompose the pooled change exactly with a Kitagawa split. With facility weights w and within-facility rates r, the change equals the sum over facilities of (w_1 - w_0) times (r_0 + r_1)/2, the mix term, plus the sum of (w_0 + w_1)/2 times (r_1 - r_0), the within term. If the mix term exceeds the total change and the within term is negative, the reversal is volume shifting toward facilities with higher baseline rates.
Ask why the weights moved. Check index-stay volume by facility, and check whether an exclusion changed behaviour: acute-to-acute transfers (discharge_disposition 'transfer_acute') must not open an index stay, and if one facility began transferring more, its denominator shrinks toward its sickest retained patients.
Check case mix inside the facilities too, using drg_code and comorbidities drawn from the 12 months of claims before admit_ts, excluding complications of the index stay itself. A facility rate can be flat while its case mix worsens.
Replace the raw pooled rate with an observed over expected ratio, where expected is the sum of predicted probabilities from a model fitted on age, sex and prior comorbidities. Raw rates are not comparable across a population whose facility and case mix is moving.

Follow-up

Your O/E ratio is 1.04 with a 95 percent interval of 0.96 to 1.13. What does that let you say to a facility, and what does it not?
If one facility contributes 40 percent of index stays, how does that change how you present the decomposition?
How would you handle a member with two index stays in the quarter, where the second is also a readmission from the first?

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.

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Describe a time you had to explain a complex model to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

What did you decide not to do, and why?
What would you do differently if you ran that project again?

Why do you want to apply your data science skills specifically within the healthcare domain?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
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?
What would you do differently if you ran that project again?

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?