Humana Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at Humana, you will play a pivotal role in transforming healthcare delivery through data-driven decision-making. Humana is one of the nation's largest healthcare insurers, and its data science teams are tasked with solving some of the most complex challenges in the industry. Your work will directly impact millions of members by optimizing clinical outcomes, improving patient experiences, and streamlining operational efficiencies.

Nearly every loop contains a round whose deliverable is a recommendation to someone non-technical. Practise stating a conclusion, the confidence attached to it, and the cost of being wrong in each direction, because that triple is the artifact being graded.

Focus areas

• Build member-month denominators from overlapping coverage spans
• Risk-adjust outcomes before comparing providers or panels
• Correct for claims runout before reporting recent months

Overview

In this role, you will work within a highly collaborative environment, partnering with clinical experts, product managers, software engineers, and business leaders. The models you build will help predict health risks, prevent chronic condition progression, and ensure that members receive the right care at the right time. Whether you are optimizing provider networks or developing predictive models to identify fraud, your contributions will have a tangible, positive impact on human lives.

This position requires a unique blend of deep technical expertise, business acumen, and a passion for healthcare. You will navigate massive, complex datasets, utilizing advanced machine learning algorithms, statistical modeling, and data visualization tools to translate raw data into actionable clinical and business strategies.

The Humana interview process

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

Automated Screening

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

What to demonstrate

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

How to prepare

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

Hiring Manager Discussion

Expect a live problem with pieces of it missing, closer to a conversation than an exam. A metric moved, or somebody wants to know whether a change worked, and you are asked how you would find out. The manager is watching the first ninety seconds, specifically whether you establish what decision hangs on the answer before you start proposing methods. Candidates who open with a technique get steered back. Once the decision is clear, describe what the data would look like if the story were true, and say what you would accept as evidence that it is not.

What to demonstrate

• Whether you fix the decision the analysis serves before choosing an approach
• How you continue when you are told the data you just asked for does not exist
• Whether you state what would change your mind, not only what would confirm the hypothesis you started with
• How you size an effect before you have measured it

How to prepare

• Take a metric you know well and practise explaining in under two minutes the four things that could have moved it and how you would separate them
• Pick a recent launch or experiment and write the single number you would ask for first, plus what you would conclude if it came back flat
• Practise being interrupted: have someone remove a data source halfway through your answer and carry on without restarting

Technical Evaluation

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

Leadership and Team Meeting

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

• Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
• Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
• How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

• Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
• Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
• Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

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.

Treating clinical measurements as missing at random

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

Solving silently instead of narrating the reasoning

Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.

Writing SQL without stating NULL and tie-breaking behaviour

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

Questions & practice

Explain the bias-variance tradeoff and how you would address high variance in a predictive model designed to forecast patient readmissions.

Medium · statistics and probability

Approach

1. Translate the result into the decision it informs, in one plain sentence.
2. Say what the estimate is of, and over what population it generalises.
3. Write down the assumption the method needs before you use the method.

Follow-up

• Which assumption here is most likely to be violated in practice?
• What sample size would you need to detect an effect half this size?

Have you ever had to build a data visualization dashboard to track model performance or business KPIs? Describe your design choices and the tools you used.

Medium · machine learning and modelling

Approach

1. Check what information would not exist at prediction time, and exclude it.
2. Say how the offline result would be validated online before it is trusted.
3. Set a baseline first, so any model has something honest to beat.

Follow-up

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

Walk us through how you would evaluate the performance of a clinical classification model. Which metrics (Precision, Recall, F1-Score, ROC-AUC) would you prioritize and why?

Medium · machine learning and modelling

Approach

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

Follow-up

• How would you choose the decision threshold, and who owns that choice?
• Where could label leakage enter this setup?

Simulate how ignoring clustering inflates the false positive rate

Medium · simulation · design effect · type i error

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

Approach

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

Worked solution (30 min)

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

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

Verify it yourself

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

Follow-up

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

How would you optimize a slow-running SQL query that joins multiple large tables containing patient clinical records?

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. 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?
• What breaks if events arrive late or out of order?

Explain the difference between RANK, DENSE_RANK, and ROW_NUMBER in SQL, and describe a healthcare scenario where you would use each.

Medium · sql and data manipulation

Approach

1. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
2. Say which table is the grain you start from, and join outward from it.
3. Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Given a table of member transactions, write a SQL query using window functions to rank members by their total healthcare expenditure within each region.

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

• How would you verify this result without re-running the same query?
• What breaks if events arrive late or out of order?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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)

1. Demonstrate the cause: SELECT COUNT(*) FROM lab_result WHERE encounter_id IS NULL returns a non-zero number.
2. Write the NOT EXISTS version with result_status IN ('final','corrected') inside the correlated subquery.
3. Write the LEFT JOIN version with the same predicate in the ON clause and WHERE l.result_id IS NULL, then compare counts.
4. Build the timestamp variant joining on patient_id with specimen_collected_ts >= admit_ts AND < discharge_ts.
5. 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?

How do you manage your time and prioritize tasks in an Agile environment with daily stand-ups and tight sprint deadlines?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. Fix the population and the time window before naming any metric.

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?

Walk me through a recent data science project you led from inception to deployment. What challenges did you face, and how did you overcome them?

Medium

Approach

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

Follow-up

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

Score trial sites without rewarding enrollment that does not stick

Medium · site scorecards · shrinkage · guardrails · gaming

A clinical operations group ranks sites on subjects randomized per activated site-month, using study_subject_visit (study_id, site_id, subject_id, arm_code, randomization_ts, site_activation_date, visit_name, visit_number, visit_status, primary_endpoint_value, protocol_deviation_flag, deviation_severity). Bottom-ranked sites lose allocation on the next study. Define the primary metric, two guardrails, the handling of sites activated too recently to rank, and the estimator you rank on. Then state what a site under allocation pressure does to the number, and which guardrail sees it.

Approach

1. Move the numerator from randomized to randomized and retained through the protocol-defined primary endpoint visit. A subject who randomizes and withdraws consumes screening, drug supply and monitoring while contributing nothing to the analysis population, so counting them rewards the wrong behaviour precisely when a site is under pressure.
2. Build the denominator as exposure, not sites: site-months from site_activation_date to the earlier of period end and database lock, per study. Ranking on a raw count instead of a rate confuses a large site with a fast one.
3. Handle young sites by reporting rather than dropping. Sites with fewer than a stated minimum exposure are shown in an insufficient-exposure band, because excluding them is how a non-enrolling tail disappears from a scorecard, and the non-enrolling tail is usually the larger operational problem.
4. Rank on a shrunken estimate, not the raw ratio. With a few site-months of exposure the ratio has enormous variance, and a site with two site-months and one randomization will top an unshrunken table. Partial pooling toward the study mean, or a simple empirical-Bayes posterior mean, prevents rank being an artefact of small exposure.
5. Pick guardrails that read the two pressure responses. A site that loosens eligibility judgement shows an implausibly low screen-failure rate, computed as subjects with randomization_ts NULL and visit_status screen_failed over subjects screened. A site that pushes visits to hit counts shows up in major protocol deviations per 100 completed visits, reported separately from minor ones because a single major deviation can remove a subject from the per-protocol set.
6. Report the companion the median hides: the share of activated sites with no randomization by day 90. A median computed only over sites that eventually randomized is silent about the sites that never did.

Worked solution (30 min)

1. Define the numerator as subjects with non-null randomization_ts whose primary endpoint visit reaches visit_status completed with a non-null primary_endpoint_value, per study and site.
2. Compute site-months of exposure from site_activation_date to period end or database lock, and set the minimum exposure below which a site is banded rather than ranked.
3. Compute the raw rate, then the shrunken estimate by partial pooling toward the study mean, and print both columns side by side so the shrinkage is visible rather than hidden.
4. Specify the two guardrails with numerator, denominator and window, keeping major and minor deviations as separate series.
5. Add the no-randomization-by-day-90 share across all activated sites, so the non-enrolling tail appears on the same page as the ranking.

Expected result: A scorecard whose primary metric is randomized-and-retained subjects per activated site-month per study, ranked on a shrunken estimate with the raw rate shown beside it; sites below a stated minimum exposure reported in an insufficient-exposure band rather than excluded; guardrails of screen-failure rate and major protocol deviations per 100 completed visits reported separately from minor ones; and a companion share of activated sites with no randomization by day 90.

Verify it yourself

• Compare raw and shrunken rankings. The largest rank movements should be concentrated among the lowest-exposure sites; if a high-exposure site moves far, the pooling is misconfigured.
• Confirm the denominator's site-months sum matches an independent count of months between activation and period end across all sites.
• Check that screen failures are being counted at all: a site reporting a zero screen-failure rate across dozens of screened subjects is a data-capture problem or an eligibility-judgement problem, and both need an answer.
• Verify that excluding the insufficient-exposure band does not change the study-level mean materially; if it does, those sites carry more of the study than the banding implies.

Follow-up

• A site has the best randomized-and-retained rate and the worst major deviation rate. What do you recommend, and what would change your answer?
• Your shrunken ranking moves a site from 3rd to 14th. How do you explain shrinkage to the site's operations lead in two sentences?
• Primary endpoint retention takes 18 months to observe. What do you rank on in the meantime, and what bias does that interim choice introduce?

Adherence share collapsed the month a feed was added

Easy · pdc · interval sql · identity resolution

The share of members with proportion of days covered at or above 0.80 in one therapeutic class fell from 0.71 to 0.58 in a single month. pharmacy_claim carries fill_id, member_id, ndc_code, therapeutic_class_code, fill_date, days_supply, quantity_dispensed, refill_number, reversal_flag, reversed_fill_id and source_system_code. There is no person-level key above member_id and no enrollment table on hand, so every diagnostic has to run on pharmacy_claim alone. That month the pipeline began ingesting a second pharmacy source. Determine whether adherence actually changed, and hand back a PDC query you would defend line by line.

Approach

1. Compute controls that depend on neither member identity nor the PDC code: total fills in the class per month, and total days_supply dispensed in the class per month. Both are sums over rows, so neither moves when one person's fills split across two member_ids, which is exactly what a per-member control cannot promise. If both are flat while PDC falls, no dispensing behaviour changed and the computation or the keys did.
2. Check days_supply for nulls and zeros by source_system_code. If the new source omits days_supply and the code coalesces null to zero, covered days vanish and PDC falls even though the fills are there.
3. Check for member identity splitting using the columns you actually have. There is no person key above member_id, so the split has to be found by its footprint: distinct member_id in the class jumping at the break while total fills stay flat, and a rise in the share of member_ids whose earliest fill for a given ndc_code carries refill_number above zero, which is a continuation history that lost its beginning. When one person is split, each id may fall below the two-fill denominator requirement and drop out, and the ids that do survive carry artificial gaps.
4. Check reversals. A reversed fill is two rows, the fill and a row with reversal_flag TRUE pointing at reversed_fill_id, and both must be excluded from exposure. If the new source expresses reversals differently and reversed_fill_id does not link, phantom coverage appears, which pushes PDC up rather than down, so use this to rule the cause in or out.
5. Verify the denominator and window are unchanged: members with at least two fills in the class and at least 91 days of follow-up, over a stated fixed 12-month window, never a mixture of window lengths.
6. Write the PDC query so overlapping refills shift the next interval's start to the later of fill_date and the prior interval's end, so a day is covered at most once, and cap at one day per calendar day.

Follow-up

• Why is PDC preferred to medication possession ratio when early refills are common?
• You want to compare adherent and non-adherent members on hospitalisation. What is wrong with classifying on a 12-month PDC and then following from day zero?
• A member switches to a different drug in the same class mid-year. How does your interval logic handle that, and is the class the right grain?

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.

Bring numbers, including the ones you are unsure of

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

Why do you want to work in the healthcare industry, and specifically at Humana?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Push back on an over-broad extract without blocking the work

Hard · minimum necessary · de-identification · governance

A researcher asks for a one-off extract covering 12,000 members: member_id, date of birth, five-digit residential postal code, full lab_result rows including value_text, and clinical note text, held on their laptop for a retrospective analysis of one lab analyte over time. They describe it as de-identified because names are excluded. A flat refusal is not an acceptable answer. Deliverable: the column list you will actually provide with a justification per retained field, the sentence explaining why free text defeats an enumerated-identifier approach, your small-cell rule for the published stratification, and what you deliver this week.

Approach

1. The probe is whether privacy is a design skill for you or a compliance reflex. Flat refusal and unquestioned fulfilment both fail, for opposite reasons.
2. Reconstruct the analysis from the research question rather than from the request. A longitudinal trend in one analyte needs a stable pseudonymous subject key, the analyte's loinc_code, value_numeric, units, the reference bounds, and a date. It does not need names, notes, or a precise birth date.
3. Replace each identifier with the least precise version that still answers the question: a salted per-study pseudonym instead of member_id, age in years or an age band instead of date of birth, a consistent per-subject date offset if only intervals matter, and a coarser geography than five-digit postal code.
4. Explain the free-text problem in one sentence. Removing an enumerated list of identifier fields does not de-identify a note, because notes carry names, relationships, employers, dates and rare clinical detail in prose, so only a reviewed extraction into structured fields or a formal statistical determination addresses it.
5. Set the publication rule before the analysis runs. Suppress cells below a stated threshold and suppress complements too, because a reader holding a row total and all but one cell recovers the suppressed count by subtraction.
6. Keep them moving. Deliver the reduced extract into a controlled environment this week rather than onto a laptop, and offer to derive any note-based variable yourself as a structured flag so they never need the prose.

Follow-up

• They say the age band loses resolution their model needs. What do you do?
• How do you set the suppression threshold, and who signs off on it?
• The analysis later needs linking to a second data source. What changes?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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?