Interview guides · Axle

Axle Data Scientist Interview Questions & Guide 2026

Diagnose a metric move for Axle data scientist prep: split by segment, rule out a logging change, separate mix shift from behaviour.

The 60-second brief

A Data Scientist at Axle operates at the critical intersection of advanced biomedical research and high-scale information technology. You are not just analyzing data; you are building the computational infrastructure that powers life-changing discoveries at premier research facilities, including the National Institutes of Health (NIH). Your work directly enables researchers to unlock insights from complex multi-omics, behavioral, and clinical datasets.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Focus areas

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

Overview

This role is inherently interdisciplinary and highly collaborative. You will bridge the gap between experimental scientists, bioinformatics experts, and software engineers to translate abstract research hypotheses into reproducible, production-grade data pipelines. Whether you are characterizing organoid fidelity or modeling digital health behaviors, your contributions directly impact the pace of scientific advancement and the quality of decision-making in translational research.

Expect a high degree of intellectual challenge and technical rigor. You will work with diverse data modalities—ranging from single-cell RNA sequencing to large-scale behavioral time-series—and be expected to maintain the highest standards of reproducibility and technical documentation. Success in this role requires a unique blend of scientific curiosity, statistical mastery, and an engineering-first mindset.

The Axle interview process

Axle candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Technical Screening

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

Deeper-Dive Rounds

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Rates built on member counts rather than exposure

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

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.

Treating a non-significant result as proof of no effect

Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.

Reporting a mean for a heavy-tailed metric without saying what it hides

For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.

Questions & practice

Explain the trade-offs between different hypothesis testing methods when dealing with skewed omics data distributions.

Medium · statistics and probability

Approach

Quantify uncertainty explicitly rather than reporting a point estimate alone.
Sanity-check the answer against a simple bound or a simulated case.
Translate the result into the decision it informs, in one plain sentence.

Follow-up

What sample size would you need to detect an effect half this size?
How would you explain this result to someone who does not know statistics?

Bootstrap a confidence interval for a ratio-of-sums PMPM

Medium · bootstrap · ratio estimator · resampling unit

You have one row per member for a fully run-out incurred year: member_id, member_months, allowed_amount. Report allowed PMPM, defined as sum(allowed_amount) divided by sum(member_months), with a 95 percent confidence interval from a bootstrap you write yourself. No library resampler. Resample members with replacement, recompute the ratio on each of 2,000 replicates, take a percentile interval, and seed the generator so the result reproduces. In two sentences, state why resampling claim lines instead of members would be wrong.

Approach

Name the estimator precisely: PMPM is a ratio of sums, not the mean of per-member ratios. The two differ whenever exposure correlates with spend, and it does, because members who churn out mid-year are systematically different from members covered all twelve months.
Resample the unit of independence, which is the member, and recompute numerator and denominator together on the same draw. Recomputing only the numerator against a fixed denominator understates variance and is a common slip.
Pull member_months and allowed_amount into numpy arrays once, then draw indices with rng.integers(0, n, size=n) per replicate. Index draws over arrays run one to two orders of magnitude faster than DataFrame.sample, which is the difference between 2,000 replicates and 200.
Take the 2.5th and 97.5th percentiles of the replicate ratios. Expect an interval that is asymmetric around the point estimate, because healthcare spend is right skewed. That asymmetry is the method working, not a bug to centre away.
Answer the line-resampling question concretely: the many lines of one high-cost member are not independent draws, so line resampling shrinks the interval toward zero width, and member_months has no line-level analogue, so the denominator becomes undefined.

Worked solution (25 min)

Compute the point estimate as allowed.sum() / months.sum() on the full sample.
Extract both columns to float numpy arrays and create rng = np.random.default_rng(seed).
Loop 2,000 times: idx = rng.integers(0, n, n); ratio = allowed[idx].sum() / months[idx].sum(); store it.
Take np.percentile(replicates, [2.5, 97.5]) and report the point estimate with that interval and the seed.

Expected result: A point estimate plus a 95 percent percentile interval that is wider on the upper side than the lower. With tens of thousands of members and a typical commercial cost distribution the relative half-width lands on the order of a few percent; an interval a small fraction of a percent wide is the signature of having resampled rows rather than members.

Verify it yourself

Re-running with the same seed reproduces both bounds exactly.
Raising replicates from 2,000 to 8,000 moves each bound by less than its third significant figure.
The mean of the replicate ratios sits close to the point estimate; a large gap indicates a bug in the resampler rather than skew.
Replicate denominators cluster tightly around the observed total member-months, which confirms the draw size is n rather than a fixed constant.

Follow-up

Half a percent of members have a transplant or a long NICU stay. Does a percentile interval still have nominal coverage, and what would you do about the tail?
The question changes to comparing PMPM across provider panels. What becomes the resampling unit and why?
How would you use the same replicates to test whether this year's PMPM differs from last year's, given the same members appear in both?

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?

How would you use SQL window functions to calculate rolling averages of patient health metrics over a specific time window?

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
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.

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 that joins multiple massive genomic datasets to minimize compute time.

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?
What breaks if events arrive late or out of order?

Reconstruct drug coverage intervals and compute proportion of days covered

Hard · gaps and islands · window functions · adherence

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

Approach

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

Worked solution (40 min)

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

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

Verify it yourself

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

Follow-up

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

If a key research dashboard suddenly shows a significant metric drop, what is your systematic process for diagnosing the root cause?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Decompose the metric into the rates that drive it, and say which one you would check first.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

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

How would you design a set of metrics to evaluate the success of a new organoid quality control protocol?

Medium · product sense and metrics

Approach

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

Follow-up

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

How do you define "success" for a new bioinformatics pipeline, and what metrics would you track to ensure its long-term reliability?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

How do you determine the required sample size to ensure statistical significance in a clinical study with high variance?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
State the primary metric and the minimum effect worth shipping, then size the test.
Say whether units interfere with each other, and switch design if they do.

Follow-up

What would you do if you could not randomise at all?
How would you handle interference between treated and control units?

Describe the common experimentation pitfalls you have encountered when designing tests for behavioral research.

Medium · a/b testing and experimentation

Approach

Name the guardrails that would stop a launch even on a positive primary result.
Say whether units interfere with each other, and switch design if they do.
Name the randomisation unit first; it decides the variance and what the test can detect.

Follow-up

What would you conclude if the result is positive but the test is underpowered?
What would you do if you could not randomise at all?

Power a readmission trial on a rare binary outcome

Easy · power analysis · minimum detectable effect · rare events

A care-transitions programme (pharmacist call plus a seven-day follow-up visit) will be tested on index inpatient discharges drawn from encounter, excluding planned admissions, acute-to-acute transfers, AMA discharges, and dispositions of expired or hospice. Baseline 30-day unplanned readmission on eligible stays is 15.8 percent. Leadership wants to detect a 1.5 percentage point absolute reduction, two-sided alpha 0.05, 80 percent power, 1:1 individual randomisation at discharge. Give the required index stays per arm, and the minimum detectable absolute effect if only 4,000 eligible stays per arm accrue in the study window.

Approach

Write the two-proportion sample size formula and state its preconditions: normal approximation, two-sided test, equal allocation, and the outcome measured on index stays rather than on members, since one member can contribute several index stays.
Plug in p1 = 0.158, p2 = 0.143, and (z_0.975 + z_0.80)^2 = (1.960 + 0.842)^2 = 7.849. Report the answer as index stays, and separately as the calendar time it implies given the observed monthly eligible volume.
Invert the same formula for the fixed-N case: solve for delta with n = 4,000 per arm, and say whether you used pooled or arm-specific variance because the two answers differ by about 0.1 percentage points.
Translate the MDE into the relative reduction it implies and hand it back to leadership before the study starts, so the question becomes whether that effect is worth running rather than whether the null result was a failure.
Flag the two adjustments that will make the real number larger: if a member can appear more than once, cluster on member_id, and if the programme is delivered by unit or by discharging team rather than to individuals, an individual-level calculation is the wrong model entirely.

Worked solution (20 min)

Compute (1.960 + 0.842)^2 = 7.849.
Compute the variance sum: 0.158 x 0.842 = 0.1330, 0.143 x 0.857 = 0.1226, total 0.2556.
n per arm = 7.849 x 0.2556 / 0.015^2 = 7.849 x 1,136 = 8,916, so round to about 8,920 per arm.
For the MDE at n = 4,000, solve delta = sqrt(7.849 x variance sum / 4,000); with arm-specific variance this gives 0.0221, with pooled variance at p = 0.158 it gives 0.0229.
Express the MDE as a relative effect: 0.023 / 0.158 is about a 15 percent relative reduction.

Expected result: About 8,900 index stays per arm (roughly 17,800 total) to detect 1.5 percentage points. With 4,000 per arm the MDE is about 2.2 to 2.3 percentage points absolute, which is a 14 to 15 percent relative reduction; state which variance convention you used.

Verify it yourself

Halving the target effect should roughly quadruple N: at 3.0 points the formula gives about 2,130 per arm, and 8,916 / 2,130 is about 4.2.
Confirm the normal approximation holds: expected events per arm are 0.158 x 8,916 = about 1,409, far above the usual threshold of 10.
Divide required N by observed monthly eligible index stays and check the implied study duration against the decision deadline.

Follow-up

The same programme is delivered by discharge unit, not to individual patients. What changes in the calculation?
How would you handle a member who has three eligible index stays during the study window?
Leadership proposes powering on a composite of readmission or ED revisit instead. What does that buy and what does it cost?

Protocol deviations fell as new sites came online

Hard · reporting lag · denominator mix · clustering

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

Approach

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

Follow-up

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

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

Repeat the depth protocol on the second-ranked area with the same six-problem structure.
Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

Re-run the six prompts from day one under the same clock and compare both correctness and time.
Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

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.

Tell me about a time you had to manage a disagreement regarding data methodology with a project lead.

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?

Disagree with a product manager about an adherence nudge

Medium · immortal time · confounding · disagreement

A product manager proposes shipping a refill-reminder feature to the whole member base, citing an internal analysis: members with 12-month proportion of days covered at or above 0.80 had 31 percent fewer admissions than members below it. The roadmap date is three weeks out and engineering is scoped. You believe the comparison builds survival and continuous coverage into the exposure definition, and is separately confounded by the kind of member who refills on time. Deliverable: what you say in the room, what you send afterwards, and the smallest study you would accept as sufficient to ship.

Approach

The probe is whether you can be specific and non-territorial at once. A disagreement that arrives as 'the analysis is flawed' costs you the room; one that names a mechanism and offers a cheaper path keeps it.
Name the two defects separately, because they have different fixes. Classifying a member as adherent over 12 months requires them to stay alive and stay covered for those 12 months, and that guaranteed event-free interval is assigned to the adherent group. Separately, members who refill on schedule differ from those who do not in ways claims never record.
Show rather than assert. Rerun the same comparison as a landmark analysis: classify adherence over days 1 to 90, start follow-up at day 91 for everyone still enrolled and event-free, and report how much of the 31 percent survives.
Separate the association claim from the intervention claim out loud. Nothing in the analysis speaks to whether a reminder changes refill behaviour, which is the actual product question and is separately testable.
Offer the shippable path: a randomised holdout on a subset with proportion of days covered as the primary endpoint, powered on the plausible effect of a reminder rather than on the 31 percent, with a stated read date.

Follow-up

The landmark rerun still shows a 20 percent difference. Do you ship?
The product manager says a holdout delays launch. How do you cost that argument?
What primary endpoint do you choose, and why not admissions?

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

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.
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.
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.
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.
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.
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?