Interview guides · Grow Therapy

Grow Therapy Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

A Data Scientist at Grow Therapy plays a pivotal role in bridging the gap between clinical operations, product development, and business growth. As a technology-enabled mental health startup, Grow Therapy relies heavily on data to match clients with the right therapists, streamline billing and insurance processing, and optimize therapist enablement. The data team is responsible for transforming raw operational data into strategic insights that directly influence how mental health care is accessed and delivered.

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
Build member-month denominators from overlapping coverage spans
Reconstruct drug exposure intervals from dispensing records

Overview

In this role, you will not just be building models; you will be answering critical business questions that shape the product roadmap. Whether you are analyzing user behavior to optimize search and matching algorithms, evaluating the performance of marketing and SEO campaigns, or building robust data pipelines, your work will have a direct impact on both providers and patients. The ideal candidate thrives in a high-growth, fast-paced environment and enjoys wearing multiple hats across analytics, data engineering, and product strategy.

While the title is Data Scientist, the day-to-day focus at heavily emphasizes product analytics, data modeling, and business intelligence. You will collaborate closely with product managers, engineers, and business operations leaders to ensure that data-driven decisions are backed by rigorous methodology. If you are passionate about healthcare accessibility and love solving complex, ambiguous problems with clean data, this role offers an exceptionally high-leverage opportunity to make a difference.

The Grow Therapy interview process

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

Recruiter Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Take-Home Assessment

The clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.

What to demonstrate

Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived

How to prepare

Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight

Technical Review and Panel Interview

A loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.

What to demonstrate

Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience

How to prepare

Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story

Where candidates lose points

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

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

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.

Dropping rows with missing values without naming the mechanism

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

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

Questions & practice

What metrics would you track to evaluate the health and efficiency of our therapist-patient matching algorithm?

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Pick an evaluation metric that matches the cost of each error type, not a default.
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?

Build a claims lag triangle and complete recent months

Medium · chain ladder · runout · pivot

medical_claim_line has claim_id, claim_version, frequency_code, service_start_date, paid_date, allowed_amount, claim_status. Using 24 fully mature incurred months, build a development triangle of allowed dollars by incurred month and payment lag in whole months, derive cumulative completion factors by chain ladder, then estimate ultimate allowed for the three most recent incurred months at a stated paid-through date. Deduplicate to surviving claim versions before building the triangle. Return incurred_month, paid_to_date, completion_factor, estimated_ultimate.

Approach

Deduplicate to surviving claim versions and keep paid lines first. A triangle built on all versions develops on adjustment churn rather than on payment timing, and replacements arrive late, so the distortion concentrates in exactly the tail you are trying to estimate.
Compute lag as a whole-month difference between incurred month and paid month, not as day difference divided by 30. Ragged month lengths otherwise shuffle identical claims between lag buckets depending on which month they fall in.
Pivot to incurred_month by lag, cumulate along lag, then form age-to-age factors as the ratio of the column L+1 total to the column L total, summed over incurred months mature enough to have both. Volume weighting is the chain ladder; a simple mean of per-month ratios lets one low-volume month dominate the factor.
Chain the age-to-age factors from each lag to ultimate and invert: completion factor at lag L is the reciprocal of the product of factors from L onward. Apply as estimated_ultimate = paid_to_date / completion_factor.
State the assumption you have just made, which is that the development pattern is stable. It is not, after a claims-system migration, a network change or a processing backlog, and the most recent month's factor is the least reliable because it rests on the fewest observations while carrying the largest adjustment.

Worked solution (35 min)

Deduplicate to surviving versions, drop voids, keep claim_status 'paid', and derive incurred_month from service_start_date and paid_month from paid_date.
lag = (paid_month.year - incurred_month.year) x 12 + (paid_month.month - incurred_month.month); drop negative lags and investigate them separately.
Pivot to a triangle of summed allowed_amount, cumulate along the lag axis, and mask cells past the paid-through date so no cell contains a future payment.
Compute volume-weighted age-to-age factors per lag over the mature rows, then chain them into cumulative completion factors.
Apply the factor for each recent month's current maturity to its paid_to_date and return the four columns.

Expected result: Three rows. Completion is well below 1 at short lag and approaches 1 in the tail; for a mixed medical book a one-month-old incurred month commonly sits far from complete, while a twelve-month-old one is close. Exact values are book-specific, so the reviewable property is monotonicity and the holdout test below, not the numbers themselves.

Verify it yourself

Cumulative completion factors are non-decreasing in lag and converge toward 1 at the tail.
Hold out one fully mature incurred month, apply the factors at each of its intermediate maturities, and confirm each estimate lands within a few percent of its known total.
estimated_ultimate is greater than or equal to paid_to_date for every row.
A pharmacy-only triangle completes far faster than an inpatient-facility-only triangle, which confirms the lag computation is reading real payment timing.

Follow-up

A processing backlog means the last two months developed slower than history. What does your estimate do, and how would you detect that before reporting?
Pharmacy, professional and inpatient facility develop on different clocks. How do you split the triangle, and what breaks if the service mix shifts?
What happens to a PMPM series if someone reports an unadjusted recent month, and in which direction does the error point?

Collapse claim versions before summing allowed amounts by member

Easy · claim versioning · groupby · deduplication

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

Approach

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

Follow-up

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

Explain how you would optimize a slow-running SQL query that involves multiple large joins and aggregations.

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.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

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

Given a table of client searches and therapist bookings, write a query to find the conversion rate for each therapist specialty.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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 would you verify this result without re-running the same query?
What breaks if events arrive late or out of order?

How would you handle duplicate records in a transaction table when calculating total monthly revenue?

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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?

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?

If therapist engagement drops by 15% week-over-week, what structured steps would you take to diagnose the root cause?

Medium · product sense and metrics

Approach

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.
Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

How would you detect that the metric is being gamed rather than genuinely improving?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you validate that your impact metrics are accurate and not just capturing seasonal trends or external noise?

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.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

If we launch a new SEO-based product feature to drive therapist sign-ups, how would you design a framework to measure its success?

Medium · product sense and metrics

Approach

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.
State what result would change your recommendation, so the answer is falsifiable.

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 structure an executive summary for an analytical deep-dive to ensure business leaders can quickly grasp the key takeaways?

Medium

Approach

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

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?

Pooled persistency fell but no one lost coverage

Medium · interval sql · mix shift · cohort seasonality

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

Approach

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

Follow-up

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

Suggested practice checklist

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.

What visualization techniques would you use to present complex retention cohort data to non-technical stakeholders?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
State the situation in two sentences and spend the rest on your reasoning.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

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

Scope a one-line request for a readmission rate

Easy · scoping · measure specification · stakeholder communication

A clinical operations lead messages you: 'What is our readmission rate? The committee meets Friday.' You have encounter (encounter_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, index_encounter_id) and member_enrollment coverage spans. Do not open a query editor yet. Deliverable: the three to five questions you send back, ranked by how much the answer moves the number; the default specification you will build if nobody replies by Wednesday; and the one line you will place under the figure so the committee does not read it against a published benchmark.

Approach

Read the probe: this tests whether you convert ambiguity into a specification without either stalling or guessing silently. Both failure modes are common, and the silent guess is worse because nobody can see it.
Rank your questions by leverage on the number rather than by curiosity. Whether the numerator is all-cause or unplanned, and whether the clock starts at discharge_ts, move the result far more than a tie-break rule on same-day returns.
Ask about the comparison first. A number going to a committee will be compared to something, and whether that something is last quarter, another panel, or a national figure decides whether you owe them a raw rate, a risk-adjusted rate, or an observed-over-expected ratio.
Commit to a default in writing so silence does not block you: unplanned acute inpatient readmission within 30 days of discharge_ts, index stays excluding planned admissions, acute-to-acute transfers, discharges against medical advice, and dispositions of expired or hospice, denominator of eligible index discharges, paid-through date stated.
Write the caveat as a restriction on use rather than a hedge: name the one comparison the figure supports and the one it does not.

Follow-up

They reply that they want it 'the way the benchmark does it'. What do you ask next?
The committee wants it split by attending provider. What changes in the specification, and what do you refuse to show?
How does your answer change if the meeting is tomorrow rather than Friday?

Make an honest case for your own analytical impact

Hard · impact attribution · counterfactual · self-assessment

You are asked to account for a year of your work. Constraint: you may only use measures that existed before you knew the outcome, and you must include one project that moved nothing. The portfolio contains a measure specification that changed how a contract settles, a predictive model that shipped, a self-serve dashboard, and an evaluation that killed a proposed programme. Deliverable: how you attribute value to each, which of them you claim credit for and which you do not, and the one number you would accept being judged on next year.

Approach

The probe is whether you can reason counterfactually about your own work, which is the same discipline this domain demands of you for programmes, applied where it is uncomfortable.
State the counterfactual for each item: what would have happened without you. A measure specification that changed how a contract settles has a dollar counterfactual you can compute. A dashboard's counterfactual is analyst hours, which is real and small, and attaching a revenue number to it is where these accounts usually turn into fiction.
Treat the killed programme as your strongest item and say why. Avoided spend is real value, it is attributable because the decision turned on your evaluation, and it is the one item nobody else will count on your behalf.
Report the shipped model the way you would report anyone's model: calibration in the subgroups that matter, adoption by the people it was built for, and whether any decision changed. A model that is live but ignored has zero impact, and saying so is the credibility test in this answer.
Name the failed project with its mechanism and its cost, then say what earlier signal you would watch to kill it sooner. Time to kill is a legitimate impact measure.
Choose next year's number so that it can go down. A metric that only ratchets upward is not a commitment.

Follow-up

Two other people also claim the contract measure. How do you split it?
Your model is live and used, but the decisions it informs would have been the same without it. Is that impact?
What is the strongest argument that your year had less impact than you just described?