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Hinge health Data Scientist Interview Questions & Guide 2026

Hinge health Data Scientist interview guide: reported rounds, practice questions with worked solutions and a 7-day prep plan.

The 60-second brief

A Data Scientist at Hinge Health plays a pivotal role in transforming how musculoskeletal (MSK) care is delivered. By leveraging vast amounts of clinical, behavioral, and sensor-based data, you will directly influence the personalized care pathways of millions of users. The data science team is responsible for driving clinical efficacy, optimizing patient engagement, and proving the financial and physical value of digital physical therapy to enterprise clients and health plans.

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

Risk-adjust outcomes before comparing providers or panels
Separate censoring from events in time-to-event models
Correct for claims runout before reporting recent months

Overview

In this role, you will work at the intersection of product analytics, machine learning, and clinical research. Your insights will help refine the algorithms that analyze data from wearable motion sensors, customize computerized physical therapy sessions, and predict patient churn before it happens. Because Hinge Health operates in a highly regulated digital health space, your models and analyses must meet rigorous standards of accuracy, safety, and scientific validity.

This is a high-impact position where your work does not just optimize click-through rates; it directly improves human mobility and reduces chronic pain. You will collaborate closely with product managers, software engineers, physical therapists, and clinical designers to build a seamless, data-driven healthcare experience.

The Hinge health interview process

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

Initial Conversation

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.

Technical Screen

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Onsite Loop

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.

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.

Ignoring interference between units in a marketplace experiment

Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.

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

If you are given a dataset with highly sparse and noisy wearable sensor data, how would you approach feature engineering to predict user pain reduction?

Medium · machine learning and modelling

Approach

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

Follow-up

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

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?

Expand overlapping coverage spans into fractional member-months

Medium · interval expansion · denominators · vectorisation

member_enrollment has span_id, member_id, plan_id, product_type, effective_date, termination_date (NULL while active). Both dates are inclusive. Write a function returning one row per calendar month of a stated year with total member-months, where a member contributes covered days in the month divided by days in that month, capped at one month per member even when two spans overlap after a plan change or a retroactive span. Do not expand to one row per member-day: the sample has 400,000 members. Output: month, member_months.

Approach

Resolve NULL termination_date to the reporting end date and say so in a comment. An open span is not an infinite span, and clipping it at the year end is what keeps the denominator finite and auditable.
Merge overlapping and adjacent spans per member before touching months. Sort by member_id and effective_date, carry a running maximum of the end date, and open a new merged group when effective_date exceeds that running max plus one day. Adjacent means a gap of zero days, which a plan change produces constantly.
Cross join the merged spans to the 12 month boundaries rather than to days. Overlap days = (min(span_end, month_end) - max(span_start, month_start)).days + 1, clipped below at 0. That is at most 12 rows per span instead of 365.
Divide overlap days by the number of days in that month, so February and July are weighted correctly, then sum by month.
Validate on a constructed member before trusting the aggregate: a single span covering the whole year must sum to exactly 12.0.

Worked solution (30 min)

Fill termination_date nulls with the reporting end date and clip all spans to the reporting year.
Sort by member_id, effective_date, then compute a running max end per member with cummax shifted by one, flag a new group where effective_date > prior_max_end + 1 day, and cumsum the flag to get merged group ids.
Aggregate each group to min start and max end, producing disjoint spans per member.
Cross join merged spans to a 12-row month frame, compute clipped overlap days, divide by days in month.
Group by month and sum.

Expected result: Twelve rows. For a member with spans 2026-01-01 to 2026-03-15 and 2026-03-01 to 2026-06-30, March contributes exactly 1.0, not 15/31 plus 31/31. For a member with spans covering January and March only, January is 1.0, February is 0.0, March is 1.0, and the member totals 2.0.

Verify it yourself

A member with one span covering the full year sums to exactly 12.0 across the twelve rows.
Total member-months is less than or equal to (distinct members) x 12, and equality holds only if every member is covered all year.
Re-running the span merge on its own output changes nothing, which proves the merged spans are disjoint.
Recompute one member by brute-force day expansion and confirm the two agree to floating point tolerance.

Follow-up

Product type changes mid-year. The metric must be reported by product_type. Where does the cap now apply and what breaks?
An eligibility file arrives with a retroactive termination that shortens a span you already reported on. How do you restate?
Why member-months rather than distinct members, in one sentence, for a director who wants the simpler number?

Given a table of sensor calibration events and a table of user sessions, write a query to find the average time elapsed between a sensor calibration and the start of a session.

Medium · sql and data manipulation

Approach

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

Follow-up

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

How would you identify the top 10% of users who have the highest drop-off rate in their first two weeks of onboarding?

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Say which table is the grain you start from, and join outward from it.
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?

Write a query to calculate the rolling 7-day average of physical therapy sessions completed by users, partitioned by their clinical cohort.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Say which table is the grain you start from, and join outward from 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?

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?

What metrics would you track to determine if a new clinical education article is successfully driving long-term user engagement?

Medium · product sense and metrics

Approach

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.
Fix the population and the time window before naming any metric.

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?

If our primary clinical outcome metric (pain reduction score) is self-reported and suffers from high non-response bias, how would you adjust your analysis to measure program efficacy?

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

What would you do if the primary metric and the guardrail moved in opposite directions?
Which segment would you cut first, and what would that rule out?

A product manager wants to roll out a feature that has a positive impact on engagement but a slightly negative impact on session completion. How do you decide whether to launch it?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Name one primary metric, then the guardrail that stops it being gamed.
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 would you design an A/B test to evaluate a new in-app nudging feature aimed at increasing weekly session compliance?

Medium · a/b testing and experimentation

Approach

State the primary metric and the minimum effect worth shipping, then size the test.
Name the guardrails that would stop a launch even on a positive primary result.
Name the randomisation unit first; it decides the variance and what the test can detect.

Follow-up

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

Set the decision rule when cost and denial guardrails conflict

Medium · guardrail conflict · denials · non-inferiority bound · audit sampling

Utilization management is asked to cut risk-adjusted allowed PMPM, and its lever is prior authorization and first-pass denial on advanced imaging and specialty drugs. The scorecard guardrail is first-pass denial rate from medical_claim_line (claim_status, denial_reason_code, claim_version, received_date) paired with the overturn rate on appealed denials. The two move in opposite directions by construction. Write the decision rule: which metric binds, at what pre-declared threshold, who adjudicates a breach, and what happens on breach. Do not resolve the conflict by weighting both into one score.

Approach

Say why a composite is the wrong instrument: the weights get chosen after someone has seen which way the quarter went, and any weighted sum lets a large enough cost win purchase an unbounded increase in wrong denials. A guardrail is a constraint, not a term in an objective.
Sharpen what the guardrail is actually protecting. A higher denial rate is not by itself a harm, since some denied claims should be denied. The harm is denials that should not have been made, so split first-pass denials by denial_reason_code into administratively correctable ones (eligibility, coding, missing documentation) and clinical-necessity ones, and put the bound only on the clinical group.
Fix the measurement of the guardrail before setting its threshold: count the denominator at claim_version 1 so replacements (frequency_code 7) and voids (8) are not double counted, hold the month back for 60 days of adjudication runout, and report by received_date.
Get the direction of the appeal-selection bias right. Appeals are filed on the denials the appellant already believes are wrong, by the members and providers with the staffing, literacy and dollar threshold to file at all, and an appeal usually arrives carrying documentation the first adjudication never had. The overturn rate on appealed denials therefore runs well above the wrong-denial rate across all denials, often by a multiple, and it moves with filing behaviour as much as with adjudication quality. The unbiased population estimate is a blind re-review of a random sample of all denied clinical-necessity lines each quarter, judged on the record as it stood at first adjudication; the appeal series is a fast indicator read against its own history between audits, not a second estimate of the same quantity.
Write the rule as a bound with a consequence: risk-adjusted allowed PMPM is the primary and binds only while the audited wrong-denial rate stays within a pre-declared absolute margin of its trailing four-quarter mean. Breach stops the program's expansion rather than discounting its score, and the margin is set before the quarter opens.
Name the adjudicator and the cadence: a clinical owner outside the utilization function, reviewing at the stated paid-through date, with the breach consequence written down before any result is known.

Worked solution (25 min)

Restate the guardrail metric properly: numerator is claim lines denied at first adjudication, denominator is lines received in the month counted at claim_version 1, held 60 days, broken out by denial_reason_code group.
Split denial_reason_code into administratively correctable and clinical-necessity groups and say which function owns each, since the two have different fixes and different harms.
Write the appeal-selection argument in two sentences, including which way it runs, and specify the random blind re-review: the frame it is drawn from, namely all denied clinical-necessity lines rather than appealed ones, plus sample size, who reviews, what record they are shown, blinding, and quarterly cadence.
State the bound as an absolute margin on the audited wrong-denial rate against its trailing four-quarter mean, with the margin fixed before the period.
Write the breach consequence and the named adjudicator, and note that both are recorded before results exist.

Expected result: A written rule in which risk-adjusted allowed PMPM is primary; the binding guardrail is the audited wrong-denial rate on clinical-necessity denials, estimated from a blind random re-review of all denied clinical-necessity lines, with the appeal overturn rate kept as a between-audit indicator that runs above the audited rate and never sets the bound; the bound is a pre-declared absolute margin against a trailing four-quarter mean; a clinical owner outside utilization management adjudicates; and a breach halts expansion rather than adjusting a score.

Verify it yourself

Recount the denominator with and without version dedup. If the two differ by more than a percent or two, the incumbent number has been counting resubmissions.
Re-run a closed month at two extract dates 60 days apart. A moving denial rate means the runout hold is too short.
Compare the audited wrong-denial rate to the appeal overturn rate for the same quarter. The overturn rate should be the higher of the two, usually by a multiple, because appeals are filed on the denials most likely to be wrong and often add documentation the first adjudication never saw. An audit rate at or above the overturn rate is the anomaly: check that the sample was drawn from all denied clinical-necessity lines rather than only appealed ones, and that the reviewers were blinded and shown the record as it stood at first adjudication.
Confirm the margin, the adjudicator and the consequence are all recorded with a timestamp before the period's data was available.

Follow-up

Denials rose and the overturn rate fell. Give the benign reading and the bad reading, and the one number that separates them.
What happens to your bound if the appeal filing rate per denial drops 30 percent in the same quarter?
How large a random audit sample do you need to detect a 2 percentage point rise in wrong denials off a 6 percent base, and what does that cost in clinician review hours?

First-pass denial rate doubled in one received week

Medium · pipeline failure · segment cuts · denominator definition

First-pass denial rate jumped from 6.1 to 11.8 percent in a single received_date week and stayed at the new level. Nothing in the adjudication rules changed that week. You have medical_claim_line: claim_id, claim_version, frequency_code, claim_status, denial_reason_code, member_id, received_date, adjudicated_at, billing_provider_npi. The metric counts lines whose first adjudication returns 'denied', over lines received in the window at claim_version = 1. Find the cause, say whether denial behaviour actually changed, and deliver the corrected series plus the query change that fixes it.

Approach

Cut the excess by denial_reason_code before anything else. A behaviour change spreads across codes; an upstream failure concentrates in one. If a single eligibility-related code carries nearly all of the 5.7 point excess, the claims are fine and the eligibility data was not.
Join the denied lines to member_enrollment and compare the span's created_at to the claim's received_date. Lines denied for members whose coverage span loaded after the claim arrived are a load-ordering failure, not a coding failure.
Check resolution downstream: take those claim_ids and look for a later version with frequency_code 7 that adjudicates to 'paid'. A high resubmit-and-pay share confirms the denial was transient and recoverable.
Verify the denominator is not the mover. Count lines at claim_version = 1 by received_date and check the weekday mix, because submission batching makes received_date strongly day-of-week patterned and a short or holiday week shifts the denominator without changing behaviour.
Confirm runout: the metric should be held until 60 days of adjudication have passed, so check that the comparison weeks are equally mature rather than comparing a settled week to a fresh one.
Restate the series with the affected reason code broken out as its own line, and change the query to carry denial_reason_code into the reported breakdown rather than only the headline rate.

Follow-up

The affected lines all get paid on resubmission. Is the first-pass denial rate then wrong, or right and uninteresting?
How would you monitor for this class of failure without waiting for someone to notice a chart?
What changes if the excess is spread evenly across denial_reason_code instead of concentrated?

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.

Stories where someone disagreed with you

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

How do you handle ambiguity when tasked with analyzing a problem space that has poorly defined data pipelines and inconsistent schema definitions?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.
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?

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?

Sequence three urgent requests with one analyst-week available

Medium · prioritisation · tradeoffs · stakeholder communication

Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.

Approach

The probe is whether you prioritise on consequence and reversibility rather than on who asked loudest or most recently.
Classify each request by what happens if it slips. A regulatory or contractual deadline is irreversible on its date, a drifting model is causing harm every day it keeps running, and a portfolio review can absorb a provisional number. That ordering is defensible to all three requesters because it does not depend on your preferences.
Take the deadline-bound work first, but scope it to the minimum defensible output, because completion factors feeding a filing carry a different error tolerance than a slide.
Do not let the drifting model simply wait. Quantify the harm cheaply by comparing calibration in the affected region against the rest, and if it is materially miscalibrated propose flagging or suppressing its output for that region within the hour rather than at the end of a refresh.
Give each deprioritised requester something real: a provisional forecast with its uncertainty and a refresh date, or a diagnostic that tells them whether their problem is urgent. Say no explicitly with a date rather than going quiet, because silence is what produces escalation.

Follow-up

The programme owner escalates to your manager. What do you want your manager to be able to say?
Midweek the actuarial work needs two more full days than you estimated. What gives?
How does your answer change if the drifting model drives a clinical outreach list rather than a report?