Interview guides · Included Health

Included Health Data Scientist Interview Questions & Guide 2026

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

The 60-second brief

As a Data Scientist at Included Health, you will occupy a highly strategic role at the intersection of healthcare, technology, and machine learning. Included Health is dedicated to raising the standard of healthcare for everyone, and data is the primary engine driving this mission. In this role, you will build and deploy models that solve complex clinical and operational challenges, such as predicting patient health risks, optimizing provider recommendation engines, and matching members to the most appropriate clinical care pathways.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

Focus areas

Risk-adjust outcomes before comparing providers or panels
Minimise protected health information in every extract
Build member-month denominators from overlapping coverage spans

Overview

The impact of your work is direct and profound. Rather than optimizing for clicks or ad revenue, your algorithms will influence real-world clinical outcomes, guide patients through a fragmented healthcare system, and lower care costs for millions of members. This means you will deal with massive, highly disparate, and often unstructured healthcare datasets, requiring a high degree of technical ingenuity, statistical rigor, and domain empathy.

To succeed, you must be more than just a model builder; you must be an exceptional communicator who can translate complex statistical concepts into actionable product strategies. The team operates in a fast-paced environment where collaboration with product managers, clinical experts, and software engineers is a daily necessity. If you are motivated by high-stakes problem-solving and want to apply cutting-edge data science to human-centric problems, this role offers an incredibly rewarding platform.

The Included Health interview process

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

Recruiter Screen

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
Whether your reason for wanting the role points at the work itself rather than the company's reputation
Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Technical Screen

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Take-Home Challenge

Before any modelling, the dataset is itself the first test. Take-home data usually carries something broken: rows duplicated at an unexpected grain, a join that silently drops part of the population, timestamps stored in more than one timezone, or missingness correlated with the outcome. An hour spent profiling row counts, key uniqueness and date ranges is not overhead, because it decides whether every number after it is real. What separates submissions is whether you report the defects you found and adapt the analysis to them, rather than modelling over them quietly and hoping the aggregate absorbs it.

What to demonstrate

Whether you established the grain of each table and checked row counts after every join, and said so in the writeup
Whether data defects you found are surfaced with their effect on the conclusion, instead of being dropped without comment
Whether filters and exclusions are reproducible from the submitted code, with the size of the excluded population quantified

How to prepare

Write a short profiling script you can point at any unfamiliar table: row count, distinct key count, null rate per column, and the min and max of every date field, then run it before anything else
Write the funnel or the join chain as one query and check the row count at each grain, so a silent fan-out shows up as a number rather than as a wrong answer later
On a past dataset, list every exclusion you applied and how many rows each one removed, then draft the single sentence about it you would put in a report

Final Round Interviews

Where a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.

What to demonstrate

Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered

How to prepare

Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one

Where candidates lose points

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

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

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.

SQL that silently fans out on a one-to-many join

State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.

Comparing periods without accounting for seasonality or day-of-week

Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.

Questions & practice

How do you measure and mitigate bias in predictive models that guide patient care?

Medium · machine learning and modelling

Approach

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.
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?
What would you monitor after launch to know the model is still valid?

Walk me through the mathematical intuition of your favorite machine learning algorithm.

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.

Follow-up

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

Four data-quality rules over corrected and cancelled lab results

Easy · data quality · record supersession · unit drift

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

Approach

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

Worked solution (25 min)

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

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

Verify it yourself

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

Follow-up

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

Given an array of integers representing patient appointment times, write a Python function to find the maximum number of overlapping appointments.

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.
Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

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

Imagine you are handed a set of disparate, poorly structured healthcare datasets. How would you clean, join, and leverage this data to identify patients at risk of hospital readmission?

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

Write a SQL query to find the active patient cohort that visited a primary care physician more than three times in the last calendar year.

Medium · sql and data manipulation

Approach

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.
Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

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

Measure ninety-day primary care contact for an enrollment cohort

Easy · cohort analysis · conversion rate · date windows

member_enrollment gives span_id, member_id, product_type, effective_date and termination_date, which is NULL while a span is active. medical_claim_line gives member_id, service_start_date, procedure_code and claim_status. The cohort is members whose earliest 2025 span has effective_date in January 2025 and who stay covered at least 90 days from it. Return, by product_type, cohort size and the share with at least one paid claim line whose procedure_code is in a supplied list of evaluation-and-management office visit codes and whose service_start_date falls in the half-open window from effective_date to effective_date plus 90 days.

Approach

Build the cohort as one row per member before touching claims. Take the earliest 2025 span per member, require its effective_date in January, and require coverage to effective_date + 90 days, which means termination_date IS NULL OR termination_date >= effective_date + 89.
Use EXISTS for the numerator rather than a join. A member with four office visits must count once; joining and counting rows inflates the numerator and can push the rate above 1.
Keep the window half-open and anchored per member. Every member has a different effective_date, so a single fixed calendar window is a different metric and will not match the cohort definition.
Aggregate numerator and denominator in one pass with a conditional sum grouped by product_type, and carry the raw counts alongside the rate so the reader can see the denominator.
State the reportability rule on the output: the January cohort is not final until 90 days plus professional-claims runout have passed, so label the paid-through date.

Worked solution (15 min)

CTE first_span: per member, the 2025 span with the minimum effective_date, filtered to January effective dates.
Add the continuous-coverage requirement to that CTE using COALESCE(termination_date, DATE '9999-12-31').
Add an EXISTS subquery on medical_claim_line correlated on member_id, restricted to the code list, claim_status = 'paid', and service_start_date >= effective_date AND < effective_date + 90.
Group by product_type: COUNT(*) as cohort_size, SUM(CASE WHEN contacted THEN 1 ELSE 0 END) as contacted, and their quotient.
Print the paid-through date used, and re-run at a 30-day window to confirm the rate falls.

Expected result: One row per product_type with cohort_size, contacted_members and contact_rate = contacted_members / cohort_size, where contacted_members never exceeds cohort_size.

Verify it yourself

contact_rate lies in [0, 1] for every product_type. Above 1 proves the numerator was counted on rows rather than members.
SUM(cohort_size) across product_types equals COUNT(DISTINCT member_id) in the cohort CTE. A larger value means a member with two January spans was counted twice.
A 0-day window returns a rate at or near zero, and a 365-day window returns a rate no lower than the 90-day one.

Follow-up

A member changes plan on day 40, closing one span and opening another. Is she still in the cohort, and which product_type does she report under?
What moves if denied lines are allowed into the numerator, and which definition is right for an access metric?
How would you show this as a cumulative curve by day since effective_date instead of one 90-day point?

If our primary member engagement metric dropped by 10% week-over-week, how would you investigate the root cause using data?

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

Explain how you would optimize a Python script that is running out of memory while processing a large claims dataset.

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
State what result would change your recommendation, so the answer is falsifiable.
Decompose the metric into the rates that drive it, and say which one you would check first.

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?

How would you design an A/B test to evaluate a new clinical intervention feature in our app when sample sizes are small?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
Name the randomisation unit first; it decides the variance and what the test can detect.
State the primary metric and the minimum effect worth shipping, then size the test.

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?

Critique a denial rate slide before it is shown

Easy · metric critique · claim versions · runout · mix shift

A weekly slide says first-pass denial rate improved from 9.1 percent to 7.4 percent month over month. It was computed from medical_claim_line as rows with claim_status 'denied' divided by all rows with a received_date in the month. You cannot query anything. List every assumption the number rests on, restate the metric so two analysts computing it independently would get the same value, and give the three questions you would ask before the slide is presented.

Approach

Work the grain first. The denominator counts all rows, so a claim resubmitted after a denial contributes its original line, its replacement (frequency_code 7) and possibly a void (8). Both numerator and denominator are inflated by exactly the claims most likely to have been denied, which makes the rate a function of resubmission behaviour rather than adjudication.
Work maturity second. A line received late in the month may still be pended at extract time, and pended lines are neither paid nor denied. If pends resolve to denials later, the most recent month is understated by construction, which looks exactly like improvement. Ask for the paid-through date and the adjudication runout hold.
Work the unit third. A denial rate on claim lines and a denial rate on claims are different numbers, and a single denied line on a twenty-line facility claim is a different operational event from a wholly denied professional claim. The spec has to pick one and say so.
Work mix fourth. Denial reasons have different owners and different volumes: eligibility denials belong to enrollment operations, coding denials to provider relations. A shift in submission mix across providers, place of service or reason code moves the blended rate with no change in any component, so the restated metric reports by denial_reason_code group.
Work precision last. A 1.7 point move is decisive on two million lines and meaningless on three thousand. The restated metric carries the denominator count, and a slide without it cannot be read.

Worked solution (15 min)

Write the assumption list as a numbered set: single version per claim, adjudication complete at extract, line grain intended, stable reason-code and provider mix, denominator large enough for the move to be real.
Restate the metric with numerator as lines denied at first adjudication, denominator as lines received in the month counted at claim_version 1, reported by received_date month, held 60 days, broken out by denial_reason_code group, and published with the denominator count.
Write the three questions in the order that would kill the slide fastest: what is the paid-through date, how many lines are in each month's denominator, and did the reason-code mix move.
State how the restated number is presented, with the most recent month marked provisional and restated at each refresh.

Expected result: A restated specification reading: numerator is claim lines whose first adjudication returns claim_status denied; denominator is lines with received_date in month M counted at claim_version 1; the month is held until 60 days of adjudication runout have passed; the rate is broken out by denial_reason_code group; and both the denominator count and the paid-through date appear on the slide, with the most recent month marked provisional. The three questions are the paid-through date, the denominator size, and whether reason-code mix shifted.

Verify it yourself

The restated denominator must be smaller than the original, since version-1 filtering can only remove rows. If it is not, the version filter is not doing anything.
Both months should be recomputable at the same paid-through age; comparing a mature month to a two-week-old month is the defect the hold is meant to prevent.
The reason-code breakout should sum back to the blended rate, which is the check that the mix decomposition is complete.

Follow-up

The rate is still down after version dedup and a 60-day hold. What is the next thing you check before anyone celebrates?
Which of these problems would also affect a paid-amount series computed the same way?
How would you present the restated number so the audience understands the prior month is provisional?

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

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.

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 highly imbalanced datasets when training a model to detect rare clinical conditions?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.
Quantify the outcome, including what you would not claim credit for.

Follow-up

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

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?

Explain an incomplete cost series to a non-technical executive

Medium · claims runout · uncertainty · executive communication

An executive opens a dashboard showing risk-adjusted allowed PMPM down 6 percent across the last three incurred months and asks whether the cost initiative is working. You know pharmacy adjudicates within days, professional claims within weeks, and inpatient facility claims over months, so those three months are both undercounted and mix-shifted toward cheap services. There is also a genuine movement of roughly 1.5 percent in the older, complete months. Deliverable: a two-minute spoken answer, the change you make to the chart, and what you commit to telling them and when.

Approach

The probe is whether you can be honest about uncertainty without sounding evasive. Executives read hedging as not knowing, so the answer has to end in a commitment.
Separate the two claims immediately. The 6 percent is an artefact of incomplete data, and there is a smaller real movement in the months that are complete. Delivering only the debunk leaves them holding nothing.
Explain the lag in their terms: the bills for the most expensive care arrive last, so an incomplete month always looks cheap and always looks like it is improving. Keep the phrase completion factor out of the first pass.
Fix the chart instead of explaining around it. Shade or omit the incomplete months, print the paid-through date on the axis, and overlay the same series as it looked at equivalent maturity a year earlier so the shape is comparable rather than merely lower.
Close with a date and a magnitude. Commit to a restated figure once runout matures, say roughly how far you expect the 6 percent to shrink, and name what you would need to see to call the initiative working.

Follow-up

They ask for your best guess today, knowing it is provisional. What do you say?
The restated number comes back at 1 percent. How do you handle having flagged the 6 percent at all?
How do you stop this dashboard producing the same conversation next quarter?