Interview guides · Blue Cross Blue Shield of Michigan

Blue Cross Blue Shield of Michigan Data Scientist Interview Questions & Guide 2026

Data-quality drills for Blue Cross Blue Shield of Michigan data scientist prep: survivorship and selection bias, leakage through time-ordered features, and extreme values where provenance decides the call.

The 60-second brief

The role of a Data Scientist at Blue Cross Blue Shield of Michigan (BCBSM) is pivotal in shaping the healthcare landscape through data-driven insights and innovative solutions. As a Data Scientist, you will leverage advanced analytics, machine learning, and statistical methods to tackle complex healthcare challenges. Your work directly impacts product development, user experience, and business strategy, contributing to the overall mission of improving health outcomes for members.

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

Focus areas

Minimise protected health information in every extract
Reconstruct drug exposure intervals from dispensing records
Separate censoring from events in time-to-event models

Overview

In this position, you will engage with a variety of teams, including clinical, operations, and product development, to analyze vast amounts of data and extract actionable insights. You will be involved in projects that enhance patient care, streamline operations, and support strategic decisions, making your contributions both critical and rewarding. The complexity and scale of the data you will work with provide a stimulating environment where your analytical skills can shine.

This role is not only about crunching numbers; it’s about translating data into meaningful narratives that drive change and improve the healthcare experience. You can expect to face diverse challenges that require creative problem-solving and collaboration, making this an exciting opportunity for those passionate about data and its impact on health.

The Blue Cross Blue Shield of Michigan interview process

Blue Cross Blue Shield of Michigan candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screening

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
Whether you name what you have not done instead of stretching to cover every line of the posting
Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

Technical Interviews

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

Behavioral Interviews

This round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.

What to demonstrate

Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
The counterfactual: what would have been done without your analysis, and why that default was worse
How far your involvement ran past the handoff, and whether you checked that the change did what you predicted

How to prepare

Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.

Final Interview

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

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.

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 a dozen metrics with no multiplicity control

Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.

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.

Questions & practice

Design a model to predict patient outcomes based on historical data.

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
Say how the offline result would be validated online before it is trusted.
Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

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

Describe a situation where you had to implement a complex algorithm.

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

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?

Write a function in Python to calculate the mean of a list of numbers.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

How would you optimize a slow-running SQL query?

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.
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?
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?

How do you prioritize tasks when working on multiple projects?

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

Follow-up

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

Given a dataset, how would you identify trends and patterns?

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

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?

Explain how you would test the effectiveness of a new health program.

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

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 would you approach a project aimed at reducing hospital readmission rates?

Medium · product sense and metrics

Approach

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

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?

Explain the difference between supervised and unsupervised learning.

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?

Design a switchback when a shared review queue breaks SUTVA

Medium · interference · switchback · carryover · clustered standard errors

A prior-authorisation auto-approval rule for a set of imaging procedure codes is meant to cut turnaround time. Requests sit in one shared queue worked by a fixed nurse pool across two review sites, so auto-approving one request frees reviewer capacity and speeds up the requests that were not auto-approved. Outcomes are median hours from received_date to adjudicated_at and the first-pass denial rate. Request-level randomisation is therefore invalid. Design the test: randomisation unit, block length, effective sample size, and the analysis.

Approach

Name the violation precisely. The treatment assigned to one request changes the outcome of other requests through a shared capacity constraint, so the stable unit treatment value assumption fails and a request-level difference in means estimates neither the direct effect nor the total effect.
Randomise time rather than requests: assign the policy on or off for whole (site, time block) cells. Every request inside a block experiences one consistent queue regime, so the block-level contrast is a genuine system-level effect including the capacity spillover you care about.
Choose block length from the carryover horizon, not from convenience. Estimate how long the queue takes to return to steady state after a load shock from the historical backlog series, then set blocks well longer than that and discard a burn-in equal to the drain time at the start of each block.
Analyse at the block level. The unit of analysis is the block mean, the effective sample size is the number of analysable blocks and not the tens of thousands of requests inside them, and standard errors must be cluster-robust or HAC because adjacent blocks share staffing and seasonality.
Balance the calendar deliberately: use whole weeks, or block on day of week, so the policy is not disproportionately on during Mondays. If the shared resource can instead be partitioned (separate nurse pods with separate queues), cluster randomisation on pods is simpler, but with two sites you have two clusters, which is not a design.

Worked solution (35 min)

Estimate the queue drain time from historical backlog after a volume spike, and set block length to several multiples of it (for example weekly blocks if the queue settles within a day).
Randomise policy on or off independently per (site, week), balanced so each site gets roughly half treated weeks.
Drop the burn-in window at the start of each block, then compute one block-level summary per (site, week) for each outcome.
Estimate the effect as a block-level regression of the summary on the treatment indicator with site and calendar-period fixed effects, using cluster-robust or HAC standard errors.
Power the study from the historical block-to-block standard deviation of the outcome, not from request-level variance.

Expected result: A switchback randomised at (site, week) with a discarded burn-in, analysed on block means with site and period fixed effects and HAC or cluster-robust standard errors. With two sites over 26 weeks the effective sample is about 52 block observations, fewer after burn-in, and that is the number the MDE must be computed from.

Verify it yourself

Lag-1 autocorrelation of block means after burn-in should be near zero; if it is not, the blocks are too short for the carryover.
Treated and control blocks should be balanced across day-of-week and month.
Run the randomisation labels on historical data as an A/A and confirm a null effect with correct interval coverage.
The block-level standard deviation should be materially larger than request-level SD over sqrt(requests per block), which is the tell that clustering matters.

Follow-up

Suppose you had randomised at the request level and 30 percent of the effect leaked to the control requests. What does that do to the required sample size?
Turnaround time is right-skewed with a long tail. Would you analyse block medians, block means, or something else?
How would you detect that your chosen block length is too short for the carryover you assumed?

Glycemic control share jumped when a feed connected

Medium · informative missingness · unit mismatch · metric specification

The share of members with a controlled glycemic result moved from 0.62 to 0.81 the month a second result feed was connected. lab_result carries result_id, order_id, patient_id, encounter_id, loinc_code, analyte_name, value_numeric, units, reference_low, reference_high, abnormal_flag, result_status and supersedes_result_id. The metric is members whose latest result in the year is below the control threshold, divided by members with any result in the year. Decide whether control actually improved, and specify the metric so results from both feeds can be pooled.

Approach

Split numerator and denominator. The denominator is members with any result, so a feed that tests more people changes the metric without changing anyone's health. Count denominator members before and after and see how much of the 19 point move is denominator growth.
Recompute on a fixed cohort: members who had a qualifying result in both the before and after periods. If the share is flat on the fixed cohort, the movement is who got tested, not what their results were.
Deduplicate to the latest non-cancelled row per (order_id, loinc_code), respecting supersedes_result_id, because a corrected result arrives as a new row and counting both gives one person two values.
Audit units and loinc_code mix by feed. Two unit conventions coexist for this analyte, percent and mmol/mol, related by mmol/mol equal to 10.929 times (percent minus 2.15), so 7.0 percent is 53 mmol/mol. Comparing mmol/mol values to a percent threshold would classify them all as uncontrolled and depress the share, so a rise cannot be explained by that mismatch, which rules it out rather than in.
State plainly that the tested population is not the covered population. Testing is ordered when someone suspects a problem or when a site runs standing orders, so the mean among tested members is not the population mean and cannot be made into one by imputation.
Respecify: numerator and denominator both restricted to members with a stated continuous-enrolment requirement, one deduplication rule, one unit after conversion, one threshold, and a separately reported testing rate so the denominator's movement is visible.

Follow-up

Someone proposes imputing the mean result for untested members. What happens to the estimate and why?
A model you are building gains AUC from a feature that marks whether the test exists. Why is that a warning rather than a win?
How would you report this metric so a clinical committee can see improvement separately from coverage of testing?

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.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

How do you handle feedback and criticism?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

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?

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?