Ford Hospital and Research center Data Scientist Interview Questions & Guide 2026

The 60-second brief

The Data Scientist role at Ford Hospital and Research center is a pivotal position that bridges the gap between complex healthcare data and actionable clinical or operational insights. You will be tasked with transforming raw, high-dimensional datasets into strategic solutions that drive improvements in patient outcomes, hospital efficiency, and research accuracy. This role is not merely about model building; it is about understanding the clinical context and applying advanced analytics to solve real-world problems within a high-stakes medical environment.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

• Risk-adjust outcomes before comparing providers or panels
• Reconstruct drug exposure intervals from dispensing records
• Build member-month denominators from overlapping coverage spans

Overview

You will work at the intersection of product-sense and technical rigor, collaborating with multidisciplinary teams—including healthcare administrators, researchers, and IT engineers—to deploy scalable data products. Whether you are optimizing resource allocation, conducting rigorous experimentation to validate clinical interventions, or designing metrics to track hospital performance, your work directly influences the quality of care provided. Candidates can expect an environment that values precision, reliability, and a deep-seated commitment to the mission of the institution.

The Ford Hospital and Research center interview process

Ford Hospital and Research center candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

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

What to demonstrate

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

How to prepare

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

Technical Assessments

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What to demonstrate

• Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
• Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
• Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

• Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
• Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
• If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows

Behavioral Evaluations

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.

Presentation/Case Study

This round runs as a working session, so part of what it decides is whether you are useful to think with. The interviewer will interrupt: a hint that the data you assumed does not exist, a challenge to your metric, a nudge toward a branch you skipped. Treating those as interference is the common failure. Reason out loud while your thinking is still provisional so there is something to react to, and when a redirect arrives, take it instead of defending the path you had already started down.

What to demonstrate

• Whether your reasoning is audible while it is still unsettled, or only after you have privately decided
• What you do with a hint: absorb it and adjust, or argue for the original route
• Whether your clarifying questions have answers that would change your approach, as opposed to filling silence
• Whether you can be wrong about something in the middle of the case and keep moving without restarting

How to prepare

• Run practice cases with a partner instructed to interrupt twice: once to remove a data source you assumed existed, once to reject the metric you chose. Practise absorbing both without going back to the start.
• Before each practice case, write down the clarifying questions you plan to ask, then check afterwards whether any answer actually changed what you did. Drop the ones that did not.
• Explain an analysis you already know well to someone outside the field and have them stop you at every point where the reasoning jumped a step.

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.

Ignoring clustering by provider, facility, or site

Patients within a provider panel, and subjects within a trial site, are correlated on both case mix and practice pattern. Standard errors computed as if observations were independent are too narrow by roughly the design effect, 1 + (m - 1) * ICC, where m is the average cluster size. With 50 patients per provider and an intracluster correlation of only 0.02, variance is understated by about a factor of two, which manufactures significant provider differences out of noise. Cluster-robust errors or a random intercept per provider, and cluster-level randomisation when you design the test, are the corrections.

Answering a product-sense question with a list of features

Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

Questions & practice

How do you balance the trade-off between model accuracy and clinical interpretability when designing a new diagnostic tool?

Medium · machine learning and modelling

Approach

1. Check what information would not exist at prediction time, and exclude it.
2. Say how the offline result would be validated online before it is trusted.
3. Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

Build a claims lag triangle and complete recent months

Medium · chain ladder · runout · pivot

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

Approach

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

Follow-up

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

Simulate how ignoring clustering inflates the false positive rate

Medium · simulation · design effect · type i error

Simulate a provider-level comparison with no true effect. 40 providers, 60 members each, a continuous outcome with total variance 1 and intraclass correlation 0.02. Randomise providers 20 to 20, not members. Over 5,000 replications, report the share of replications where a two-sample t-test run on all 2,400 individual observations, ignoring provider, returns p below 0.05. Report the same share when the unit of analysis is the 40 provider means. Then state how the first number follows from the design effect 1 + (m - 1) x ICC.

Approach

1. Decompose the variance explicitly rather than tuning it: provider variance = ICC = 0.02, residual variance = 1 - ICC = 0.98. Draw u_j once per provider and e_ij per member, so the outcome is u_j + e_ij and the correlation between two members of one provider is 0.02 by construction.
2. Randomise at the provider level. That is the entire mechanism: the treatment indicator is constant within a provider, so in any single replicate the provider intercepts are confounded with the arm, and the naive test reads that confounding as signal.
3. Vectorise over replications with a (reps, providers, members) array. 5,000 x 2,400 normals is trivial in numpy, and a Python loop is what tempts people to cut replications to 500 and report a number with Monte Carlo noise of plus or minus 0.017.
4. Predict the answer before running it: design effect = 1 + (60 - 1) x 0.02 = 2.18, naive standard errors are too small by sqrt(2.18) = 1.476, so the rejection rate at nominal 0.05 is about 2 x (1 - Phi(1.96 / 1.476)) = 0.18. Agreement between prediction and simulation is what makes the result a demonstration rather than an anecdote.
5. Run the provider-mean analysis as the control. Recovering 0.05 there proves the generator is correct and isolates the failure to the analysis unit.

Worked solution (30 min)

1. Draw u with shape (reps, 40) from N(0, sqrt(0.02)) and e with shape (reps, 40, 60) from N(0, sqrt(0.98)); y = u[:, :, None] + e.
2. Assign providers 0-19 to one arm and 20-39 to the other; because there is no true effect, a fixed split is valid and removes one source of Monte Carlo noise.
3. Naive test: flatten each arm to 1,200 observations and compute a two-sample t statistic per replication with vectorised means and pooled variance.
4. Cluster test: average within provider to 40 values, then run the same two-sample t on 20 versus 20.
5. Report both rejection shares and compare the naive one against 2 x (1 - Phi(1.96 / sqrt(2.18))).

Expected result: The naive individual-level test rejects in roughly 18 percent of replications, against a nominal 5 percent. Monte Carlo standard error at 5,000 replications is about 0.005, so anything in 0.17 to 0.19 is consistent. The provider-mean analysis rejects in about 5 percent, within roughly 0.003.

Verify it yourself

• Set ICC to 0 and both analyses return about 0.05. If the naive one does not, the data generator is wrong, not the test.
• Raise members per provider from 60 to 120 with ICC fixed and the naive rate rises, because the design effect grows linearly in cluster size.
• The provider-mean analysis stays near 0.05 at every ICC you try, which is the definition of a valid test.
• The simulated naive rate lands within about 0.01 of 2 x (1 - Phi(1.96 / sqrt(design effect))).

Follow-up

• How many providers would you need for 80 percent power on a 0.2 SD difference with 60 members each and this ICC?
• Cluster sizes are unequal in reality. The design effect approximation becomes 1 + ((1 + CV^2) x mbar - 1) x ICC. Which direction does that move your sample size and why?
• The intervention cannot be withheld from any provider. Sketch a design that still yields a defensible estimate.

Explain how you would use SQL window functions to calculate a moving average of patient wait times over the last 30 days.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

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?

Why do you want to join Ford Hospital and Research center specifically?

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Write a query to identify the top 5 clinics by patient volume using a window function for ranking.

Medium · sql and data manipulation

Approach

1. Compute rates by summing numerator and denominator separately, never by averaging rates.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. 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?

Reconstruct drug coverage intervals and compute proportion of days covered

Hard · gaps and islands · window functions · adherence

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

Approach

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

Worked solution (40 min)

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

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

Verify it yourself

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

Follow-up

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

How would you design a metric to measure the success of a new patient triage system?

Medium · product sense and metrics

Approach

1. Restate the decision this analysis has to support, and who acts on the answer.
2. State what result would change your recommendation, so the answer is falsifiable.
3. Decompose the metric into the rates that drive it, and say which one you would check first.

Follow-up

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

Describe a situation where you had to define success metrics for an ambiguous data project.

Medium · product sense and metrics

Approach

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

Follow-up

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

If you notice a sudden drop in our primary patient engagement metric, how would you go about diagnosing the root cause?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Decompose the metric into the rates that drive it, and say which one you would check first.
3. Name one primary metric, then the guardrail that stops it being gamed.

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?

What are the most common experimentation pitfalls you have encountered when running tests in a real-world setting?

Medium · a/b testing and experimentation

Approach

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

Follow-up

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

If you have a small sample size for a clinical trial, how does that impact your statistical power?

Medium · a/b testing and experimentation

Approach

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

Follow-up

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

Score trial sites without rewarding enrollment that does not stick

Medium · site scorecards · shrinkage · guardrails · gaming

A clinical operations group ranks sites on subjects randomized per activated site-month, using study_subject_visit (study_id, site_id, subject_id, arm_code, randomization_ts, site_activation_date, visit_name, visit_number, visit_status, primary_endpoint_value, protocol_deviation_flag, deviation_severity). Bottom-ranked sites lose allocation on the next study. Define the primary metric, two guardrails, the handling of sites activated too recently to rank, and the estimator you rank on. Then state what a site under allocation pressure does to the number, and which guardrail sees it.

Approach

1. Move the numerator from randomized to randomized and retained through the protocol-defined primary endpoint visit. A subject who randomizes and withdraws consumes screening, drug supply and monitoring while contributing nothing to the analysis population, so counting them rewards the wrong behaviour precisely when a site is under pressure.
2. Build the denominator as exposure, not sites: site-months from site_activation_date to the earlier of period end and database lock, per study. Ranking on a raw count instead of a rate confuses a large site with a fast one.
3. Handle young sites by reporting rather than dropping. Sites with fewer than a stated minimum exposure are shown in an insufficient-exposure band, because excluding them is how a non-enrolling tail disappears from a scorecard, and the non-enrolling tail is usually the larger operational problem.
4. Rank on a shrunken estimate, not the raw ratio. With a few site-months of exposure the ratio has enormous variance, and a site with two site-months and one randomization will top an unshrunken table. Partial pooling toward the study mean, or a simple empirical-Bayes posterior mean, prevents rank being an artefact of small exposure.
5. Pick guardrails that read the two pressure responses. A site that loosens eligibility judgement shows an implausibly low screen-failure rate, computed as subjects with randomization_ts NULL and visit_status screen_failed over subjects screened. A site that pushes visits to hit counts shows up in major protocol deviations per 100 completed visits, reported separately from minor ones because a single major deviation can remove a subject from the per-protocol set.
6. Report the companion the median hides: the share of activated sites with no randomization by day 90. A median computed only over sites that eventually randomized is silent about the sites that never did.

Worked solution (30 min)

1. Define the numerator as subjects with non-null randomization_ts whose primary endpoint visit reaches visit_status completed with a non-null primary_endpoint_value, per study and site.
2. Compute site-months of exposure from site_activation_date to period end or database lock, and set the minimum exposure below which a site is banded rather than ranked.
3. Compute the raw rate, then the shrunken estimate by partial pooling toward the study mean, and print both columns side by side so the shrinkage is visible rather than hidden.
4. Specify the two guardrails with numerator, denominator and window, keeping major and minor deviations as separate series.
5. Add the no-randomization-by-day-90 share across all activated sites, so the non-enrolling tail appears on the same page as the ranking.

Expected result: A scorecard whose primary metric is randomized-and-retained subjects per activated site-month per study, ranked on a shrunken estimate with the raw rate shown beside it; sites below a stated minimum exposure reported in an insufficient-exposure band rather than excluded; guardrails of screen-failure rate and major protocol deviations per 100 completed visits reported separately from minor ones; and a companion share of activated sites with no randomization by day 90.

Verify it yourself

• Compare raw and shrunken rankings. The largest rank movements should be concentrated among the lowest-exposure sites; if a high-exposure site moves far, the pooling is misconfigured.
• Confirm the denominator's site-months sum matches an independent count of months between activation and period end across all sites.
• Check that screen failures are being counted at all: a site reporting a zero screen-failure rate across dozens of screened subjects is a data-capture problem or an eligibility-judgement problem, and both need an answer.
• Verify that excluding the insufficient-exposure band does not change the study-level mean materially; if it does, those sites carry more of the study than the banding implies.

Follow-up

• A site has the best randomized-and-retained rate and the worst major deviation rate. What do you recommend, and what would change your answer?
• Your shrunken ranking moves a site from 3rd to 14th. How do you explain shrinkage to the site's operations lead in two sentences?
• Primary endpoint retention takes 18 months to observe. What do you rank on in the meantime, and what bias does that interim choice introduce?

Allowed PMPM fell fourteen percent in three months

Easy · claims runout · completion factors · incurred vs paid

Allowed PMPM in the monthly series fell from $412 to $354 over the three most recent incurred months, and leadership wants to announce the saving. You have medical_claim_line (allowed_amount, service_start_date, received_date, paid_date, claim_id, claim_version, frequency_code, place_of_service_code), pharmacy_claim (allowed_amount, fill_date, reversal_flag) and member_enrollment coverage spans. The chart carries no paid-through date. Work out how much of the fall survives completion, and hand back a restated series with an explicit paid-through date and the completion factors you applied.

Approach

1. Get the paid-through date first and put it on the chart. Without it the series is uninterpretable, because every incurred month is a different age.
2. Build a lag triangle: for each incurred month, cumulative allowed_amount by the number of months between service_start_date and paid_date, on surviving claim versions only (drop frequency_code 8 voids and keep the highest claim_version per claim_id).
3. Estimate development factors separately for pharmacy fills, professional lines and inpatient facility lines. Pharmacy adjudicates within days, professional within weeks, inpatient facility over months, so one blended factor understates the correction on the newest month and overstates it on the oldest.
4. Gross each of the last three incurred months up by the reciprocal of its cumulative development factor, using factors fitted on months that have already fully run out.
5. Rebuild the denominator independently as member-months from coverage spans. Member-months are complete on day one, so if the denominator also shows a lag pattern the enrollment file is late, which is a different bug with a different fix.
6. Restate the series, mark the last three months as estimated, and report the residual movement after completion as the only part worth investigating.

Follow-up

• The completed series still shows a three percent fall. What do you look at next, and in what order?
• How would you detect that the fall is a mix shift toward cheaper services rather than lower volume?
• A contract settles on this number at a fixed paid-through date. What do you owe the other party about the estimate you just made?

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.

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.

Tell me about a time you had to explain a complex data finding to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Defend a null result against a programme sponsor

Medium · regression to the mean · difference-in-differences · stakeholder management

A care management programme enrolled the top 1 percent of members by prior-year allowed spend. The sponsor's deck shows allowed PMPM for enrollees falling 34 percent from the year before enrollment to the year after, and asks for budget to triple the programme. You rebuild the evaluation with a concurrent comparison group selected by the identical spend rule in the same period. The difference-in-differences estimate is a 3 percent reduction with a confidence interval spanning zero. Deliverable: how you present this, to whom, in what order, and what you propose next.

Approach

1. Name the probe: whether you can deliver a finding that costs somebody their programme without softening it into uselessness or creating an adversary who routes around you next time.
2. Lead with the mechanism, not the verdict. Show the comparison group's own unadjusted drop, which will be large, because a cohort selected on an extreme of the outcome regresses toward the mean whether or not anyone intervenes. The sponsor's 34 percent is mostly that, and it is a property of the selection rule rather than a criticism of their clinicians.
3. Give the sponsor the finding privately before it appears in any deck their leadership sees. Being surprised in a room is what turns a methods disagreement into a political one.
4. State the estimate with its interval and say what it rules out as well as what it fails to establish. A 3 percent point estimate whose interval crosses zero is not evidence of no effect; it is insufficient power to separate a modest effect from none, and those are different claims.
5. Arrive with a design rather than only an objection. Propose a regression discontinuity at the enrollment threshold if the rule is applied sharply, or a randomised rollout across the next wave of eligible members, and state the sample size needed to detect the effect size the sponsor believes in.

Follow-up

• The sponsor says withholding the programme from a comparison group is unethical. What do you propose instead?
• Leadership wants one number for the board next week. What do you give them?
• What result would change your mind and make you believe the 34 percent?

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

1. The probe is whether you prioritise on consequence and reversibility rather than on who asked loudest or most recently.
2. 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.
3. 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.
4. 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.
5. 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?