At PSI CRO, the Data Scientist (often operating under the title Principal Statistician) plays a foundational role in the integrity and success of global clinical trials. You are the architect of clinical data analysis, transforming raw, complex patient data into the statistically sound evidence required for regulatory submissions and medical breakthroughs. Your work directly dictates the success of high-stakes pharmaceutical research, making precision and adherence to strict industry guidelines your primary responsibilities.
This role requires a unique intersection of advanced mathematical rigor and a deep understanding of the regulatory landscape. You will not only be performing complex statistical modeling but also serving as a key partner for clinical teams, ensuring that every protocol is executed with the highest scientific integrity. For those who thrive in environments where technical accuracy is the ultimate currency, this role offers the opportunity to drive clinical outcomes that have a meaningful impact on global health.
Preliminary Screening
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Technical Round
reportedA 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
PracHub editorial advice for the preparation topics above.
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.
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.
Crediting a treatment for regression to the mean
Selecting a group because it is extreme (lowest-engagement users, accounts having their worst month, the bottom decile of a score) moves that group's expected next-period value back toward the average even under no treatment, by exactly as much as the selecting measure is imperfectly correlated with its own later value. Compare against units that met the same selection rule and went untreated, or use two pre-periods so the bounce-back is visible before the intervention starts. A pre-post number on a group chosen for being extreme measures the selection rule, not the treatment.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe the statistical challenges associated with multi-center clini…
Describe the statistical challenges associated with multi-center clinical trials.
Approach
- Translate the result into the decision it informs, in one plain sentence.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Sanity-check the answer against a simple bound or a simulated case.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
How do you determine the sample size for a non-inferiority trial?
How do you determine the sample size for a non-inferiority trial?
Approach
- Translate the result into the decision it informs, in one plain sentence.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Say what the estimate is of, and over what population it generalises.
Follow-up
- How would you explain this result to someone who does not know statistics?
- Which assumption here is most likely to be violated in practice?
Explain the difference between intent-to-treat and per-protocol analys…
Explain the difference between intent-to-treat and per-protocol analysis in the context of clinical data.
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- Say what the estimate is of, and over what population it generalises.
- Write down the assumption the method needs before you use the method.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
How would you approach a situation where the assumptions of your chose…
How would you approach a situation where the assumptions of your chosen statistical model are violated?
Approach
- Say how the offline result would be validated online before it is trusted.
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Four data-quality rules over corrected and cancelled lab results
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.
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?
Compute allowed PMPM by incurred month without span fan-out
Report allowed PMPM by incurred month and product_type for 2025 from medical_claim_line (member_id, claim_id, claim_version, frequency_code, service_start_date, allowed_amount, claim_status), pharmacy_claim (member_id, fill_date, allowed_amount, reversal_flag, reversed_fill_id), member_enrollment (span_id, member_id, product_type, effective_date, termination_date) and member_risk_score (member_id, score_year, prospective_risk_score), which holds at most one row per member per score year. The numerator is allowed_amount on surviving claim versions plus unreversed fills, attributed to the product_type in force on the service or fill date. The denominator is member-months. Also return a risk-adjusted PMPM for each cell: allowed PMPM divided by the member-month-weighted mean prospective_risk_score for score_year 2025 over the members making up that cell's denominator. A colleague joined claim lines to member_enrollment on member_id alone and the annual total came back 1.4 times the finance figure. Diagnose that, then produce the correct monthly series.
Approach
- Name the defect precisely. member_enrollment is one row per span, so joining on member_id alone is one-to-many and duplicates every claim line once per span that member ever held. The inflation factor is the spend-weighted mean span count per member, which is why 1.4 is plausible and why it is not a constant you can divide back out.
- Make the join functional: member_id AND service date between effective_date and COALESCE(termination_date, DATE '9999-12-31'). That yields at most one span per line unless spans genuinely overlap, in which case pick a documented precedence rule, most recently effective wins being the usual one, and prove uniqueness with a duplicate check rather than asserting it.
- Aggregate numerator and denominator separately and join them on (month, product_type). Computing both sides across one join is the second route to inflation, because the member-month denominator fans out over claim lines just as easily as the dollars fan out over spans.
- Clean each numerator source on its own terms: surviving claim versions with voids removed and claim_status 'paid' on the medical side, reversal rows and their originals removed on the pharmacy side.
- Apply completion before anyone reads a trend. State the paid-through date and either withhold the three most recent incurred months or apply completion factors, since the lag differs by service type and the incomplete tail reads as a cost improvement and a utilisation drop at once.
- Risk-adjust last, and weight the score the way the denominator is weighted. The divisor for a cell is SUM(member_months * prospective_risk_score) / SUM(member_months) over that cell's members, not a plain AVG over distinct members: a member covered two months must not count the same as one covered twelve. member_risk_score is member-level, so join it after the member-month grain is fixed, or it becomes a second fan-out.
- State the rule for members with no 2025 member_risk_score row: keep them in member_months, leave them out of the weighted mean, and publish the scored share of member-months beside the adjusted series. A thin score table moves the adjusted number while the raw one sits still, and without that share nobody can tell the two apart.
Follow-up
- A member holds two overlapping spans under different product_types on the service date. Which one gets the dollar, and how do you keep the member-month denominator consistent with that choice?
- Write the check that proves the span join is one-to-one rather than assuming it.
- Finance reports on paid_date and you reported on service_start_date. Reconcile one month between the two bases.
Find inpatient encounters with no resulted lab during the stay
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.
Follow-up
- Write the LEFT JOIN form and say exactly where the result_status predicate must sit for the two forms to agree.
- How would you separate encounters whose only labs were cancelled from encounters with no lab rows at all?
- Some encounters carry member_id NULL because the person index did not match. What does that do to a payer-side version of this measure?
What are the most effective ways to optimize SAS code for large clinic…
What are the most effective ways to optimize SAS code for large clinical datasets?
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
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
Describe your process for validating statistical programming outputs w…
Describe your process for validating statistical programming outputs within a team.
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
How do you document your programming and analysis to meet audit requir…
How do you document your programming and analysis to meet audit requirements?
Approach
- Clarify what is being asked and what a complete answer would contain.
- State your assumptions explicitly before working the problem.
- Work from the decision backwards to the evidence you would need.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Reduce variance on a skewed cost outcome with CUPED
A member-randomised digital care programme will be read on 12-month allowed PMPM, built from surviving medical_claim_line versions plus unreversed pharmacy_claim rows over member-months from member_enrollment. The outcome is heavily right-skewed. Eighty percent of members have 12 months of prior continuous coverage and therefore a usable pre-period PMPM; among those, the correlation between pre-period and post-period member PMPM is 0.45. Propose a variance reduction plan, quantify the expected gain, and say how you handle the 20 percent with no pre-period.
Approach
- Apply CUPED with the pre-period PMPM as the covariate: theta = Cov(Y, X) / Var(X), and Y_adj = Y - theta x (X - mean(X)). Because X is measured strictly before randomisation it cannot be affected by treatment, so the adjusted difference in means stays unbiased while its variance falls by the factor 1 - rho^2.
- Quantify honestly: rho = 0.45 gives 1 - 0.2025 = 0.80, a 20 percent variance reduction inside the covered stratum, which is a real but modest gain. Do not quote consumer-experimentation figures of 40 to 50 percent, which come from outcomes far more autocorrelated than health spend.
- Handle the skew before handling the covariate. Pre-specify a truncation point (a percentile of the prior year, or a fixed stop-loss amount) applied identically to both arms and fixed before unblinding, and state plainly that you are now estimating an effect on truncated cost. Report the untruncated result as a secondary so nobody can claim the cap created the finding.
- Do not impute a mean pre-period for the 20 percent with none. Stratify: run CUPED inside the covered stratum, take the unadjusted difference in the no-pre-period stratum, and combine with fixed weights; equivalently, regress with a missingness indicator and an indicator-by-covariate interaction. Both keep the estimator unbiased because the stratum is defined pre-randomisation.
- Get some of the same gain for free at assignment time by stratifying or covariate-constrained randomisation on prior-year cost decile, which removes the post-hoc analytic choices that a sceptical reviewer will otherwise interrogate.
Worked solution 30 min
- Estimate theta on the full randomised sample as Cov(Y, X) / Var(X), using both arms because X is pre-treatment.
- Form Y_adj = Y - theta x (X - mean(X)) inside the covered stratum and compute the variance ratio 1 - rho^2 = 1 - 0.45^2 = 0.80.
- Combine strata: assuming equal per-member outcome variance, the overall variance of the treatment-effect estimate falls by about 0.8 x 20 percent = 16 percent.
- Translate to MDE: sqrt(0.84) = 0.916, so the MDE falls about 8 percent; equivalently you save about 16 percent of the sample at fixed MDE.
- Validate on a historical A/A split: CUPED should return an effect indistinguishable from zero with the claimed variance reduction.
Follow-up
- rho drops to 0.20 after truncation. What do you do with the CUPED plan?
- Members with no pre-period are new enrollees. What does that do to your assumption of equal per-member variance across strata?
- Would you switch the estimand from mean PMPM to a rank-based or log-scale comparison, and what would that cost you with a finance audience?
Glycemic control share jumped when a feed connected
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?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.
How do you handle missing data in a longitudinal study, and what are t…
How do you handle missing data in a longitudinal study, and what are the implications for bias?
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Own an analysis that shipped wrong and was acted on
Describe an analysis you delivered that was wrong, where someone acted on it before the error surfaced. Pick something with a mechanism you can name, not a typo. Cover what the number claimed, what was actually true, how much time passed before it was caught, who caught it, and what you told the people who had already acted. Deliverable: a five-minute account that ends with the specific control you added afterwards, plus one occasion since when that control has fired.
Approach
- Read the probe: the interviewer is testing whether you detect your own errors and whether the correction was structural. Pick an error with a nameable mechanism (a denominator built from distinct members instead of member-months, a series read before claims runout completed, a cohort selected on the outcome) so the story has a diagnosis rather than a mood.
- Open with the decision that was made on your number, not with the bug. The severity of an analytic error is measured in the action it caused: a rate filing, a staffing plan, a programme expansion.
- State the counterfactual number. 'The true figure was 14.1 admissions per 1,000 member-years, not 9.8' is a fact; 'it was off by a lot' is not.
- Describe the detection path honestly, including whether you found it or somebody else did. Say how long it took, because time to detection is the part you actually control next time.
- Close on the control, and make it something that protects a reader who has never met you: a unit test on the denominator, a paid-through date stamped on every chart, a required exclusions block in the spec template. Name one later date it caught something.
Follow-up
- What would have had to be true for you to catch this before delivery rather than after?
- Did the stakeholder change how much they trust your work, and did you want them to?
- Give an example of a control you added that turned out to be the wrong control.
Defend a null result against a programme sponsor
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
- 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.
- 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.
- 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.
- 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.
- 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?
- 01
How do you handle missing data in a longitudinal study, and what are the implications for bias?
- 02
Describe an analysis you delivered that was wrong, where someone acted on it before the error surfaced. Pick something with a mechanism you can name, not a typo. Cover what the number claimed, what was actually true, how much time passed before it was caught, who caught it, and what you told the people who had already acted. Deliverable: a five-minute account that ends with the specific control you added afterwards, plus one occasion since when that control has fired.
- 03
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.
Is this an official PSI CRO interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at PSI CRO. Rounds and questions reflect what candidates have reported, not a process PSI CRO has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗Is the technical interview as difficult as it sounds?
Yes, it is designed to be challenging. The focus is on your ability to provide accurate, technical answers under time pressure, so ensure your fundamentals are sharp.
PracHub interview research ↗What is the company culture like?
PSI CRO has a professional, high-performance culture. It is a place for those who enjoy structured, precise work where the quality of the scientific output is the primary measure of success.
PracHub interview research ↗Does the role allow for remote work?
PSI CRO offers remote positions for this role, providing flexibility for the right candidate who demonstrates the ability to work autonomously in a high-stakes, decentralized environment.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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