As a Data Scientist at Kaiser Permanente, you will sit at the intersection of cutting-edge technology and patient-centered healthcare. Kaiser Permanente is one of the nation's largest not-for-profit health plans and clinical providers, meaning your work will directly impact millions of members and patients. Unlike tech-first companies where optimization centers around clicks or ad-revenue, data science here focuses on improving clinical outcomes, predicting patient risks, optimizing hospital operations, and personalizing the member experience.
You will be responsible for transforming massive, complex, and highly sensitive electronic health records (EHR) and operational data into actionable insights. Your models might predict patient readmission risks, optimize staffing levels in emergency departments, or automate the extraction of clinical insights from unstructured medical notes. This requires not only deep technical expertise but also a strong sense of empathy and a commitment to ethical data use.
The data ecosystem at Kaiser Permanente is vast and complex. You will work alongside clinicians, product managers, and software engineers to deploy machine learning models into live clinical and administrative workflows. For a motivated, this role offers a rare opportunity to solve highly complex analytical challenges where the ultimate return on investment is measured in lives saved and health outcomes improved.
Resume Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Phone Screen
reportedMost candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.
What to demonstrate
- Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
- Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
- The substance of the questions you ask back, which an experienced screener reads as a level signal
How to prepare
- Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
- Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
- Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered
Technical Evaluations
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Panel Interview
reportedWhere a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.
What to demonstrate
- Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
- Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
- Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
- Whether you ask what decision is actually on the table before explaining anything
How to prepare
- Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
- For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
- Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else
PracHub editorial advice for the preparation topics above.
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.
Immortal time in adherence, treatment, and enrolment definitions
Classifying members as adherent, treated, or programme-enrolled requires them to survive and stay covered long enough to accumulate the defining events. That guaranteed event-free interval is assigned to the exposed group, so the exposure looks protective for reasons that have nothing to do with the treatment. Adherence studies are the classic case: measuring 12-month proportion of days covered and then comparing mortality builds survival into the exposure definition. Use time-varying exposure or a landmark analysis with the classification window excluded from follow-up.
Reading experiment results before checking the arm split
Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.
Sizing estimates built on unnamed, unrevisable assumptions
Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Which machine learning algorithm would you select to predict whether a…
Which machine learning algorithm would you select to predict whether a patient will miss their scheduled appointment, and why?
Approach
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- What would you monitor after launch to know the model is still valid?
Walk me through the mathematical formulation and logical steps to impl…
Walk me through the mathematical formulation and logical steps to implement the K-Nearest Neighbors (KNN) algorithm from scratch.
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Say how the offline result would be validated online before it is trusted.
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
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?
Explain the difference between lists and tuples in Python, and when yo…
Explain the difference between lists and tuples in Python, and when you would use each.
Approach
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- 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 does the query change if the join becomes one-to-many?
Report site activation to first randomization without hiding non-enrollers
study_site holds one row per (study_id, site_id): site_activation_date, which is NULL for a site selected but never activated, and site_status. study_subject_visit holds subject_visit_id, study_id, site_id, subject_id, randomization_ts (NULL for screen failures), visit_name, visit_number, actual_visit_date and visit_status, with one row per subject per protocol visit, so randomization_ts repeats across every visit of a randomized subject and a site that has screened nobody has no rows there at all. For one study_id and a stated data cut date, return per activated site the activation date, the first randomization_ts and the days between them, with activated sites that have randomized nobody still present. Add a study-level summary giving median days to first randomization and the share of activated sites with no randomization by day 90.
Approach
- Take the site universe from study_site, never from study_subject_visit. The visit table can only contain sites that have screened at least one subject, so a universe built from DISTINCT site_id there omits exactly the sites this metric exists to expose. Filter study_site to the study and to site_activation_date IS NOT NULL, then LEFT JOIN the per-site first randomization.
- Collapse the grain before aggregating anything on the visit side. The table repeats randomization_ts across every visit of a randomized subject, so any count taken at row grain is inflated by visits per subject. MIN(randomization_ts) per site is safe, COUNT is not.
- State the date boundary rule. randomization_ts is a timestamp and site_activation_date is a date, so a randomization at 23:40 local time can land on either side of a day boundary depending on the zone the timestamp is stored in. Cast once, deliberately, and say which zone.
- Use PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY days) for the median and be explicit that it ignores NULLs, so it is a median over enrolling sites only. That is exactly why the non-enroller share is reported beside it rather than instead of it.
- Censor the day-90 denominator against the data cut. A site activated 40 days before the cut has not had 90 days to randomize, so it cannot be scored on that bar and belongs outside that denominator.
Worked solution 25 min
- CTE sites: site_id and site_activation_date from study_site for the study where site_activation_date IS NOT NULL. The (study_id, site_id) key already gives one activation date per site, so assert that key rather than de-duplicating defensively.
- CTE first_rand: site_id and MIN(randomization_ts) over study_subject_visit rows for the study where randomization_ts IS NOT NULL, grouped by site_id.
- LEFT JOIN sites to first_rand and compute days_to_first_randomization with an explicit cast of the timestamp to a date.
- Summary: PERCENTILE_CONT(0.5) over non-null days, plus the count and share of sites with no randomization by day 90, restricted to sites activated at least 90 days before the data cut.
- Compare the site-level row count with COUNT(DISTINCT site_id) in study_subject_visit for the study; the difference is the activated sites that have screened nobody, which is the population the metric is for.
Follow-up
- The same site_id appears under two studies. What breaks, and what is the correct grain for this metric?
- Would you present this as a median, as a Kaplan-Meier curve of time to first randomization, or both, and what does the curve show that the median cannot?
- A site randomized one subject who withdrew at visit 2. Does that site count as enrolling here, and how does that differ from a randomized-and-retained measure?
Write a clean, optimized function to implement the Fibonacci Series us…
Write a clean, optimized function to implement the Fibonacci Series using both iterative and recursive approaches.
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you optimize a Python script that is running out of memory w…
How would you optimize a Python script that is running out of memory when processing a large medical dataset?
Approach
- 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.
- 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?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design an A/B test to evaluate a new digital health feat…
How would you design an A/B test to evaluate a new digital health feature in the member portal?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
- Decide the analysis before seeing data, including how long it runs and when you look.
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?
Estimate a programme effect at a fixed risk-score enrolment cutoff
Care management enrols members whose prospective_risk_score in member_enrollment crosses a fixed annual threshold of 1.75; above the line enrolment is near-automatic and below it unavailable, and about 85 percent of members above the line actually enrol. Leadership refused a randomised holdout and wants the programme's effect on next-year allowed PMPM. Two years of scores and claims are available. State the design, the estimand it identifies, the assumptions, and the diagnostics you would run before reporting anything.
Approach
- Propose a fuzzy regression discontinuity on prospective_risk_score at 1.75. Uptake jumps but is not complete, so the estimator is the ratio of the jump in outcome to the jump in enrolment probability at the cutoff, which is a Wald ratio.
- State the estimand honestly up front: a local effect for members near 1.75, who are the least sick enrollees. It does not identify the effect on the top risk decile, which is usually the number leadership actually wants, and saying so before the result lands is what keeps the analysis credible.
- Say explicitly why the obvious alternative fails. A pre-post on enrolled members selects on a high score in year one, and such cohorts regress toward the mean in year two whether or not anything is done, so a pre-post design reports savings every time. The discontinuity is immune because it compares members on either side of an arbitrary line.
- Specify the estimation: local linear fits on each side, triangular kernel, a data-driven MSE-optimal bandwidth, and robust bias-corrected inference rather than a naive local-linear confidence interval. Do not fit a high-order global polynomial.
- Run the falsification suite before looking at the effect: a density test for manipulation of the running variable, continuity of pre-determined covariates at the cutoff, sensitivity across bandwidths, and placebo cutoffs away from 1.75.
Worked solution 30 min
- Plot the outcome and the enrolment rate against the centred running variable in uniform bins to confirm a visible jump in both.
- Estimate the first stage (jump in enrolment probability at 1.75) and the reduced form (jump in next-year PMPM), then take the ratio for the fuzzy RD estimate.
- Select the bandwidth by an MSE-optimal rule with a triangular kernel and local linear fits, and report robust bias-corrected intervals.
- Run the density test at 1.75 and the covariate continuity tests on age, prior-year cost, dual_eligible_flag, and product_type.
- Re-estimate at 0.5x, 1x, and 2x the selected bandwidth, and at placebo cutoffs of 1.50 and 2.00.
Follow-up
- Risk scores are built from coded diagnoses, and coding intensity is something people can influence. What would manipulation look like in the density, and what do you do if you see it?
- The MSE-optimal bandwidth leaves 900 members. How do you decide whether to report an underpowered estimate at all?
- Design an encouragement version instead: randomise the invitation rather than enrolment. What does instrumental variables buy you, and which assumption is most at risk?
Pooled persistency fell but no one lost coverage
Pooled 12-month enrollment persistency fell from 0.78 to 0.66 for the cohort anchored in a single month. member_enrollment carries span_id, member_id, plan_id, product_type, effective_date, termination_date, termination_reason_code, dual_eligible_flag and region_code. The metric allows at most one gap of 45 days or fewer. Product mix in that anchor month was not stable. Determine how much of the fall is real disenrollment rather than span bookkeeping or mix, and deliver a persistency series a contract owner could act on.
Approach
- Inspect the query's unit of analysis first. Spans are not members, and a benefit or plan change closes one span and opens the next on the same day, so any logic that treats a non-null termination_date as loss of coverage counts an administrative event as churn.
- Rebuild member-level coverage by unioning all spans per member and merging adjacent or overlapping ones, then apply the 45-day gap rule to the merged intervals rather than per span.
- Cut the terminations by termination_reason_code. plan_change and eligibility_redetermination have entirely different meanings for a contract, and death is not churn you can address.
- Split by product_type and stop pooling. Commercial open enrollment and managed medicaid redetermination produce structurally different churn on different calendars, and the pooled average of the two is a number that describes nobody.
- Compare the anchor month to the same month in the prior year rather than to the adjacent month, because enrollment is strongly seasonal and an anchor month sitting on an open-enrollment or redetermination boundary is not comparable to its neighbour.
- Apply the Kitagawa decomposition across product_type to separate how much of the 12 point fall is mix, meaning the anchor cohort simply contained more of a high-churn product, from how much is within-product deterioration.
Follow-up
- Why 45 days, and what would change if the allowable gap were 30 or 60?
- A member terminates and re-enrolls 90 days later under a different plan_id. Are they retained? Defend your answer against the opposite one.
- What guardrail would catch someone improving persistency by declining to enroll members likely to churn?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.
How do you handle missing or corrupt data in a large Pandas DataFrame?
How do you handle missing or corrupt data in a large Pandas DataFrame?
Approach
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Make an honest case for your own analytical impact
You are asked to account for a year of your work. Constraint: you may only use measures that existed before you knew the outcome, and you must include one project that moved nothing. The portfolio contains a measure specification that changed how a contract settles, a predictive model that shipped, a self-serve dashboard, and an evaluation that killed a proposed programme. Deliverable: how you attribute value to each, which of them you claim credit for and which you do not, and the one number you would accept being judged on next year.
Approach
- The probe is whether you can reason counterfactually about your own work, which is the same discipline this domain demands of you for programmes, applied where it is uncomfortable.
- State the counterfactual for each item: what would have happened without you. A measure specification that changed how a contract settles has a dollar counterfactual you can compute. A dashboard's counterfactual is analyst hours, which is real and small, and attaching a revenue number to it is where these accounts usually turn into fiction.
- Treat the killed programme as your strongest item and say why. Avoided spend is real value, it is attributable because the decision turned on your evaluation, and it is the one item nobody else will count on your behalf.
- Report the shipped model the way you would report anyone's model: calibration in the subgroups that matter, adoption by the people it was built for, and whether any decision changed. A model that is live but ignored has zero impact, and saying so is the credibility test in this answer.
- Name the failed project with its mechanism and its cost, then say what earlier signal you would watch to kill it sooner. Time to kill is a legitimate impact measure.
- Choose next year's number so that it can go down. A metric that only ratchets upward is not a commitment.
Follow-up
- Two other people also claim the contract measure. How do you split it?
- Your model is live and used, but the decisions it informs would have been the same without it. Is that impact?
- What is the strongest argument that your year had less impact than you just described?
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.
- 01
How do you handle missing or corrupt data in a large Pandas DataFrame?
- 02
You are asked to account for a year of your work. Constraint: you may only use measures that existed before you knew the outcome, and you must include one project that moved nothing. The portfolio contains a measure specification that changed how a contract settles, a predictive model that shipped, a self-serve dashboard, and an evaluation that killed a proposed programme. Deliverable: how you attribute value to each, which of them you claim credit for and which you do not, and the one number you would accept being judged on next year.
- 03
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.
Is this an official Kaiser Permanente interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Kaiser Permanente. Rounds and questions reflect what candidates have reported, not a process Kaiser Permanente has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical are the coding interviews?
The coding interviews are moderately rigorous. You should expect to solve standard data structure and algorithm questions (such as sorting and recursion) and be ready to implement machine learning algorithms (like KNN) from scratch without using external machine learning libraries.
PracHub interview research ↗Do I need prior healthcare experience to apply?
While prior healthcare experience is a significant advantage, it is not a strict requirement. However, you must demonstrate a strong willingness to learn healthcare concepts, understand the sensitivity of patient data, and show an interest in clinical and operational problem-solving.
PracHub interview research ↗What is the typical interview timeline?
The interview process can take anywhere from three to six weeks from the initial HR screen to the final decision. Because Kaiser Permanente is a large organization, some candidates have reported administrative delays, so maintaining proactive communication with your recruiter is beneficial.
PracHub interview research ↗Is the work environment hybrid or remote?
The working model depends heavily on the specific team and location. Many data science teams operate under a hybrid model, requiring occasional visits to regional offices in locations such as Oakland, Pleasanton, or Burbank, California.
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
Explore related engineering environments and compare the preparation focus.
Veracyte Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for Veracyte data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
Zoetis Data Scientist Interview Questions & Guide 2026
Metric-diagnosis practice for Zoetis data scientist candidates: a number moves week over week and you work out why, splitting by segment, ruling out a logging or release change, and separating a mix shift from a real change in user behaviou
Vizient Data Scientist Interview Questions & Guide 2026
Communication practice for Vizient data scientist candidates: turn an analysis into a recommendation, name the uncertainty honestly, and answer the follow-up a skeptical stakeholder will raise.
Vibrant Emotional Health Data Scientist Interview Questions & Guide 2026
Case practice for the Vibrant Emotional Health data scientist interview, where the prompt is deliberately underspecified and your first move is scoping it into something measurable.