As a Data Scientist at Roche, you operate at the critical intersection of advanced analytics, healthcare innovation, and large-scale data systems. You will build and deploy predictive models, design robust experiments, and extract actionable insights from complex biomedical and product datasets. Your work directly influences research and development, clinical diagnostics, and commercial healthcare products that impact patients globally.
This role requires a unique blend of heavy technical execution and collaborative scientific inquiry. You will partner closely with multidisciplinary teams—including software engineers, clinical researchers, product managers, and domain experts—to translate ambiguous business and scientific challenges into well-defined analytical frameworks. Whether you are analyzing longitudinal health studies, optimizing diagnostic algorithms, or architecting product telemetry pipelines, your contributions shape core product strategies and drive data-informed decision-making across the enterprise.
Expect a fast-paced yet highly collaborative environment where scientific rigor is paramount. While you will leverage modern machine learning and statistical methodologies, you must also communicate complex findings clearly to stakeholders outside of data science. Success in this role demands both intellectual curiosity and a disciplined approach to experimentation, metric design, and production-grade code development.
Recruiter Screening Call
reportedA 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 Screen
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
Technical Evaluations
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
Behavioral Panels
reportedBehavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.
What to demonstrate
- Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
- Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
- Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier
How to prepare
- For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
- Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
- Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
Presentation Rounds
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.
PracHub editorial advice for the preparation topics above.
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.
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.
Ending an analysis without a recommendation or next step
Close with what you would do and what would change your mind, stated as a condition you can check later. If the evidence is genuinely inconclusive, recommend the specific next measurement and say what it costs in time or exposure.
Reporting a p-value with no effect size or interval
Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you choose between parametric and non-parametric testing method…
How do you choose between parametric and non-parametric testing methods when analyzing skewed biomedical distributions?
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
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
What processing techniques would you use for cleaning and transforming…
What processing techniques would you use for cleaning and transforming noisy longitudinal datasets?
Approach
- 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.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- What would you monitor after launch to know the model is still valid?
- How would you choose the decision threshold, and who owns that choice?
Sessionise transfer chains into episodes, then count readmissions
encounter has encounter_id, patient_id, facility_id, encounter_type, admit_ts, discharge_ts, admission_type, discharge_disposition, is_planned_admission, principal_diagnosis_code. Chain acute-to-acute transfers into episodes of care: an inpatient encounter discharged as transfer_acute, followed by another inpatient encounter for the same patient at a different facility admitting within 24 hours of that discharge, belongs to the same episode. Then compute the 30-day unplanned readmission rate over episodes, excluding index episodes that are planned, that end in expired, hospice or against medical advice, or that are still open. Return encounter_id to episode_id, plus the rate.
Approach
- Restrict to inpatient encounters for the chaining step and sort by patient_id, admit_ts. Observation and emergency encounters are not acute inpatient stays and pooling them changes both the chain and the denominator.
- Build a boolean continues flag: the previous row is the same patient, its discharge_disposition is transfer_acute, its facility_id differs from this row's, and admit_ts minus previous discharge_ts is between 0 and 24 hours. Episode id = cumsum of the negation of that flag. This is the vectorised form of a sessionisation loop and is why the pattern generalises to any event stream.
- Collapse to episode grain taking first admit_ts, last discharge_ts, last discharge_disposition, and any() of is_planned_admission. The episode inherits its exit state, not its entry state, which is why a transfer chain ending at home is one home discharge rather than two transfers.
- Apply index exclusions at episode grain. Dropping episodes that end in death is not optional: a dead patient cannot be readmitted, so leaving them in inflates the denominator and depresses the rate for exactly the panels caring for the sickest members.
- For each surviving index episode, find the next unplanned acute inpatient episode for that patient admitting within 30 days of the index discharge_ts. Count events at episode grain, since one patient can contribute several index episodes. Report the raw rate and say plainly that it is not comparable across panels without risk adjustment, which is what the observed-over-expected form exists for.
Worked solution 45 min
- Filter to encounter_type 'inpatient', sort by patient_id and admit_ts, and shift discharge_ts, discharge_disposition, facility_id and patient_id by one row.
- Compute continues as same patient AND prior disposition is transfer_acute AND facility differs AND 0 <= (admit_ts - prior discharge_ts) <= 24 hours; episode_id = (~continues).cumsum().
- Aggregate to episodes: first admit_ts, last discharge_ts, last discharge_disposition, any planned flag, patient_id.
- Drop index-ineligible episodes: planned, disposition in expired, hospice or ama, and null discharge_ts.
- For each eligible index episode, search the same patient's later episodes for an unplanned admit_ts within 30 days of index discharge_ts; a merge_asof on patient with a forward direction and a 30-day tolerance does this without a nested loop.
- Rate = index episodes with an event divided by eligible index episodes; return the encounter-to-episode map alongside it.
Follow-up
- discharge_ts is null on an open stay in the middle of a chain. What does your flag do, and what should it do?
- A patient is transferred out and then back to the originating facility within 24 hours. Should that chain, and does your facility_id condition handle it?
- Is a readmission itself eligible to serve as a later index episode? Say what you chose and what the choice does to the rate.
What database optimization techniques do you use when querying massive…
What database optimization techniques do you use when querying massive, distributed biomedical data tables?
Approach
- Say which table is the grain you start from, and join outward from 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
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Write a complex query using SQL window functions to calculate rolling …
Write a complex query using SQL window functions to calculate rolling user engagement retention rates across longitudinal patient cohorts.
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Measure ninety-day primary care contact for an enrollment cohort
member_enrollment gives span_id, member_id, product_type, effective_date and termination_date, which is NULL while a span is active. medical_claim_line gives member_id, service_start_date, procedure_code and claim_status. The cohort is members whose earliest 2025 span has effective_date in January 2025 and who stay covered at least 90 days from it. Return, by product_type, cohort size and the share with at least one paid claim line whose procedure_code is in a supplied list of evaluation-and-management office visit codes and whose service_start_date falls in the half-open window from effective_date to effective_date plus 90 days.
Approach
- Build the cohort as one row per member before touching claims. Take the earliest 2025 span per member, require its effective_date in January, and require coverage to effective_date + 90 days, which means termination_date IS NULL OR termination_date >= effective_date + 89.
- Use EXISTS for the numerator rather than a join. A member with four office visits must count once; joining and counting rows inflates the numerator and can push the rate above 1.
- Keep the window half-open and anchored per member. Every member has a different effective_date, so a single fixed calendar window is a different metric and will not match the cohort definition.
- Aggregate numerator and denominator in one pass with a conditional sum grouped by product_type, and carry the raw counts alongside the rate so the reader can see the denominator.
- State the reportability rule on the output: the January cohort is not final until 90 days plus professional-claims runout have passed, so label the paid-through date.
Worked solution 15 min
- CTE first_span: per member, the 2025 span with the minimum effective_date, filtered to January effective dates.
- Add the continuous-coverage requirement to that CTE using COALESCE(termination_date, DATE '9999-12-31').
- Add an EXISTS subquery on medical_claim_line correlated on member_id, restricted to the code list, claim_status = 'paid', and service_start_date >= effective_date AND < effective_date + 90.
- Group by product_type: COUNT(*) as cohort_size, SUM(CASE WHEN contacted THEN 1 ELSE 0 END) as contacted, and their quotient.
- Print the paid-through date used, and re-run at a 30-day window to confirm the rate falls.
Follow-up
- A member changes plan on day 40, closing one span and opening another. Is she still in the cohort, and which product_type does she report under?
- What moves if denied lines are allowed into the numerator, and which definition is right for an access metric?
- How would you show this as a cumulative curve by day since effective_date instead of one 90-day point?
What product metric design principles would you apply when balancing u…
What product metric design principles would you apply when balancing user privacy with predictive personalization in healthcare applications?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Fix the population and the time window before naming any metric.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you design the core evaluation metrics for a new digital hea…
How would you design the core evaluation metrics for a new digital health diagnostic tool?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How do you determine whether a drop in user retention is driven by sea…
How do you determine whether a drop in user retention is driven by seasonal trends or a regression in product performance?
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?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Imagine a key engagement metric drops by fifteen percent week-over-wee…
Imagine a key engagement metric drops by fifteen percent week-over-week. How would you structure your investigation to diagnose the root cause?
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.
- 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?
Explain how you establish statistical significance and guardrail metri…
Explain how you establish statistical significance and guardrail metrics when running concurrent experiments.
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- 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?
- How would you handle interference between treated and control units?
What are the most common experimentation pitfalls, such as sample rati…
What are the most common experimentation pitfalls, such as sample ratio mismatch or peeking, and how do you prevent them?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Design a switchback when a shared review queue breaks SUTVA
A prior-authorisation auto-approval rule for a set of imaging procedure codes is meant to cut turnaround time. Requests sit in one shared queue worked by a fixed nurse pool across two review sites, so auto-approving one request frees reviewer capacity and speeds up the requests that were not auto-approved. Outcomes are median hours from received_date to adjudicated_at and the first-pass denial rate. Request-level randomisation is therefore invalid. Design the test: randomisation unit, block length, effective sample size, and the analysis.
Approach
- Name the violation precisely. The treatment assigned to one request changes the outcome of other requests through a shared capacity constraint, so the stable unit treatment value assumption fails and a request-level difference in means estimates neither the direct effect nor the total effect.
- Randomise time rather than requests: assign the policy on or off for whole (site, time block) cells. Every request inside a block experiences one consistent queue regime, so the block-level contrast is a genuine system-level effect including the capacity spillover you care about.
- Choose block length from the carryover horizon, not from convenience. Estimate how long the queue takes to return to steady state after a load shock from the historical backlog series, then set blocks well longer than that and discard a burn-in equal to the drain time at the start of each block.
- Analyse at the block level. The unit of analysis is the block mean, the effective sample size is the number of analysable blocks and not the tens of thousands of requests inside them, and standard errors must be cluster-robust or HAC because adjacent blocks share staffing and seasonality.
- Balance the calendar deliberately: use whole weeks, or block on day of week, so the policy is not disproportionately on during Mondays. If the shared resource can instead be partitioned (separate nurse pods with separate queues), cluster randomisation on pods is simpler, but with two sites you have two clusters, which is not a design.
Worked solution 35 min
- Estimate the queue drain time from historical backlog after a volume spike, and set block length to several multiples of it (for example weekly blocks if the queue settles within a day).
- Randomise policy on or off independently per (site, week), balanced so each site gets roughly half treated weeks.
- Drop the burn-in window at the start of each block, then compute one block-level summary per (site, week) for each outcome.
- Estimate the effect as a block-level regression of the summary on the treatment indicator with site and calendar-period fixed effects, using cluster-robust or HAC standard errors.
- Power the study from the historical block-to-block standard deviation of the outcome, not from request-level variance.
Follow-up
- Suppose you had randomised at the request level and 30 percent of the effect leaked to the control requests. What does that do to the required sample size?
- Turnaround time is right-skewed with a long tail. Would you analyse block medians, block means, or something else?
- How would you detect that your chosen block length is too short for the carryover you assumed?
Readmission rate rose while every facility improved
Quarter over quarter the 30-day unplanned readmission rate for an attributed population rose from 14.2 to 15.1 percent, yet all eleven facilities in encounter show a flat or falling rate over the same period. You have encounter (facility_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, is_planned_admission, index_encounter_id) and twelve months of prior medical_claim_line for comorbidities. Explain the direction reversal numerically, and state what the population-level number should be reported as instead.
Approach
- Confirm the reversal is arithmetic, not a definition difference, by recomputing both the facility rates and the pooled rate from the same query with the same index-stay exclusions.
- Decompose the pooled change exactly with a Kitagawa split. With facility weights w and within-facility rates r, the change equals the sum over facilities of (w_1 - w_0) times (r_0 + r_1)/2, the mix term, plus the sum of (w_0 + w_1)/2 times (r_1 - r_0), the within term. If the mix term exceeds the total change and the within term is negative, the reversal is volume shifting toward facilities with higher baseline rates.
- Ask why the weights moved. Check index-stay volume by facility, and check whether an exclusion changed behaviour: acute-to-acute transfers (discharge_disposition 'transfer_acute') must not open an index stay, and if one facility began transferring more, its denominator shrinks toward its sickest retained patients.
- Check case mix inside the facilities too, using drg_code and comorbidities drawn from the 12 months of claims before admit_ts, excluding complications of the index stay itself. A facility rate can be flat while its case mix worsens.
- Replace the raw pooled rate with an observed over expected ratio, where expected is the sum of predicted probabilities from a model fitted on age, sex and prior comorbidities. Raw rates are not comparable across a population whose facility and case mix is moving.
Follow-up
- Your O/E ratio is 1.04 with a 95 percent interval of 0.96 to 1.13. What does that let you say to a facility, and what does it not?
- If one facility contributes 40 percent of index stays, how does that change how you present the decomposition?
- How would you handle a member with two index stays in the quarter, where the second is also a readmission from the first?
For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.
How do you resolve conflicts with cross-functional stakeholders regard…
How do you resolve conflicts with cross-functional stakeholders regarding project direction or timelines?
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Name the disagreement or constraint, and how you resolved it with evidence.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Disagree with a product manager about an adherence nudge
A product manager proposes shipping a refill-reminder feature to the whole member base, citing an internal analysis: members with 12-month proportion of days covered at or above 0.80 had 31 percent fewer admissions than members below it. The roadmap date is three weeks out and engineering is scoped. You believe the comparison builds survival and continuous coverage into the exposure definition, and is separately confounded by the kind of member who refills on time. Deliverable: what you say in the room, what you send afterwards, and the smallest study you would accept as sufficient to ship.
Approach
- The probe is whether you can be specific and non-territorial at once. A disagreement that arrives as 'the analysis is flawed' costs you the room; one that names a mechanism and offers a cheaper path keeps it.
- Name the two defects separately, because they have different fixes. Classifying a member as adherent over 12 months requires them to stay alive and stay covered for those 12 months, and that guaranteed event-free interval is assigned to the adherent group. Separately, members who refill on schedule differ from those who do not in ways claims never record.
- Show rather than assert. Rerun the same comparison as a landmark analysis: classify adherence over days 1 to 90, start follow-up at day 91 for everyone still enrolled and event-free, and report how much of the 31 percent survives.
- Separate the association claim from the intervention claim out loud. Nothing in the analysis speaks to whether a reminder changes refill behaviour, which is the actual product question and is separately testable.
- Offer the shippable path: a randomised holdout on a subset with proportion of days covered as the primary endpoint, powered on the plausible effect of a reminder rather than on the 31 percent, with a stated read date.
Follow-up
- The landmark rerun still shows a 20 percent difference. Do you ship?
- The product manager says a holdout delays launch. How do you cost that argument?
- What primary endpoint do you choose, and why not admissions?
Push back on an over-broad extract without blocking the work
A researcher asks for a one-off extract covering 12,000 members: member_id, date of birth, five-digit residential postal code, full lab_result rows including value_text, and clinical note text, held on their laptop for a retrospective analysis of one lab analyte over time. They describe it as de-identified because names are excluded. A flat refusal is not an acceptable answer. Deliverable: the column list you will actually provide with a justification per retained field, the sentence explaining why free text defeats an enumerated-identifier approach, your small-cell rule for the published stratification, and what you deliver this week.
Approach
- The probe is whether privacy is a design skill for you or a compliance reflex. Flat refusal and unquestioned fulfilment both fail, for opposite reasons.
- Reconstruct the analysis from the research question rather than from the request. A longitudinal trend in one analyte needs a stable pseudonymous subject key, the analyte's loinc_code, value_numeric, units, the reference bounds, and a date. It does not need names, notes, or a precise birth date.
- Replace each identifier with the least precise version that still answers the question: a salted per-study pseudonym instead of member_id, age in years or an age band instead of date of birth, a consistent per-subject date offset if only intervals matter, and a coarser geography than five-digit postal code.
- Explain the free-text problem in one sentence. Removing an enumerated list of identifier fields does not de-identify a note, because notes carry names, relationships, employers, dates and rare clinical detail in prose, so only a reviewed extraction into structured fields or a formal statistical determination addresses it.
- Set the publication rule before the analysis runs. Suppress cells below a stated threshold and suppress complements too, because a reader holding a row total and all but one cell recovers the suppressed count by subtraction.
- Keep them moving. Deliver the reduced extract into a controlled environment this week rather than onto a laptop, and offer to derive any note-based variable yourself as a structured flag so they never need the prose.
Follow-up
- They say the age band loses resolution their model needs. What do you do?
- How do you set the suppression threshold, and who signs off on it?
- The analysis later needs linking to a second data source. What changes?
- 01
How do you resolve conflicts with cross-functional stakeholders regarding project direction or timelines?
- 02
A product manager proposes shipping a refill-reminder feature to the whole member base, citing an internal analysis: members with 12-month proportion of days covered at or above 0.80 had 31 percent fewer admissions than members below it. The roadmap date is three weeks out and engineering is scoped. You believe the comparison builds survival and continuous coverage into the exposure definition, and is separately confounded by the kind of member who refills on time. Deliverable: what you say in the room, what you send afterwards, and the smallest study you would accept as sufficient to ship.
- 03
A researcher asks for a one-off extract covering 12,000 members: member_id, date of birth, five-digit residential postal code, full lab_result rows including value_text, and clinical note text, held on their laptop for a retrospective analysis of one lab analyte over time. They describe it as de-identified because names are excluded. A flat refusal is not an acceptable answer. Deliverable: the column list you will actually provide with a justification per retained field, the sentence explaining why free text defeats an enumerated-identifier approach, your small-cell rule for the published stratification, and what you deliver this week.
Is this an official Roche interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Roche. Rounds and questions reflect what candidates have reported, not a process Roche has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process at Roche?
The interview process is moderately rigorous, focusing heavily on practical problem-solving, behavioral alignment, and clear communication rather than hazing-style algorithmic puzzles. While technical rounds test your core competencies in SQL, statistics, and machine learning, the overall atmosphere is professional, supportive, and conversational.
PracHub interview research ↗How much time should I invest in preparing for behavioral rounds?
Do not neglect the behavioral portion, as multi-stage loops routinely feature dedicated culture and leadership panels. Prepare 5 to 7 detailed stories from your past experience highlighting cross-functional collaboration, conflict resolution, and independent learning using structured frameworks.
PracHub interview research ↗Are AI coding tools or external assistants used during technical rounds?
Interview loops typically test your fundamental coding and problem-solving abilities without relying on automated AI assistants. Focus on practicing live coding and query writing independently so you can articulate your logic cleanly on the spot.
PracHub interview research ↗What is the typical timeline from initial screen to final offer?
The end-to-end timeline varies by region and team, often ranging from 4 to 6 weeks from initial recruiter contact. Some specialized R&D loops involving multiple panel reviews may take slightly longer, but recruiters generally maintain steady communication throughout.
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.
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