As a Data Scientist at Dexcom, you sit at the exciting intersection of advanced analytics, health technology, and product innovation. You will play a pivotal role in translating complex data streams from continuous glucose monitoring systems and customer touchpoints into actionable insights that directly improve patient outcomes. Your work impacts millions of users relying on life-changing medical devices, meaning your quantitative models and product experiments carry high visibility and direct clinical relevance.
This role requires a blend of rigorous technical execution and strong product sense. Whether you are optimizing customer success metrics, translating behavioral evidence into product features, or designing experiments to test user engagement features, you will collaborate closely with product managers, software engineers, and clinical researchers. You will work within specialized domains like evidence translation and customer experience, making sense of large-scale behavioral and physiological data.
Expect an environment that values intellectual curiosity, rigorous experimentation, and a genuine passion for digital health. While the work is fast-paced and intellectually demanding, it offers an incredible opportunity to influence how continuous health monitoring empowers individuals worldwide. Success here requires you to be both a meticulous statistician and a pragmatic product thinker who can communicate complex analytical findings to diverse cross-functional stakeholders.
Recruiter 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 Screening
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
Panel Presentation
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
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.
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.
Building features from data that postdates the prediction time
Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
When would you choose a Bayesian approach over frequentist hypothesis …
When would you choose a Bayesian approach over frequentist hypothesis testing in a clinical data context?
Approach
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
Follow-up
- Which assumption here is most likely to be violated in practice?
- How would you explain this result to someone who does not know statistics?
Explain neural networks and discuss what the best approach is when dea…
Explain neural networks and discuss what the best approach is when dealing with overfitting on small tabular datasets.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- 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?
Bootstrap a confidence interval for a ratio-of-sums PMPM
You have one row per member for a fully run-out incurred year: member_id, member_months, allowed_amount. Report allowed PMPM, defined as sum(allowed_amount) divided by sum(member_months), with a 95 percent confidence interval from a bootstrap you write yourself. No library resampler. Resample members with replacement, recompute the ratio on each of 2,000 replicates, take a percentile interval, and seed the generator so the result reproduces. In two sentences, state why resampling claim lines instead of members would be wrong.
Approach
- Name the estimator precisely: PMPM is a ratio of sums, not the mean of per-member ratios. The two differ whenever exposure correlates with spend, and it does, because members who churn out mid-year are systematically different from members covered all twelve months.
- Resample the unit of independence, which is the member, and recompute numerator and denominator together on the same draw. Recomputing only the numerator against a fixed denominator understates variance and is a common slip.
- Pull member_months and allowed_amount into numpy arrays once, then draw indices with rng.integers(0, n, size=n) per replicate. Index draws over arrays run one to two orders of magnitude faster than DataFrame.sample, which is the difference between 2,000 replicates and 200.
- Take the 2.5th and 97.5th percentiles of the replicate ratios. Expect an interval that is asymmetric around the point estimate, because healthcare spend is right skewed. That asymmetry is the method working, not a bug to centre away.
- Answer the line-resampling question concretely: the many lines of one high-cost member are not independent draws, so line resampling shrinks the interval toward zero width, and member_months has no line-level analogue, so the denominator becomes undefined.
Worked solution 25 min
- Compute the point estimate as allowed.sum() / months.sum() on the full sample.
- Extract both columns to float numpy arrays and create rng = np.random.default_rng(seed).
- Loop 2,000 times: idx = rng.integers(0, n, n); ratio = allowed[idx].sum() / months[idx].sum(); store it.
- Take np.percentile(replicates, [2.5, 97.5]) and report the point estimate with that interval and the seed.
Follow-up
- Half a percent of members have a transplant or a long NICU stay. Does a percentile interval still have nominal coverage, and what would you do about the tail?
- The question changes to comparing PMPM across provider panels. What becomes the resampling unit and why?
- How would you use the same replicates to test whether this year's PMPM differs from last year's, given the same members appear in both?
How do you select north-star metrics for a behavioral data science ini…
How do you select north-star metrics for a behavioral data science initiative focused on customer success?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- 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.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
How would you identify duplicate user sessions in a large telemetry da…
How would you identify duplicate user sessions in a large telemetry dataset using advanced SQL clauses?
Approach
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
Given a table of continuous glucose monitoring readings, write a query…
Given a table of continuous glucose monitoring readings, write a query to find the maximum consecutive hours a user stayed within their target glucose range.
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
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Find inpatient encounters with no resulted lab during the stay
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 product metric design principles would you apply when evaluating …
What product metric design principles would you apply when evaluating the long-term success of a patient onboarding flow?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
A key user retention metric has suddenly dropped by fifteen percent ov…
A key user retention metric has suddenly dropped by fifteen percent over the last two weeks. Walk me through how you would diagnose this drop.
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?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design a product metric to measure customer engagement w…
How would you design a product metric to measure customer engagement with our continuous glucose monitoring companion app?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design an A/B test for a new algorithmic alert feature, …
How would you design an A/B test for a new algorithmic alert feature, and how do you ensure statistical significance?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Name the guardrails that would stop a launch even on a positive primary result.
- Say whether units interfere with each other, and switch design if they do.
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?
How do you determine the required sample size for an experiment with l…
How do you determine the required sample size for an experiment with low baseline conversion rates?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- 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.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
How would you decide whether to launch a new notification feature aime…
How would you decide whether to launch a new notification feature aimed at helping users maintain stable glucose ranges?
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?
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?
Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and set a baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
- Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
- Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.
Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One query pattern, written three times
- Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
- On the third attempt, write the grain of every CTE as a comment before writing its body.
- Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.
Deliverable: Three independent versions of the same query plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Only the statistics you will be asked to defend
- Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
- Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
- Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.
Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04One case, and the assumptions holding it up
- Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
- Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
- Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.
Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
- Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in a longer weekend block
- Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
- Re-read only your own notes from this week, and open no new material.
- Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.
How do you handle situations where your sample ratio mismatch threaten…
How do you handle situations where your sample ratio mismatch threatens the validity of your A/B test?
Approach
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Scope a one-line request for a readmission rate
A clinical operations lead messages you: 'What is our readmission rate? The committee meets Friday.' You have encounter (encounter_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, index_encounter_id) and member_enrollment coverage spans. Do not open a query editor yet. Deliverable: the three to five questions you send back, ranked by how much the answer moves the number; the default specification you will build if nobody replies by Wednesday; and the one line you will place under the figure so the committee does not read it against a published benchmark.
Approach
- Read the probe: this tests whether you convert ambiguity into a specification without either stalling or guessing silently. Both failure modes are common, and the silent guess is worse because nobody can see it.
- Rank your questions by leverage on the number rather than by curiosity. Whether the numerator is all-cause or unplanned, and whether the clock starts at discharge_ts, move the result far more than a tie-break rule on same-day returns.
- Ask about the comparison first. A number going to a committee will be compared to something, and whether that something is last quarter, another panel, or a national figure decides whether you owe them a raw rate, a risk-adjusted rate, or an observed-over-expected ratio.
- Commit to a default in writing so silence does not block you: unplanned acute inpatient readmission within 30 days of discharge_ts, index stays excluding planned admissions, acute-to-acute transfers, discharges against medical advice, and dispositions of expired or hospice, denominator of eligible index discharges, paid-through date stated.
- Write the caveat as a restriction on use rather than a hedge: name the one comparison the figure supports and the one it does not.
Follow-up
- They reply that they want it 'the way the benchmark does it'. What do you ask next?
- The committee wants it split by attending provider. What changes in the specification, and what do you refuse to show?
- How does your answer change if the meeting is tomorrow rather than Friday?
Sequence three urgent requests with one analyst-week available
Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.
Approach
- The probe is whether you prioritise on consequence and reversibility rather than on who asked loudest or most recently.
- Classify each request by what happens if it slips. A regulatory or contractual deadline is irreversible on its date, a drifting model is causing harm every day it keeps running, and a portfolio review can absorb a provisional number. That ordering is defensible to all three requesters because it does not depend on your preferences.
- Take the deadline-bound work first, but scope it to the minimum defensible output, because completion factors feeding a filing carry a different error tolerance than a slide.
- Do not let the drifting model simply wait. Quantify the harm cheaply by comparing calibration in the affected region against the rest, and if it is materially miscalibrated propose flagging or suppressing its output for that region within the hour rather than at the end of a refresh.
- Give each deprioritised requester something real: a provisional forecast with its uncertainty and a refresh date, or a diagnostic that tells them whether their problem is urgent. Say no explicitly with a date rather than going quiet, because silence is what produces escalation.
Follow-up
- The programme owner escalates to your manager. What do you want your manager to be able to say?
- Midweek the actuarial work needs two more full days than you estimated. What gives?
- How does your answer change if the drifting model drives a clinical outreach list rather than a report?
- 01
How do you handle situations where your sample ratio mismatch threatens the validity of your A/B test?
- 02
A clinical operations lead messages you: 'What is our readmission rate? The committee meets Friday.' You have encounter (encounter_id, admit_ts, discharge_ts, encounter_type, admission_type, discharge_disposition, drg_code, index_encounter_id) and member_enrollment coverage spans. Do not open a query editor yet. Deliverable: the three to five questions you send back, ranked by how much the answer moves the number; the default specification you will build if nobody replies by Wednesday; and the one line you will place under the figure so the committee does not read it against a published benchmark.
- 03
Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.
Is this an official Dexcom interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Dexcom. Rounds and questions reflect what candidates have reported, not a process Dexcom 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 for a Data Scientist at Dexcom?
The interview process is moderately rigorous, balancing technical coding and experimentation fundamentals with product sense and behavioral evaluation. While technical rounds test your core analytical skills, interviewers place an equally high value on your structured thinking and communication abilities.
PracHub interview research ↗What is the typical timeline from initial recruiter screen to a final offer?
The timeline typically spans between three to five weeks, depending on interview scheduling availability and team responsiveness. Maintaining prompt communication with your recruiter helps keep the process moving efficiently across multiple rounds.
PracHub interview research ↗Are remote or hybrid work arrangements available for this role?
Many data science roles are anchored around key hub locations such as San Diego, CA, with specific hybrid workplace expectations set by individual teams. Review your specific job posting and discuss location flexibility with your recruiter early in the screening stage.
PracHub interview research ↗What differentiates successful candidates from those who are not selected?
Successful candidates distinguish themselves by connecting technical solutions directly to user and business impact rather than getting lost in pure methodology. They demonstrate structured problem-solving frameworks, clear communication, and a genuine passion for digital health innovation.
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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