A Data Scientist at Omada Health plays a critical role in shaping the future of digital healthcare. By leveraging clinical, behavioral, and operational data, you will directly influence how the company delivers personalized care and improves patient outcomes. The data science team is responsible for building the predictive engines and analytical frameworks that power Omada Health's digital care programs, which target chronic conditions such as diabetes, hypertension, and musculoskeletal issues.
In this role, your work will cross multiple domains, from clinical efficacy to product optimization and operational efficiency. You will not just build models; you will design experiments, apply causal inference, and develop forecasting algorithms that ensure coaches are matched with patients effectively and clinical interventions are delivered at the optimal moment. The scale and complexity of managing diverse patient populations make this position both intellectually challenging and highly impactful.
Ultimately, a Data Scientist at Omada Health acts as a bridge between complex data systems and real-world health outcomes. Successful candidates are those who are inspired by the company's clinical mission, possess deep technical capabilities, and can communicate advanced statistical concepts to cross-functional stakeholders in product, clinical, and operations teams.
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 Conversation
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
Take-Home Assignment
reportedYour submission is read asynchronously by someone who cannot ask you a clarifying question, and who will usually skim it before reading it properly. That changes what good looks like. The conclusion belongs near the top with the supporting analysis beneath it, and the code should run start to finish on a clean machine without a manual step you forgot to document. A reviewer forced to reconstruct your reasoning from the order of notebook cells is already discounting the work. The submissions that land are the ones where a busy reader gets the answer immediately and can verify it if they want to.
What to demonstrate
- Whether the answer arrives before the methodology, so a reader who stops after the first page still has the recommendation
- Whether the code runs end to end from the submitted files, with dependencies and data paths declared rather than assumed
- Whether each chart is legible on its own, carrying axis labels and units, and exists to support a claim made in the text
How to prepare
- Restructure a past analysis so the opening paragraph holds the recommendation and the number behind it, then check that nothing later in the document quietly contradicts it
- Copy your own submission into an empty directory, run it in a clean environment, and fix everything that breaks; hidden local state is caught here or by the reviewer
- For every chart, write the one sentence it is meant to prove, and delete the chart if you cannot write that sentence
Virtual Onsite 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.
Rates built on member counts rather than exposure
Members join and leave mid-period, so dividing events by distinct members mixes a person covered for 30 days with one covered for 365. New joiners also have artificially low observed utilisation because their claims have not arrived yet and because care takes time to initiate. Denominators must be member-months or member-years, and comparative quality measures usually need a continuous-enrolment requirement with an explicit allowable gap, stated in days.
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.
Explaining an aggregate move without decomposing the mix shift
Split the change in the aggregate into within-segment movement and movement in segment weights before you explain it. Every segment's rate can fall while the overall rate rises, purely because volume shifted toward segments that already had higher rates.
Stopping an experiment the moment it crosses significance
Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you evaluate the performance of a predictive model when the cos…
How do you evaluate the performance of a predictive model when the cost of a false positive is significantly different from a false negative?
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
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Describe a machine learning model you built from scratch in a previous…
Describe a machine learning model you built from scratch in a previous project, including how you handled data preprocessing and feature engineering.
Approach
- Say how the offline result would be validated online before it is trusted.
- 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
- 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?
Expand overlapping coverage spans into fractional member-months
member_enrollment has span_id, member_id, plan_id, product_type, effective_date, termination_date (NULL while active). Both dates are inclusive. Write a function returning one row per calendar month of a stated year with total member-months, where a member contributes covered days in the month divided by days in that month, capped at one month per member even when two spans overlap after a plan change or a retroactive span. Do not expand to one row per member-day: the sample has 400,000 members. Output: month, member_months.
Approach
- Resolve NULL termination_date to the reporting end date and say so in a comment. An open span is not an infinite span, and clipping it at the year end is what keeps the denominator finite and auditable.
- Merge overlapping and adjacent spans per member before touching months. Sort by member_id and effective_date, carry a running maximum of the end date, and open a new merged group when effective_date exceeds that running max plus one day. Adjacent means a gap of zero days, which a plan change produces constantly.
- Cross join the merged spans to the 12 month boundaries rather than to days. Overlap days = (min(span_end, month_end) - max(span_start, month_start)).days + 1, clipped below at 0. That is at most 12 rows per span instead of 365.
- Divide overlap days by the number of days in that month, so February and July are weighted correctly, then sum by month.
- Validate on a constructed member before trusting the aggregate: a single span covering the whole year must sum to exactly 12.0.
Worked solution 30 min
- Fill termination_date nulls with the reporting end date and clip all spans to the reporting year.
- Sort by member_id, effective_date, then compute a running max end per member with cummax shifted by one, flag a new group where effective_date > prior_max_end + 1 day, and cumsum the flag to get merged group ids.
- Aggregate each group to min start and max end, producing disjoint spans per member.
- Cross join merged spans to a 12-row month frame, compute clipped overlap days, divide by days in month.
- Group by month and sum.
Follow-up
- Product type changes mid-year. The metric must be reported by product_type. Where does the cap now apply and what breaks?
- An eligibility file arrives with a retroactive termination that shortens a span you already reported on. How do you restate?
- Why member-months rather than distinct members, in one sentence, for a director who wants the simpler number?
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?
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.
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?
What metrics would you define to measure the success of a new digital …
What metrics would you define to measure the success of a new digital health intervention?
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.
- State what result would change your recommendation, so the answer is falsifiable.
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 forecast coach capacity and patient demand to ensure opt…
How would you forecast coach capacity and patient demand to ensure optimal coach-to-patient ratios?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- 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?
If patient engagement drops suddenly after a mobile app update, how wo…
If patient engagement drops suddenly after a mobile app update, how would you investigate the root cause?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
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?
How would you measure the long-term clinical efficacy of a program usi…
How would you measure the long-term clinical efficacy of a program using short-term engagement metrics?
Approach
- 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.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
How would you design an experiment to test a new coaching feature when…
How would you design an experiment to test a new coaching feature when a randomized control trial is not clinically or operationally feasible?
Approach
- 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.
- 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?
- How would you handle interference between treated and control units?
Can you explain the difference between propensity score matching and r…
Can you explain the difference between propensity score matching and randomized A/B testing?
Approach
- Say whether units interfere with each other, and switch design if they do.
- 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
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
Size a clinic-randomised trial when the intracluster correlation binds
A care-coordination change is delivered at the clinic level and cannot be withheld from some members of a participating clinic. Forty-eight clinics are available for 1:1 randomisation. The primary outcome is the 90-day primary care contact rate among newly enrolled members, baseline 0.62, with a target of plus 5 percentage points. Each clinic contributes about 60 eligible members a year, and the intracluster correlation on this outcome is 0.02. Two-sided alpha 0.05, 80 percent power. Give clinics required per arm, say whether 48 suffices, and what you would do.
Approach
- Compute the individually randomised sample size first, then inflate it by the design effect 1 + (m - 1) x ICC, where m is the average cluster size. The design effect is the price of correlation within clinics and is the single largest term in this calculation.
- Convert the inflated individual N into clusters by dividing by m, and compare with the 24 clinics available per arm.
- Check the ceiling before proposing a longer accrual window. As m grows, effective members per cluster approaches 1 / ICC, here 50, so 24 clinics can never contribute more than 1,200 effective members per arm no matter how many members each one enrols. If the required effective N exceeds that, no amount of accrual rescues the parallel design.
- Propose the designs that actually change the arithmetic: a stepped wedge in which every clinic crosses over so each contributes a within-cluster comparison, or a smaller randomisation unit (provider pod) if the intervention can be delivered at that level without contamination.
- Add the two refinements a reviewer will ask for: use a t-distribution with roughly 2k - 2 degrees of freedom rather than z when k is small (here about 46 df, which raises N by about 4 percent), and use covariate-constrained randomisation on baseline contact rate so 48 clinics do not land badly imbalanced by chance.
Worked solution 30 min
- Individual n per arm = 7.849 x (0.62 x 0.38 + 0.67 x 0.33) / 0.05^2 = 7.849 x 0.4567 / 0.0025 = 1,434.
- Design effect = 1 + (60 - 1) x 0.02 = 2.18.
- Inflated n per arm = 1,434 x 2.18 = 3,126 members, so clusters per arm = 3,126 / 60 = 52.1, round up to 53.
- Compare with 24 clinics available per arm: the study is short by more than a factor of two.
- Compute the ceiling: 1 / 0.02 = 50 effective members per clinic, so 24 clinics give at most 1,200 effective members per arm against the 1,434 required.
Follow-up
- Sketch the stepped-wedge analysis model. What does a secular trend do to the estimate if you leave period effects out?
- The ICC was estimated from a prior year at 0.02 with a wide interval. How do you size under ICC uncertainty?
- Six clinics withdraw after randomisation. What does that do to power compared with losing the same number of members?
Allowed PMPM fell fourteen percent in three months
Allowed PMPM in the monthly series fell from $412 to $354 over the three most recent incurred months, and leadership wants to announce the saving. You have medical_claim_line (allowed_amount, service_start_date, received_date, paid_date, claim_id, claim_version, frequency_code, place_of_service_code), pharmacy_claim (allowed_amount, fill_date, reversal_flag) and member_enrollment coverage spans. The chart carries no paid-through date. Work out how much of the fall survives completion, and hand back a restated series with an explicit paid-through date and the completion factors you applied.
Approach
- Get the paid-through date first and put it on the chart. Without it the series is uninterpretable, because every incurred month is a different age.
- Build a lag triangle: for each incurred month, cumulative allowed_amount by the number of months between service_start_date and paid_date, on surviving claim versions only (drop frequency_code 8 voids and keep the highest claim_version per claim_id).
- Estimate development factors separately for pharmacy fills, professional lines and inpatient facility lines. Pharmacy adjudicates within days, professional within weeks, inpatient facility over months, so one blended factor understates the correction on the newest month and overstates it on the oldest.
- Gross each of the last three incurred months up by the reciprocal of its cumulative development factor, using factors fitted on months that have already fully run out.
- Rebuild the denominator independently as member-months from coverage spans. Member-months are complete on day one, so if the denominator also shows a lag pattern the enrollment file is late, which is a different bug with a different fix.
- Restate the series, mark the last three months as estimated, and report the residual movement after completion as the only part worth investigating.
Follow-up
- The completed series still shows a three percent fall. What do you look at next, and in what order?
- How would you detect that the fall is a mix shift toward cheaper services rather than lower volume?
- A contract settles on this number at a fixed paid-through date. What do you owe the other party about the estimate you just made?
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 ↗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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
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.
Explain an incomplete cost series to a non-technical executive
An executive opens a dashboard showing risk-adjusted allowed PMPM down 6 percent across the last three incurred months and asks whether the cost initiative is working. You know pharmacy adjudicates within days, professional claims within weeks, and inpatient facility claims over months, so those three months are both undercounted and mix-shifted toward cheap services. There is also a genuine movement of roughly 1.5 percent in the older, complete months. Deliverable: a two-minute spoken answer, the change you make to the chart, and what you commit to telling them and when.
Approach
- The probe is whether you can be honest about uncertainty without sounding evasive. Executives read hedging as not knowing, so the answer has to end in a commitment.
- Separate the two claims immediately. The 6 percent is an artefact of incomplete data, and there is a smaller real movement in the months that are complete. Delivering only the debunk leaves them holding nothing.
- Explain the lag in their terms: the bills for the most expensive care arrive last, so an incomplete month always looks cheap and always looks like it is improving. Keep the phrase completion factor out of the first pass.
- Fix the chart instead of explaining around it. Shade or omit the incomplete months, print the paid-through date on the axis, and overlay the same series as it looked at equivalent maturity a year earlier so the shape is comparable rather than merely lower.
- Close with a date and a magnitude. Commit to a restated figure once runout matures, say roughly how far you expect the 6 percent to shrink, and name what you would need to see to call the initiative working.
Follow-up
- They ask for your best guess today, knowing it is provisional. What do you say?
- The restated number comes back at 1 percent. How do you handle having flagged the 6 percent at all?
- How do you stop this dashboard producing the same conversation next quarter?
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
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.
- 02
An executive opens a dashboard showing risk-adjusted allowed PMPM down 6 percent across the last three incurred months and asks whether the cost initiative is working. You know pharmacy adjudicates within days, professional claims within weeks, and inpatient facility claims over months, so those three months are both undercounted and mix-shifted toward cheap services. There is also a genuine movement of roughly 1.5 percent in the older, complete months. Deliverable: a two-minute spoken answer, the change you make to the chart, and what you commit to telling them and when.
- 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 Omada Health interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Omada Health. Rounds and questions reflect what candidates have reported, not a process Omada Health has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the target time commitment for the take-home assignment?
Omada Health recommends spending approximately 5 to 7 hours on the take-home challenge. You are given a 48-hour window to complete and return the assignment, allowing you to manage your time flexibly.
PracHub interview research ↗Do I need a background in healthcare or clinical research to apply?
While prior experience with healthcare data is a nice-to-have, it is not a strict requirement. The hiring team values strong statistical foundations, clean coding practices, and a willingness to learn the clinical nuances of the business.
PracHub interview research ↗How technical is the hiring manager interview?
The hiring manager screen is highly technical. It typically includes a deep dive into a past project from your resume, where you will be asked to explain your modeling choices, and a case study focused on experimentation and causal inference.
PracHub interview research ↗What is the culture like on the data science team?
The team is highly collaborative, supportive, and mission-driven. Team members regularly share feedback, participate in peer reviews, and work closely with cross-functional partners to solve meaningful healthcare challenges.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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