A Data Scientist at Médecins Sans Frontières plays a pivotal role in harnessing data to drive evidence-based decision-making and improve the effectiveness of humanitarian aid. This position is critical as it combines advanced analytical skills with an understanding of healthcare needs in crisis contexts. By analyzing complex datasets, you will help inform strategies that enhance operational efficiency and ensure the organization can respond effectively to emergencies.
In this role, you will work closely with teams across various domains, including medical operations, logistics, and public health. Your insights will directly impact how resources are allocated, enabling Médecins Sans Frontières to maximize their outreach and efficacy. The complexity of the datasets you will encounter, ranging from patient health records to logistical supply chain data, adds a layer of intrigue and challenge to this position. You will contribute to meaningful projects that save lives and improve health outcomes in some of the world’s most vulnerable communities.
Initial Screening Interview
reportedData Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.
What to demonstrate
- Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
- Whether you name what you have not done instead of stretching to cover every line of the posting
- Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage
How to prepare
- Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
- Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
- Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
Hiring Manager Interview
reportedMuch of this round runs on your own history, but the manager is not collecting a project list. They are working out what it is like when something goes wrong on your watch: how late the bad news tends to arrive, and whether a number you hand over has been checked by anyone including you. That is why the strongest material is a project where you can describe the part that did not work and what it cost. A result you cannot take full responsibility for, however clean, gives them nothing to trust you with afterwards.
What to demonstrate
- Whether you volunteer the limits of a result you are proud of, or wait to be pushed onto them
- How errors surfaced in your past work, and whether you or somebody else found them
- Whether the scope you claim matches the level of detail you can still produce about it
- What you did the first time a stakeholder acted on something of yours that turned out to be wrong
How to prepare
- Rebuild one headline figure from memory down to the join and the filter, so a question about the denominator does not stall the conversation
- For each project you raise, write the sentence you would say to someone who had already acted on a number that later turned out wrong
- Mark which parts of a project were yours and which belonged to other people, and state that boundary yourself before anyone asks
Technical Assessment
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
PracHub editorial advice for the preparation topics above.
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.
Reading the most recent months of a claims-based series as real
Claims incur before they are reported and paid, so recent incurred months are systematically undercounted until runout completes. The lag is not uniform: pharmacy adjudicates in days, professional claims in weeks, inpatient facility claims in months. That means recent data is both too low and mix-shifted toward cheap services, which reads as a cost improvement and a utilisation drop at once. The fix is to hold the last three incurred months back or apply completion factors, and to state the paid-through date on every chart.
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.
Answering a product-sense question with a list of features
Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function in R that calculates the mean and standard deviation …
Write a function in R that calculates the mean and standard deviation of a given numeric vector.
Approach
- Say what the estimate is of, and over what population it generalises.
- Translate the result into the decision it informs, in one plain sentence.
- 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?
Describe how you would approach building a predictive model for resour…
Describe how you would approach building a predictive model for resource allocation in emergency situations.
Approach
- Set a baseline first, so any model has something honest to beat.
- 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?
Four data-quality rules over corrected and cancelled lab results
lab_result has result_id, order_id, patient_id, loinc_code, value_numeric, value_text, units, reference_low, reference_high, specimen_collected_ts, resulted_ts, result_status (preliminary, final, corrected, cancelled), supersedes_result_id. Return one row per rule with a failing count, a failing share, and one example result_id, for these rules: more than one surviving row per (order_id, loinc_code) after resolving corrections and dropping cancelled; resulted_ts earlier than specimen_collected_ts; a numeric result whose unit is not the modal unit for its loinc_code; value_numeric and value_text both null on a row with result_status 'final'.
Approach
- Resolve the correction chain before anything else. Drop result_status 'cancelled', then within each (order_id, loinc_code) keep the row whose result_id appears in no other row's supersedes_result_id. That terminal node is the surviving value and the rule holds even if a correction is loaded with a backdated timestamp.
- Build each rule as a boolean mask over a named frame rather than as a filtered copy, so the failing share has an explicit, per-rule denominator. Rule three is eligible only on rows with a non-null value_numeric; rule four only on final rows.
- For the unit rule, derive the modal unit per loinc_code from the data instead of hardcoding an expected unit. The failure being hunted is one analyte reported in two units whose values differ by a fixed factor, which no range check catches because both sets of numbers look plausible.
- Emit a tidy frame with columns rule, eligible_rows, failing_rows, failing_share, example_result_id. A printed report cannot be diffed between runs; a frame can be stored and alerted on.
- Order the output by failing_share descending so the run has a lead finding rather than four equal-weight lines.
Worked solution 25 min
- Drop cancelled rows, then compute the set of result_ids referenced by supersedes_result_id and keep rows not in that set.
- Rule one: group the survivors by (order_id, loinc_code) and count rows above 1.
- Rule two: mask on resulted_ts < specimen_collected_ts over rows where both timestamps are present.
- Rule three: compute the mode of units per loinc_code over rows with non-null value_numeric, then mask rows whose unit differs from it.
- Rule four: mask rows with result_status 'final' and both value columns null. Assemble the four results into one frame with counts, shares and an example id.
Follow-up
- The modal-unit rule fires on 8 percent of one analyte. How do you decide whether that is a unit-conversion bug or a second legitimate assay?
- What would you add to catch a value that is inside its reference range but physiologically impossible?
- Which of these four rules should block a downstream pipeline and which should only alert, and why?
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.
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?
Reconstruct drug coverage intervals and compute proportion of days covered
pharmacy_claim holds fill_id, member_id, ndc_code, therapeutic_class_code, fill_date, days_supply, reversal_flag and reversed_fill_id. Early refills overlap, so days_supply must be laid end to end: each fill's coverage starts at the later of its fill_date and the previous interval's end. Both rows of a reversed pair are excluded. For one therapeutic_class_code over 2025-01-01 to 2025-06-30, return per member the proportion of days covered, defined as covered days divided by days from first fill_date to window end, plus the share of members at or above 0.80. Require at least two fills and 91 days of follow-up.
Approach
- Clean the fills first. Drop rows with reversal_flag TRUE and also the fills they point at through reversed_fill_id. Dropping only the reversal row leaves an original fill counted as exposure that never reached the member. Use NOT EXISTS for that second step, since reversed_fill_id is nullable and NOT IN would return nothing.
- Shift, never stack. The shifted end is covered_end_i = GREATEST(fill_date_i, covered_end_{i-1}) + days_supply_i, which looks recursive but has a closed form: with cum_i the running sum of days_supply through fill i, covered_end_i = cum_i + MAX over j <= i of (fill_date_j - cum_{j-1}). That is SUM(days_supply) OVER (ORDER BY fill_date ROWS UNBOUNDED PRECEDING) plus a running MAX of an anchor column, so it is two window functions and no recursive CTE.
- Derive covered_start_i as GREATEST(fill_date_i, LAG(covered_end)). The intervals are disjoint by construction, so covered days is a plain sum of lengths and never needs a distinct day grid.
- Clip intervals to the window end before summing, and decide explicitly whether to clip at disenrollment as well. Supply that runs past the window must not inflate the numerator.
- Use the denominator the spec names, first fill_date to window end, and hold it constant across classes. A fixed-window denominator yields a different and usually lower number, and mixing the two across classes makes the comparison meaningless.
- Apply the inclusion rules at member level after the intervals are built, then compute the share at or above 0.80 with the qualifying member count beside it.
Worked solution 40 min
- Filter to the therapeutic_class_code and window, then remove reversal rows and their originals with NOT EXISTS.
- Per member ordered by fill_date, compute cum_supply and anchor = fill_date - (cum_supply - days_supply), then covered_end = cum_supply + MAX(anchor) OVER (ORDER BY fill_date ROWS UNBOUNDED PRECEDING).
- Compute covered_start = GREATEST(fill_date, LAG(covered_end) OVER (ORDER BY fill_date)), clip both ends to the window, and drop intervals that collapse.
- Sum interval lengths as covered_days, compute follow_up_days from first fill_date to window end, and divide.
- Apply the two-fill and 91-day rules, then aggregate the share at or above 0.80.
Follow-up
- A member is hospitalised for twelve days mid-window, when the facility supplies medication. Should those days count as covered, and what do published adherence measures do about them?
- Someone wants to classify adherence over twelve months and then compare mortality from month zero. What is wrong with that design and what fixes it?
- Two different ndc_codes in the same therapeutic class overlap. Is that stacking or switching, and how does your interval logic treat each case?
Given a dataset on patient outcomes, how would you identify trends and…
Given a dataset on patient outcomes, how would you identify trends and anomalies?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you optimize a slow-running data processing script in R?
How would you optimize a slow-running data processing script in R?
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
- 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 prioritize multiple data requests from various teams?
How would you prioritize multiple data requests from various teams?
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.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
What statistical methods do you commonly employ to validate your model…
What statistical methods do you commonly employ to validate your models?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer would contain.
- 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?
Score trial sites without rewarding enrollment that does not stick
A clinical operations group ranks sites on subjects randomized per activated site-month, using study_subject_visit (study_id, site_id, subject_id, arm_code, randomization_ts, site_activation_date, visit_name, visit_number, visit_status, primary_endpoint_value, protocol_deviation_flag, deviation_severity). Bottom-ranked sites lose allocation on the next study. Define the primary metric, two guardrails, the handling of sites activated too recently to rank, and the estimator you rank on. Then state what a site under allocation pressure does to the number, and which guardrail sees it.
Approach
- Move the numerator from randomized to randomized and retained through the protocol-defined primary endpoint visit. A subject who randomizes and withdraws consumes screening, drug supply and monitoring while contributing nothing to the analysis population, so counting them rewards the wrong behaviour precisely when a site is under pressure.
- Build the denominator as exposure, not sites: site-months from site_activation_date to the earlier of period end and database lock, per study. Ranking on a raw count instead of a rate confuses a large site with a fast one.
- Handle young sites by reporting rather than dropping. Sites with fewer than a stated minimum exposure are shown in an insufficient-exposure band, because excluding them is how a non-enrolling tail disappears from a scorecard, and the non-enrolling tail is usually the larger operational problem.
- Rank on a shrunken estimate, not the raw ratio. With a few site-months of exposure the ratio has enormous variance, and a site with two site-months and one randomization will top an unshrunken table. Partial pooling toward the study mean, or a simple empirical-Bayes posterior mean, prevents rank being an artefact of small exposure.
- Pick guardrails that read the two pressure responses. A site that loosens eligibility judgement shows an implausibly low screen-failure rate, computed as subjects with randomization_ts NULL and visit_status screen_failed over subjects screened. A site that pushes visits to hit counts shows up in major protocol deviations per 100 completed visits, reported separately from minor ones because a single major deviation can remove a subject from the per-protocol set.
- Report the companion the median hides: the share of activated sites with no randomization by day 90. A median computed only over sites that eventually randomized is silent about the sites that never did.
Worked solution 30 min
- Define the numerator as subjects with non-null randomization_ts whose primary endpoint visit reaches visit_status completed with a non-null primary_endpoint_value, per study and site.
- Compute site-months of exposure from site_activation_date to period end or database lock, and set the minimum exposure below which a site is banded rather than ranked.
- Compute the raw rate, then the shrunken estimate by partial pooling toward the study mean, and print both columns side by side so the shrinkage is visible rather than hidden.
- Specify the two guardrails with numerator, denominator and window, keeping major and minor deviations as separate series.
- Add the no-randomization-by-day-90 share across all activated sites, so the non-enrolling tail appears on the same page as the ranking.
Follow-up
- A site has the best randomized-and-retained rate and the worst major deviation rate. What do you recommend, and what would change your answer?
- Your shrunken ranking moves a site from 3rd to 14th. How do you explain shrinkage to the site's operations lead in two sentences?
- Primary endpoint retention takes 18 months to observe. What do you rank on in the meantime, and what bias does that interim choice introduce?
Glycemic control share jumped when a feed connected
The share of members with a controlled glycemic result moved from 0.62 to 0.81 the month a second result feed was connected. lab_result carries result_id, order_id, patient_id, encounter_id, loinc_code, analyte_name, value_numeric, units, reference_low, reference_high, abnormal_flag, result_status and supersedes_result_id. The metric is members whose latest result in the year is below the control threshold, divided by members with any result in the year. Decide whether control actually improved, and specify the metric so results from both feeds can be pooled.
Approach
- Split numerator and denominator. The denominator is members with any result, so a feed that tests more people changes the metric without changing anyone's health. Count denominator members before and after and see how much of the 19 point move is denominator growth.
- Recompute on a fixed cohort: members who had a qualifying result in both the before and after periods. If the share is flat on the fixed cohort, the movement is who got tested, not what their results were.
- Deduplicate to the latest non-cancelled row per (order_id, loinc_code), respecting supersedes_result_id, because a corrected result arrives as a new row and counting both gives one person two values.
- Audit units and loinc_code mix by feed. Two unit conventions coexist for this analyte, percent and mmol/mol, related by mmol/mol equal to 10.929 times (percent minus 2.15), so 7.0 percent is 53 mmol/mol. Comparing mmol/mol values to a percent threshold would classify them all as uncontrolled and depress the share, so a rise cannot be explained by that mismatch, which rules it out rather than in.
- State plainly that the tested population is not the covered population. Testing is ordered when someone suspects a problem or when a site runs standing orders, so the mean among tested members is not the population mean and cannot be made into one by imputation.
- Respecify: numerator and denominator both restricted to members with a stated continuous-enrolment requirement, one deduplication rule, one unit after conversion, one threshold, and a separately reported testing rate so the denominator's movement is visible.
Follow-up
- Someone proposes imputing the mean result for untested members. What happens to the estimate and why?
- A model you are building gains AUC from a feature that marks whether the test exists. Why is that a warning rather than a win?
- How would you report this metric so a clinical committee can see improvement separately from coverage of testing?
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 ↗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 ↗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 ↗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 ↗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.
A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.
How do you ensure that your work aligns with the mission and values of…
How do you ensure that your work aligns with the mission and values of Médecins Sans Frontières?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Quantify the outcome, including what you would not claim credit for.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Describe a situation where you had to communicate complex data insight…
Describe a situation where you had to communicate complex data insights to a non-technical audience.
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
- 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?
Own an analysis that shipped wrong and was acted on
Describe an analysis you delivered that was wrong, where someone acted on it before the error surfaced. Pick something with a mechanism you can name, not a typo. Cover what the number claimed, what was actually true, how much time passed before it was caught, who caught it, and what you told the people who had already acted. Deliverable: a five-minute account that ends with the specific control you added afterwards, plus one occasion since when that control has fired.
Approach
- Read the probe: the interviewer is testing whether you detect your own errors and whether the correction was structural. Pick an error with a nameable mechanism (a denominator built from distinct members instead of member-months, a series read before claims runout completed, a cohort selected on the outcome) so the story has a diagnosis rather than a mood.
- Open with the decision that was made on your number, not with the bug. The severity of an analytic error is measured in the action it caused: a rate filing, a staffing plan, a programme expansion.
- State the counterfactual number. 'The true figure was 14.1 admissions per 1,000 member-years, not 9.8' is a fact; 'it was off by a lot' is not.
- Describe the detection path honestly, including whether you found it or somebody else did. Say how long it took, because time to detection is the part you actually control next time.
- Close on the control, and make it something that protects a reader who has never met you: a unit test on the denominator, a paid-through date stamped on every chart, a required exclusions block in the spec template. Name one later date it caught something.
Follow-up
- What would have had to be true for you to catch this before delivery rather than after?
- Did the stakeholder change how much they trust your work, and did you want them to?
- Give an example of a control you added that turned out to be the wrong control.
- 01
How do you ensure that your work aligns with the mission and values of Médecins Sans Frontières?
- 02
Describe a situation where you had to communicate complex data insights to a non-technical audience.
- 03
Describe an analysis you delivered that was wrong, where someone acted on it before the error surfaced. Pick something with a mechanism you can name, not a typo. Cover what the number claimed, what was actually true, how much time passed before it was caught, who caught it, and what you told the people who had already acted. Deliverable: a five-minute account that ends with the specific control you added afterwards, plus one occasion since when that control has fired.
Is this an official Médecins Sans Frontières interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Médecins Sans Frontières. Rounds and questions reflect what candidates have reported, not a process Médecins Sans Frontières has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The interview process may take several weeks, generally ranging from two to four weeks, depending on the number of candidates and the scheduling of interviews.
PracHub interview research ↗How much preparation time is typical?
Candidates often spend 1-2 weeks preparing for interviews, focusing on both technical skills and understanding the organization's mission.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates not only demonstrate technical proficiency but also show a strong alignment with the humanitarian values of Médecins Sans Frontières.
PracHub interview research ↗What is the culture and working style at Médecins Sans Frontières?
The culture emphasizes collaboration, flexibility, and a commitment to making a positive impact in challenging environments.
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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