As a Data Scientist at Otsuka, you will play a pivotal role in harnessing data to inform decision-making and drive innovation across the organization. This position is critical not only for optimizing internal processes but also for enhancing the effectiveness of our products, thereby improving patient outcomes. You will work on complex datasets to uncover insights that influence product development and strategic direction, making your contributions vital to the success of our mission in the pharmaceutical industry.
In this role, you will engage with cross-functional teams to address real-world challenges, leveraging your analytical skills to provide actionable recommendations. You may work on projects related to clinical trial data analysis, market research, or operational efficiency, all of which contribute significantly to Otsuka’s goal of creating meaningful advances in healthcare. Expect to be part of a dynamic environment where you can impact diverse areas, from drug discovery to patient engagement.
Phone Screening
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 Interviews
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
Behavioral Interviews
reportedBehavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.
What to demonstrate
- Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
- Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
- Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier
How to prepare
- For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
- Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
- Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
Final Evaluation
reportedWhere a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.
What to demonstrate
- Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
- Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
- Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
- Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered
How to prepare
- Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
- Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
- Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one
PracHub editorial advice for the preparation topics above.
Treating clinical measurements as missing at random
A lab result, a vital sign, or a screening exists because someone ordered it, and ordering tracks suspicion of disease, visit frequency, and site workflow. Imputing the mean or dropping incomplete rows biases the population estimate and can flip the sign of an association, because the untested are systematically healthier or systematically disengaged. The presence indicator is often more predictive than the value, which is a warning sign rather than a feature win: a model that learns test ordering will not transfer to a site with different protocols.
Immortal time in adherence, treatment, and enrolment definitions
Classifying members as adherent, treated, or programme-enrolled requires them to survive and stay covered long enough to accumulate the defining events. That guaranteed event-free interval is assigned to the exposed group, so the exposure looks protective for reasons that have nothing to do with the treatment. Adherence studies are the classic case: measuring 12-month proportion of days covered and then comparing mortality builds survival into the exposure definition. Use time-varying exposure or a landmark analysis with the classification window excluded from follow-up.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Accepting a metric definition without asking about the denominator
Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
What is the time complexity of your preferred sorting algorithm?
What is the time complexity of your preferred sorting algorithm?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Say how the offline result would be validated online before it is trusted.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- What would you monitor after launch to know the model is still valid?
Proportion of days covered with shifted, truncated refill intervals
pharmacy_claim has fill_id, member_id, therapeutic_class_code, fill_date, days_supply, reversal_flag, reversed_fill_id. For one therapeutic class and a fixed 12-month window, compute proportion of days covered per member: distinct days on which a dispensed days_supply covers the day, divided by days from the member's first in-window fill through the window end. An early refill shifts coverage forward rather than stacking, and coverage is truncated at the window end. Drop both rows of every reversed pair. Return member_id, pdc, and the share at pdc >= 0.80 among members with at least 2 fills and at least 91 days of follow-up.
Approach
- Remove reversals as pairs first. Drop every row with reversal_flag true, and also drop the fill_ids those rows point at through reversed_fill_id. Dropping only the flagged row leaves a dispense that was never collected in the exposure.
- Walk fills per member in fill_date order carrying a cursor: start = max(fill_date, previous_end + 1 day), end = start + days_supply - 1. The shift is path dependent, so a plain cumsum over days_supply does not reproduce it. Use itertools.accumulate or a per-member loop over numpy arrays, not a row-wise apply over the whole frame.
- Truncate the last interval at the window end before measuring. Without truncation a 90-day fill dispensed on the final day pushes the covered-day count past the denominator and PDC above 1.0.
- Use the denominator the definition states: first in-window fill_date through window end, inclusive. Not a flat 365, and not first fill to last fill, which is a different metric that rewards early discontinuation.
- Apply the eligibility filter before computing the >= 0.80 share, and report the size of that denominator next to the share. A share without its denominator is not reviewable.
Follow-up
- How does PDC differ from medication possession ratio, and which of the two can exceed 1.0?
- A member switches to a different ingredient inside the same therapeutic class mid-window. Should the intervals chain, and what does that do to the class-level number?
- Members who die or lose coverage mid-window get a short denominator and often a high PDC. If you then compare mortality by adherence category, what bias have you built in and how do you remove it?
Build a claims lag triangle and complete recent months
medical_claim_line has claim_id, claim_version, frequency_code, service_start_date, paid_date, allowed_amount, claim_status. Using 24 fully mature incurred months, build a development triangle of allowed dollars by incurred month and payment lag in whole months, derive cumulative completion factors by chain ladder, then estimate ultimate allowed for the three most recent incurred months at a stated paid-through date. Deduplicate to surviving claim versions before building the triangle. Return incurred_month, paid_to_date, completion_factor, estimated_ultimate.
Approach
- Deduplicate to surviving claim versions and keep paid lines first. A triangle built on all versions develops on adjustment churn rather than on payment timing, and replacements arrive late, so the distortion concentrates in exactly the tail you are trying to estimate.
- Compute lag as a whole-month difference between incurred month and paid month, not as day difference divided by 30. Ragged month lengths otherwise shuffle identical claims between lag buckets depending on which month they fall in.
- Pivot to incurred_month by lag, cumulate along lag, then form age-to-age factors as the ratio of the column L+1 total to the column L total, summed over incurred months mature enough to have both. Volume weighting is the chain ladder; a simple mean of per-month ratios lets one low-volume month dominate the factor.
- Chain the age-to-age factors from each lag to ultimate and invert: completion factor at lag L is the reciprocal of the product of factors from L onward. Apply as estimated_ultimate = paid_to_date / completion_factor.
- State the assumption you have just made, which is that the development pattern is stable. It is not, after a claims-system migration, a network change or a processing backlog, and the most recent month's factor is the least reliable because it rests on the fewest observations while carrying the largest adjustment.
Worked solution 35 min
- Deduplicate to surviving versions, drop voids, keep claim_status 'paid', and derive incurred_month from service_start_date and paid_month from paid_date.
- lag = (paid_month.year - incurred_month.year) x 12 + (paid_month.month - incurred_month.month); drop negative lags and investigate them separately.
- Pivot to a triangle of summed allowed_amount, cumulate along the lag axis, and mask cells past the paid-through date so no cell contains a future payment.
- Compute volume-weighted age-to-age factors per lag over the mature rows, then chain them into cumulative completion factors.
- Apply the factor for each recent month's current maturity to its paid_to_date and return the four columns.
Follow-up
- A processing backlog means the last two months developed slower than history. What does your estimate do, and how would you detect that before reporting?
- Pharmacy, professional and inpatient facility develop on different clocks. How do you split the triangle, and what breaks if the service mix shifts?
- What happens to a PMPM series if someone reports an unadjusted recent month, and in which direction does the error point?
Describe how to handle outliers in a dataset programmatically.
Describe how to handle outliers in a dataset programmatically.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- 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 does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
How would you optimize a SQL query for better performance?
How would you optimize a SQL query for better performance?
Approach
- 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.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Deduplicate claim versions before totalling allowed amounts
medical_claim_line holds one row per service line per claim version: claim_line_id, claim_id, claim_version, frequency_code (1 original, 7 replacement, 8 void), member_id, service_start_date, procedure_code, allowed_amount, claim_status and adjudicated_at. Adjusted claims appear more than once, so summing every row double counts them. Return total allowed_amount by procedure_code for service_start_date in Q1 2025, counting only lines with claim_status 'paid' on the surviving version of each claim, which is the highest claim_version per claim_id, and dropping the claim entirely when that surviving version carries frequency_code 8. Resolve survivorship with a window function, not a self-join.
Approach
- Resolve survivorship at claim_id, not claim_line_id. Carry MAX(claim_version) OVER (PARTITION BY claim_id) alongside every line and keep lines where claim_version equals it, because a replacement version can contain a different number of lines than the original.
- Read the frequency_code of the surviving version only. A frequency_code 8 removes the claim outright; it does not revert payment to the prior version, so a void must delete the claim rather than promote version n-1.
- Apply claim_status and the date filter after survivorship is resolved. Filtering paid lines first can strip the surviving version and silently elect a superseded one.
- Group by procedure_code and sum allowed_amount, then state the paid-through date on the output because Q1 amounts keep moving until adjudication runout completes.
- Sanity check the shrinkage: report how many claim_ids and how many dollars the dedup removed, so the reviewer can see the step did something.
Worked solution 20 min
- Build a CTE that adds surviving_version = MAX(claim_version) OVER (PARTITION BY claim_id) and surviving_freq = the frequency_code of the row at that version, to every line.
- Filter to claim_version = surviving_version so all lines of the surviving version survive together.
- Drop claims whose surviving_freq is 8, then apply claim_status = 'paid' and service_start_date between 2025-01-01 and 2025-03-31.
- Group by procedure_code, sum allowed_amount, and emit the count of distinct claim_ids contributing.
- Run the naive SUM over all rows beside it and report the difference as the double count that was removed.
Follow-up
- Two rows share a claim_id and claim_version but differ on adjudicated_at. What do you do, and what does that imply about the extract?
- How does the query change if the request is allowed_amount by paid_date rather than service_start_date, and which basis does a finance reconciliation want?
- A void arrives for a claim already published in a closed month. How do you restate without rewriting history in the warehouse?
If tasked with reducing operational costs, what data would you analyze…
If tasked with reducing operational costs, what data would you analyze and why?
Approach
- 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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
Describe how you would approach a project to improve patient adherence…
Describe how you would approach a project to improve patient adherence to medication.
Approach
- Fix the population and the time window before naming any metric.
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
Given a dataset with various patient outcomes, how would you identify …
Given a dataset with various patient outcomes, how would you identify which factors are most significant?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Name one primary metric, then the guardrail that stops it being gamed.
- Fix the population and the time window before naming any metric.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
Present a case where you had to make a data-driven decision with incom…
Present a case where you had to make a data-driven decision with incomplete information.
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design an experiment to test a new drug’s efficacy?
How would you design an experiment to test a new drug’s efficacy?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- 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 conclude if the result is positive but the test is underpowered?
- What would you do if you could not randomise at all?
Explain the difference between supervised and unsupervised learning.
Explain the difference between supervised and unsupervised learning.
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Decompose risk-adjusted cost per member into owned leaf metrics
Build a metric tree, at most four levels deep, from risk-adjusted allowed PMPM down to leaves an owner can move. Data: medical_claim_line (allowed_amount, units, procedure_code, place_of_service_code, service_start_date, claim_version, frequency_code), pharmacy_claim (allowed_amount, days_supply, is_generic, formulary_tier, reversal_flag), member_enrollment (coverage spans, prospective_risk_score). Give the arithmetic identity each split rests on, the leading indicator at each leaf, and identify the one node where the arithmetic is exact but the attribution is not.
Approach
- State the base identity before drawing anything. Allowed PMPM equals total allowed over member-months, and it decomposes exactly by service category c as the sum over c of (units_c / member-months) times (allowed_c / units_c). Utilization per member-month and allowed per unit, summed across categories, so category mix is inside the sum rather than a separate hand-wave.
- Give the change decomposition too, because the tree will be read on deltas: the change in PMPM equals the sum over categories of (change in utilization times base price) plus (base utilization times change in price) plus (change in utilization times change in price). The third term is the interaction, it is not noise, and an analysis that drops it will not reconcile.
- Build the medical branch to leaves with owners: inpatient admits per 1,000 member-months and allowed per admit (with DRG case mix as the sub-split), ambulatory visits per 1,000 and allowed per visit split by place_of_service_code, and emergency visits per 1,000 and allowed per visit. Prepare the data with version dedup to the surviving claim_version before a single dollar is summed, since frequency_code 7 replaces and 8 voids.
- Build the pharmacy branch on 30-day equivalents: scripts_30 equals the sum of days_supply over 30 on unreversed fills, so pharmacy PMPM equals (scripts_30 per member-month) times (allowed per 30-day script). Split the price term exactly as g times the generic price plus (1 minus g) times the brand price, where g is the generic dispensing rate, which makes generic substitution and unit price separable leaves with different owners.
- Attach one leading indicator per leaf: prior-authorization approvals and elective surgical bookings ahead of inpatient admits, contract effective dates ahead of allowed per unit, site-of-service steering ahead of the ambulatory place_of_service mix, new-to-brand starts and formulary tier placement ahead of pharmacy price.
- Name the node where arithmetic is exact but attribution is not: the risk-adjustment denominator. Dividing by the population mean prospective_risk_score is exact arithmetic, but the score is built from coded diagnoses, so more complete coding raises the denominator and lowers risk-adjusted PMPM with no change in care delivered. Carry raw PMPM, the mean risk score trend and a coding-intensity measure such as diagnoses per member-year beside it, permanently.
Worked solution 45 min
- Write the base identity and the three-term change decomposition on paper first, and confirm the terms sum to the total change on a two-category toy example before touching real data.
- Specify the preparation rules that precede any aggregation: dedup medical_claim_line to the surviving version per claim_id using claim_version and frequency_code, drop both rows of every reversed pharmacy pair, and build member-months from coverage spans with partial months counted fractionally.
- Draw the medical branch and the pharmacy branch to leaf level, writing the identity beneath each split rather than only the metric name.
- Write the generic-dispensing split as the exact price identity and confirm it reproduces the observed allowed per 30-day script.
- Attach one leading indicator and one named owning function to each leaf.
- Write the caveat on the risk-adjustment node, including the two companion series that must be published with it.
Follow-up
- Utilization is flat and allowed per unit is flat, yet PMPM rose 4 percent. Name every place the increase can be hiding.
- Your member-month denominator came from coverage spans that overlap across products for some members. What does that do to PMPM, and how do you decide whether to deduplicate?
- Which leaf would you refuse to put on a team's scorecard, and why?
First-pass denial rate doubled in one received week
First-pass denial rate jumped from 6.1 to 11.8 percent in a single received_date week and stayed at the new level. Nothing in the adjudication rules changed that week. You have medical_claim_line: claim_id, claim_version, frequency_code, claim_status, denial_reason_code, member_id, received_date, adjudicated_at, billing_provider_npi. The metric counts lines whose first adjudication returns 'denied', over lines received in the window at claim_version = 1. Find the cause, say whether denial behaviour actually changed, and deliver the corrected series plus the query change that fixes it.
Approach
- Cut the excess by denial_reason_code before anything else. A behaviour change spreads across codes; an upstream failure concentrates in one. If a single eligibility-related code carries nearly all of the 5.7 point excess, the claims are fine and the eligibility data was not.
- Join the denied lines to member_enrollment and compare the span's created_at to the claim's received_date. Lines denied for members whose coverage span loaded after the claim arrived are a load-ordering failure, not a coding failure.
- Check resolution downstream: take those claim_ids and look for a later version with frequency_code 7 that adjudicates to 'paid'. A high resubmit-and-pay share confirms the denial was transient and recoverable.
- Verify the denominator is not the mover. Count lines at claim_version = 1 by received_date and check the weekday mix, because submission batching makes received_date strongly day-of-week patterned and a short or holiday week shifts the denominator without changing behaviour.
- Confirm runout: the metric should be held until 60 days of adjudication have passed, so check that the comparison weeks are equally mature rather than comparing a settled week to a fresh one.
- Restate the series with the affected reason code broken out as its own line, and change the query to carry denial_reason_code into the reported breakdown rather than only the headline rate.
Follow-up
- The affected lines all get paid on resubmission. Is the first-pass denial rate then wrong, or right and uninteresting?
- How would you monitor for this class of failure without waiting for someone to notice a chart?
- What changes if the excess is spread evenly across denial_reason_code instead of concentrated?
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 ↗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.
Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.
Give an example of a challenge you faced while working in a team and h…
Give an example of a challenge you faced while working in a team and how you overcame it.
Approach
- Close with what you would do differently, concretely.
- 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
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Defend a null result against a programme sponsor
A care management programme enrolled the top 1 percent of members by prior-year allowed spend. The sponsor's deck shows allowed PMPM for enrollees falling 34 percent from the year before enrollment to the year after, and asks for budget to triple the programme. You rebuild the evaluation with a concurrent comparison group selected by the identical spend rule in the same period. The difference-in-differences estimate is a 3 percent reduction with a confidence interval spanning zero. Deliverable: how you present this, to whom, in what order, and what you propose next.
Approach
- Name the probe: whether you can deliver a finding that costs somebody their programme without softening it into uselessness or creating an adversary who routes around you next time.
- Lead with the mechanism, not the verdict. Show the comparison group's own unadjusted drop, which will be large, because a cohort selected on an extreme of the outcome regresses toward the mean whether or not anyone intervenes. The sponsor's 34 percent is mostly that, and it is a property of the selection rule rather than a criticism of their clinicians.
- Give the sponsor the finding privately before it appears in any deck their leadership sees. Being surprised in a room is what turns a methods disagreement into a political one.
- State the estimate with its interval and say what it rules out as well as what it fails to establish. A 3 percent point estimate whose interval crosses zero is not evidence of no effect; it is insufficient power to separate a modest effect from none, and those are different claims.
- Arrive with a design rather than only an objection. Propose a regression discontinuity at the enrollment threshold if the rule is applied sharply, or a randomised rollout across the next wave of eligible members, and state the sample size needed to detect the effect size the sponsor believes in.
Follow-up
- The sponsor says withholding the programme from a comparison group is unethical. What do you propose instead?
- Leadership wants one number for the board next week. What do you give them?
- What result would change your mind and make you believe the 34 percent?
Make an honest case for your own analytical impact
You are asked to account for a year of your work. Constraint: you may only use measures that existed before you knew the outcome, and you must include one project that moved nothing. The portfolio contains a measure specification that changed how a contract settles, a predictive model that shipped, a self-serve dashboard, and an evaluation that killed a proposed programme. Deliverable: how you attribute value to each, which of them you claim credit for and which you do not, and the one number you would accept being judged on next year.
Approach
- The probe is whether you can reason counterfactually about your own work, which is the same discipline this domain demands of you for programmes, applied where it is uncomfortable.
- State the counterfactual for each item: what would have happened without you. A measure specification that changed how a contract settles has a dollar counterfactual you can compute. A dashboard's counterfactual is analyst hours, which is real and small, and attaching a revenue number to it is where these accounts usually turn into fiction.
- Treat the killed programme as your strongest item and say why. Avoided spend is real value, it is attributable because the decision turned on your evaluation, and it is the one item nobody else will count on your behalf.
- Report the shipped model the way you would report anyone's model: calibration in the subgroups that matter, adoption by the people it was built for, and whether any decision changed. A model that is live but ignored has zero impact, and saying so is the credibility test in this answer.
- Name the failed project with its mechanism and its cost, then say what earlier signal you would watch to kill it sooner. Time to kill is a legitimate impact measure.
- Choose next year's number so that it can go down. A metric that only ratchets upward is not a commitment.
Follow-up
- Two other people also claim the contract measure. How do you split it?
- Your model is live and used, but the decisions it informs would have been the same without it. Is that impact?
- What is the strongest argument that your year had less impact than you just described?
- 01
Give an example of a challenge you faced while working in a team and how you overcame it.
- 02
A care management programme enrolled the top 1 percent of members by prior-year allowed spend. The sponsor's deck shows allowed PMPM for enrollees falling 34 percent from the year before enrollment to the year after, and asks for budget to triple the programme. You rebuild the evaluation with a concurrent comparison group selected by the identical spend rule in the same period. The difference-in-differences estimate is a 3 percent reduction with a confidence interval spanning zero. Deliverable: how you present this, to whom, in what order, and what you propose next.
- 03
You are asked to account for a year of your work. Constraint: you may only use measures that existed before you knew the outcome, and you must include one project that moved nothing. The portfolio contains a measure specification that changed how a contract settles, a predictive model that shipped, a self-serve dashboard, and an evaluation that killed a proposed programme. Deliverable: how you attribute value to each, which of them you claim credit for and which you do not, and the one number you would accept being judged on next year.
Is this an official Otsuka interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Otsuka. Rounds and questions reflect what candidates have reported, not a process Otsuka has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the interviews for the Data Scientist position?
The interview process is rigorous but fair, designed to evaluate both your technical expertise and cultural fit. Candidates typically find that preparation in key areas significantly enhances their performance.
PracHub interview research ↗What distinguishes successful candidates at Otsuka?
Successful candidates demonstrate a strong blend of technical skills, problem-solving ability, and the capacity to communicate insights clearly. They also align well with Otsuka's mission and values.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The interview process usually spans several weeks, with candidates typically receiving feedback after each stage. Expect a structured timeline that allows for thorough evaluations.
PracHub interview research ↗What is the culture like at Otsuka?
Otsuka fosters a collaborative and innovative environment where team members are encouraged to contribute their ideas and expertise. The focus is on improving patient outcomes through teamwork and data-driven decision-making.
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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