Illumina Data Scientist Interview Questions & Guide 2026

The 60-second brief

At Illumina, a Data Scientist sits at the intersection of cutting-edge technology and biological breakthrough. You are not just analyzing numbers; you are interpreting the code of life to help solve some of the world’s most complex medical challenges. Your work directly impacts the development of Next-Generation Sequencing (NGS) platforms and the algorithms that power personalized medicine, oncology research, and rare disease diagnostics.

Learn the economics of the product category before the loop. Marketplaces, subscription products and ad-supported products turn on different core quantities (match rate and liquidity, retention and churn, fill rate and yield) and fail in different characteristic ways.

Focus areas

• Correct for claims runout before reporting recent months
• Separate censoring from events in time-to-event models
• Reconstruct drug exposure intervals from dispensing records

Overview

The role is critical because it bridges the gap between raw genomic data and actionable clinical insights. You will collaborate with Bioinformaticians, Software Engineers, and Wet-lab Scientists to optimize sequencing accuracy, develop predictive models, and scale data pipelines. Whether you are working on DRAGEN (Illumina's secondary analysis suite) or exploring new machine learning applications in genomics, your contributions help maintain Illumina’s position as the global leader in DNA sequencing.

Candidates find this role particularly rewarding due to the sheer scale and complexity of the data. You will face challenges that don't exist in traditional tech environments, such as high-dimensional biological noise and the need for extreme precision in clinical settings. Success here means applying rigorous statistical methods to improve human health on a global scale.

The Illumina interview process

Illumina candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

• Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
• Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
• Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

• Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
• Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
• Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Evaluation

Much 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

Asynchronous Assessment

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

• Whether an ambiguous term becomes a specific column and filter before any computation happens
• Whether you read the schema for keys and cardinality rather than only for column names
• Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

• Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
• On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
• Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Onsite Interview

Where 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

Final Decision

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

• Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
• Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
• Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

• Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
• Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
• Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
• Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Where candidates lose points

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.

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.

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

Ignoring interference between units in a marketplace experiment

Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.

Questions & practice

What are the main sources of error in Illumina sequencing, and how would you model them?

Medium · machine learning and modelling

Approach

1. Pick an evaluation metric that matches the cost of each error type, not a default.
2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
3. Check what information would not exist at prediction time, and exclude it.

Follow-up

• Where could label leakage enter this setup?
• How would you choose the decision threshold, and who owns that choice?

How do you stay current with the rapidly evolving field of Machine Learning?

Medium · machine learning and modelling

Approach

1. Pick an evaluation metric that matches the cost of each error type, not a default.
2. Set a baseline first, so any model has something honest to beat.
3. Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

• What would you monitor after launch to know the model is still valid?
• Where could label leakage enter this setup?

What metrics would you use to evaluate a model designed to detect rare genetic variants?

Medium · machine learning and modelling

Approach

1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
2. Check what information would not exist at prediction time, and exclude it.
3. Say how the offline result would be validated online before it is trusted.

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?

Explain the difference between supervised and unsupervised learning in the context of gene expression clustering.

Medium · machine learning and modelling

Approach

1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
2. Set a baseline first, so any model has something honest to beat.
3. Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

• What would you monitor after launch to know the model is still valid?
• Where could label leakage enter this setup?

Expand overlapping coverage spans into fractional member-months

Medium · interval expansion · denominators · vectorisation

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

1. 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.
2. 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.
3. 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.
4. Divide overlap days by the number of days in that month, so February and July are weighted correctly, then sum by month.
5. 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)

1. Fill termination_date nulls with the reporting end date and clip all spans to the reporting year.
2. 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.
3. Aggregate each group to min start and max end, producing disjoint spans per member.
4. Cross join merged spans to a 12-row month frame, compute clipped overlap days, divide by days in month.
5. Group by month and sum.

Expected result: Twelve rows. For a member with spans 2026-01-01 to 2026-03-15 and 2026-03-01 to 2026-06-30, March contributes exactly 1.0, not 15/31 plus 31/31. For a member with spans covering January and March only, January is 1.0, February is 0.0, March is 1.0, and the member totals 2.0.

Verify it yourself

• A member with one span covering the full year sums to exactly 12.0 across the twelve rows.
• Total member-months is less than or equal to (distinct members) x 12, and equality holds only if every member is covered all year.
• Re-running the span merge on its own output changes nothing, which proves the merged spans are disjoint.
• Recompute one member by brute-force day expansion and confirm the two agree to floating point tolerance.

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?

Compute allowed PMPM by incurred month without span fan-out

Hard · fan-out · many-to-many · runout

Report allowed PMPM by incurred month and product_type for 2025 from medical_claim_line (member_id, claim_id, claim_version, frequency_code, service_start_date, allowed_amount, claim_status), pharmacy_claim (member_id, fill_date, allowed_amount, reversal_flag, reversed_fill_id), member_enrollment (span_id, member_id, product_type, effective_date, termination_date) and member_risk_score (member_id, score_year, prospective_risk_score), which holds at most one row per member per score year. The numerator is allowed_amount on surviving claim versions plus unreversed fills, attributed to the product_type in force on the service or fill date. The denominator is member-months. Also return a risk-adjusted PMPM for each cell: allowed PMPM divided by the member-month-weighted mean prospective_risk_score for score_year 2025 over the members making up that cell's denominator. A colleague joined claim lines to member_enrollment on member_id alone and the annual total came back 1.4 times the finance figure. Diagnose that, then produce the correct monthly series.

Approach

1. Name the defect precisely. member_enrollment is one row per span, so joining on member_id alone is one-to-many and duplicates every claim line once per span that member ever held. The inflation factor is the spend-weighted mean span count per member, which is why 1.4 is plausible and why it is not a constant you can divide back out.
2. Make the join functional: member_id AND service date between effective_date and COALESCE(termination_date, DATE '9999-12-31'). That yields at most one span per line unless spans genuinely overlap, in which case pick a documented precedence rule, most recently effective wins being the usual one, and prove uniqueness with a duplicate check rather than asserting it.
3. Aggregate numerator and denominator separately and join them on (month, product_type). Computing both sides across one join is the second route to inflation, because the member-month denominator fans out over claim lines just as easily as the dollars fan out over spans.
4. Clean each numerator source on its own terms: surviving claim versions with voids removed and claim_status 'paid' on the medical side, reversal rows and their originals removed on the pharmacy side.
5. Apply completion before anyone reads a trend. State the paid-through date and either withhold the three most recent incurred months or apply completion factors, since the lag differs by service type and the incomplete tail reads as a cost improvement and a utilisation drop at once.
6. Risk-adjust last, and weight the score the way the denominator is weighted. The divisor for a cell is SUM(member_months * prospective_risk_score) / SUM(member_months) over that cell's members, not a plain AVG over distinct members: a member covered two months must not count the same as one covered twelve. member_risk_score is member-level, so join it after the member-month grain is fixed, or it becomes a second fan-out.
7. State the rule for members with no 2025 member_risk_score row: keep them in member_months, leave them out of the weighted mean, and publish the scored share of member-months beside the adjusted series. A thin score table moves the adjusted number while the raw one sits still, and without that share nobody can tell the two apart.

Follow-up

• A member holds two overlapping spans under different product_types on the service date. Which one gets the dollar, and how do you keep the member-month denominator consistent with that choice?
• Write the check that proves the span join is one-to-one rather than assuming it.
• Finance reports on paid_date and you reported on service_start_date. Reconcile one month between the two bases.

Count thirty-day unplanned readmissions without counting transfers

Medium · self-join · event streams · exclusions

encounter holds encounter_id, patient_id, facility_id, encounter_type, admit_ts, discharge_ts, admission_type, discharge_disposition, is_planned_admission and index_encounter_id. For inpatient stays discharged between 2025-01-01 and 2025-11-30, return the 30-day unplanned readmission rate overall and by facility_id. Exclude an index stay when is_planned_admission is TRUE or discharge_disposition is transfer_acute, expired, hospice or ama. A qualifying readmission is any unplanned inpatient admission for the same patient with admit_ts after the index discharge_ts and within 30 days of it. One stay can be both a readmission and the next index stay.

Approach

1. Decide between LEAD and EXISTS explicitly and say why. LEAD over the patient's admission stream gives the literal next stay, so an intervening planned admission hides a later unplanned one. The measure asks whether any unplanned admission fell in the window, which is an EXISTS or a self-join with a range predicate.
2. Exclude transfers at the index end before anything else. An acute-to-acute transfer closes one encounter and opens another at a different facility_id within hours, so without the transfer_acute exclusion the facilities that move the sickest patients out look the worst.
3. Apply the index exclusions as a set, including stays with NULL discharge_ts, which are still open and have no measurable window.
4. Stop the denominator early enough that every index stay had a full 30 days of observation plus encounter lag. That is why the window ends 2025-11-30 rather than at year end.
5. Compute the rate at stay level, not patient level, then compare the raw facility rates against an observed-over-expected framing before anyone publishes a ranking.

Worked solution (30 min)

1. CTE ip: inpatient encounters with a non-null discharge_ts.
2. CTE index_stays: ip rows with discharge_ts in the window, is_planned_admission FALSE, and discharge_disposition not in the four excluded values.
3. Flag each index stay with EXISTS against ip for the same patient_id where is_planned_admission is FALSE, encounter_id differs, admit_ts > index.discharge_ts and admit_ts <= index.discharge_ts + 30 days.
4. Aggregate numerator and denominator overall and grouped by facility_id.
5. Re-run with the transfer exclusion removed and record how much the rate moves.

Expected result: Eligible index stays, stays with a qualifying readmission, and the quotient, overall and per facility_id. Removing the transfer exclusion returns a higher rate, and the gap between the two is a number you should be able to quote.

Verify it yourself

• Numerator never exceeds denominator, and an index stay with two qualifying readmissions still contributes exactly one.
• No index stay in the output has discharge_disposition transfer_acute, expired, hospice or ama.
• A 0-day window gives a rate of exactly 0, and a 365-day window gives a rate no lower than the 30-day one.
• Facility rates weighted by facility denominators reconcile to the overall rate.

Follow-up

• A patient has three stays ten days apart. How many index stays and how many readmissions, and why does the answer depend on the stated rule?
• Which comparison would you publish across facilities, raw rate or observed over expected, and what does risk adjustment still fail to fix?
• index_encounter_id already exists on the table. Would you trust it, and how would you audit it against your own logic?

Describe the process of cross-validation and why it's critical for genomic models.

Medium

Approach

1. Say what you would check first and why it is the highest-information step.
2. State your assumptions explicitly before working the problem.
3. 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?

How would you handle missing data in a longitudinal clinical study?

Medium

Approach

1. Clarify what is being asked and what a complete answer would contain.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.

Follow-up

• How would you know your answer was wrong?
• What assumption would you test first?

Propose a proxy for care that never happened

Hard · proxy metrics · informative missingness · bias direction · churn

Leadership asks for a metric for unmet need: care a member should have received and did not. Claims record billing events, so no claim and no need look identical in the data. Using member_enrollment, medical_claim_line, encounter, lab_result and pharmacy_claim, propose the best available proxy, state the direction of its bias and what drives the magnitude, and name one decision the proxy is good enough to support and one it is not. Say plainly whether the quantity itself is identified by this data.

Approach

1. Start by refusing the direct metric and saying why in one sentence: every table here records an action someone took and billed for, so the absence of a row is the absence of a billed action, and no combination of these tables distinguishes a member who needed nothing from a member nobody reached. The quantity is not identified.
2. Build the proxy only where non-treatment leaves a downstream trace. Two families work: ambulatory care sensitive admissions per 1,000 member-years, where an inpatient stay for short-term diabetes complications, heart failure, adult asthma, bacterial pneumonia or urinary infection implies outpatient care that did not land; and guideline gap measures on an identified cohort, for example members with a qualifying diagnosis on at least two medical_claim_line rows at least 30 days apart, continuously enrolled, with no matching lab_result loinc_code and no matching procedure_code in the last 12 months.
3. State the bias direction without hedging. Both families require prior contact: a gap measure needs a diagnosis, which needs someone to have coded it, and an admission needs the member to reach a facility. The proxy therefore undercounts unmet need, and it undercounts it most in exactly the population that has the most of it, namely members with no contact at all.
4. Name the magnitude drivers so the number can be read over time: coverage churn (short spans fail continuous-enrolment filters and drop out of the denominator), coding intensity (more diagnosis coding enlarges the identified cohort and can raise measured gaps with no change in care), and site-level ordering habits in lab_result, since a test exists because somebody chose to order it.
5. Pair it with a denominator-side companion that moves the opposite way: the count and share of continuously enrolled members with zero claims of any kind in 12 months. Reporting it beside every unmet-need number blocks the improvement that comes from losing the least-observed members.
6. Close with the usability boundary: the proxy supports relative prioritisation of outreach across panels within the same product and region, where the bias is shared; it does not support a year-over-year claim that unmet need fell, because churn and coding intensity move the proxy on their own.

Worked solution (40 min)

1. Write the identification argument in three sentences: what generates rows in each table, what a missing row can mean, and why no filter separates the two meanings.
2. Specify one gap measure end to end: cohort definition with the two-claims-30-days-apart rule, continuous-enrolment requirement with the allowable gap in days, the expected event defined by loinc_code in lab_result or procedure_code in medical_claim_line, and the 12-month window.
3. Specify the ambulatory care sensitive admission rate per 1,000 member-years from encounter and member_enrollment, with the diagnosis list named and planned admissions excluded.
4. Write the bias paragraph: direction, the three magnitude drivers, and the specific scenario in which the proxy improves while care worsens.
5. Define the zero-utiliser companion metric and state that it is published on the same page as the proxy, not in an appendix.
6. Write the two-line usability boundary naming the one decision it supports and the one it does not.

Expected result: A written proposal stating that unmet need is not identified by claims or clinical data; offering a two-part proxy of guideline gap rates on a diagnosis-identified, continuously enrolled cohort plus ambulatory care sensitive admissions per 1,000 member-years; declaring the bias as a systematic undercount concentrated in members with no prior contact; naming churn, coding intensity and site ordering habits as the magnitude drivers; pairing the proxy with the zero-utiliser share; and limiting its use to cross-panel prioritisation within a product and region rather than year-over-year claims.

Verify it yourself

• Recompute the gap rate with the continuous-enrolment requirement removed. A large move confirms churn is driving the denominator, which is the bias the write-up claims.
• Split the identified cohort by number of qualifying diagnosis claims. If the gap rate differs sharply between members at exactly two claims and members at many, the cohort definition is picking up coding intensity as much as disease.
• Compare gap rates across sites with known differences in lab ordering volume. Divergence there is the ordering-habit driver, not a care difference.
• Check that the zero-utiliser share and the gap rate are not moving in opposite directions unexplained; if they are, the proxy is improving by losing members.

Follow-up

• Your gap measure improves 3 points year over year and the zero-utiliser share rose 1.5 points. What do you tell leadership?
• Would adding the presence indicator for lab orders as a model feature help or hurt, and what happens when that model is deployed at a site with different ordering protocols?
• What data outside these five tables would actually identify unmet need, and what would it cost to get it?

Allowed PMPM fell fourteen percent in three months

Easy · claims runout · completion factors · incurred vs paid

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

1. 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.
2. 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).
3. 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.
4. 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.
5. 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.
6. 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?

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

• Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
• For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
• Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

• Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
• Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
• Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

• Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
• Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
• Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

• Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
• For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
• Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

• Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
• Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
• Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

• Repeat the depth protocol on the second-ranked area with the same six-problem structure.
• Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
• Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

• Re-run the six prompts from day one under the same clock and compare both correctness and time.
• Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
• Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

Ownership when the result is inconvenient

Sometimes the honest read is that the initiative did not work, and the person who commissioned the analysis was hoping otherwise. Interviewers want to know whether you softened it. Prepare the case where you delivered an unwelcome result, how you presented the uncertainty without hiding behind it, and what the team did next.

Tell me about a time you had to explain a complex technical concept to someone with no data background.

Medium · behavioural and stakeholder questions

Approach

1. Pick a story where you drove the decision, not one where you observed it.
2. State the situation in two sentences and spend the rest on your reasoning.
3. Close with what you would do differently, concretely.

Follow-up

• What did you decide not to do, and why?
• What would you do differently if you ran that project again?

Why are you interested in Genomics, and why Illumina specifically?

Medium · behavioural and stakeholder questions

Approach

1. State the situation in two sentences and spend the rest on your reasoning.
2. Name the disagreement or constraint, and how you resolved it with evidence.
3. Close with what you would do differently, concretely.

Follow-up

• What did you decide not to do, and why?
• How did you know the outcome was caused by your change?

Sequence three urgent requests with one analyst-week available

Medium · prioritisation · tradeoffs · stakeholder communication

Three requests land on Monday and you have one week. An actuarial team needs incurred-claims completion factors restated before a filing deadline on Thursday. A clinical programme owner wants a deterioration model refreshed because its calibration has drifted in one region. A trial operations team wants site enrolment forecasts for a portfolio review in two weeks. Each requester believes theirs is blocking. Deliverable: your sequence with the reasoning, the message you send to whoever is deprioritised, and the smaller artefact you hand each of the two you cannot fully serve.

Approach

1. The probe is whether you prioritise on consequence and reversibility rather than on who asked loudest or most recently.
2. Classify each request by what happens if it slips. A regulatory or contractual deadline is irreversible on its date, a drifting model is causing harm every day it keeps running, and a portfolio review can absorb a provisional number. That ordering is defensible to all three requesters because it does not depend on your preferences.
3. Take the deadline-bound work first, but scope it to the minimum defensible output, because completion factors feeding a filing carry a different error tolerance than a slide.
4. Do not let the drifting model simply wait. Quantify the harm cheaply by comparing calibration in the affected region against the rest, and if it is materially miscalibrated propose flagging or suppressing its output for that region within the hour rather than at the end of a refresh.
5. Give each deprioritised requester something real: a provisional forecast with its uncertainty and a refresh date, or a diagnostic that tells them whether their problem is urgent. Say no explicitly with a date rather than going quiet, because silence is what produces escalation.

Follow-up

• The programme owner escalates to your manager. What do you want your manager to be able to say?
• Midweek the actuarial work needs two more full days than you estimated. What gives?
• How does your answer change if the drifting model drives a clinical outreach list rather than a report?