Interview guides · Biogen

Biogen Data Scientist Interview Questions & Guide 2026

Timed data scientist practice for Biogen: short SQL and probability sets run end to end, then scored against a rubric.

The 60-second brief

As a Data Scientist at Biogen, you are at the intersection of cutting-edge biotechnology and advanced computational science. You will contribute to the development of life-changing therapies for patients with complex neurological and immunological conditions. Your work involves transforming massive, high-dimensional datasets into actionable insights that drive drug discovery, clinical trial optimization, and digital marketing precision.

Coding rounds for this role are usually data-manipulation shaped rather than data-structure shaped: group-bys, joins, time windows, ranking within a partition. Confirm the format before spending a week on graph traversal.

Focus areas

Minimise protected health information in every extract
Reconstruct drug exposure intervals from dispensing records
Separate censoring from events in time-to-event models

Overview

The role is both intellectually demanding and mission-critical. You will partner with cross-functional teams, including biologists, clinicians, and commercial strategists, to solve high-stakes problems. Whether you are building Natural Language Processing (NLP) models to analyze patient feedback or applying signal processing to clinical trial data, your contributions directly influence the speed and efficacy with which Biogen brings innovation to the global market.

The Biogen interview process

Biogen candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Most 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

Hiring Manager Discussion

Much 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

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

Panel Interview

Where 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

Where candidates lose points

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.

Reporting a p-value with no effect size or interval

Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.

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.

Questions & practice

What statistical methods do you prefer for validating model performance in a high-stakes environment?

Medium · machine learning and modelling

Approach

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

Follow-up

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

Can you walk us through a complex machine learning model you built from scratch?

Medium · machine learning and modelling

Approach

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

Follow-up

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

Simulate how ignoring clustering inflates the false positive rate

Medium · simulation · design effect · type i error

Simulate a provider-level comparison with no true effect. 40 providers, 60 members each, a continuous outcome with total variance 1 and intraclass correlation 0.02. Randomise providers 20 to 20, not members. Over 5,000 replications, report the share of replications where a two-sample t-test run on all 2,400 individual observations, ignoring provider, returns p below 0.05. Report the same share when the unit of analysis is the 40 provider means. Then state how the first number follows from the design effect 1 + (m - 1) x ICC.

Approach

Decompose the variance explicitly rather than tuning it: provider variance = ICC = 0.02, residual variance = 1 - ICC = 0.98. Draw u_j once per provider and e_ij per member, so the outcome is u_j + e_ij and the correlation between two members of one provider is 0.02 by construction.
Randomise at the provider level. That is the entire mechanism: the treatment indicator is constant within a provider, so in any single replicate the provider intercepts are confounded with the arm, and the naive test reads that confounding as signal.
Vectorise over replications with a (reps, providers, members) array. 5,000 x 2,400 normals is trivial in numpy, and a Python loop is what tempts people to cut replications to 500 and report a number with Monte Carlo noise of plus or minus 0.017.
Predict the answer before running it: design effect = 1 + (60 - 1) x 0.02 = 2.18, naive standard errors are too small by sqrt(2.18) = 1.476, so the rejection rate at nominal 0.05 is about 2 x (1 - Phi(1.96 / 1.476)) = 0.18. Agreement between prediction and simulation is what makes the result a demonstration rather than an anecdote.
Run the provider-mean analysis as the control. Recovering 0.05 there proves the generator is correct and isolates the failure to the analysis unit.

Worked solution (30 min)

Draw u with shape (reps, 40) from N(0, sqrt(0.02)) and e with shape (reps, 40, 60) from N(0, sqrt(0.98)); y = u[:, :, None] + e.
Assign providers 0-19 to one arm and 20-39 to the other; because there is no true effect, a fixed split is valid and removes one source of Monte Carlo noise.
Naive test: flatten each arm to 1,200 observations and compute a two-sample t statistic per replication with vectorised means and pooled variance.
Cluster test: average within provider to 40 values, then run the same two-sample t on 20 versus 20.
Report both rejection shares and compare the naive one against 2 x (1 - Phi(1.96 / sqrt(2.18))).

Expected result: The naive individual-level test rejects in roughly 18 percent of replications, against a nominal 5 percent. Monte Carlo standard error at 5,000 replications is about 0.005, so anything in 0.17 to 0.19 is consistent. The provider-mean analysis rejects in about 5 percent, within roughly 0.003.

Verify it yourself

Set ICC to 0 and both analyses return about 0.05. If the naive one does not, the data generator is wrong, not the test.
Raise members per provider from 60 to 120 with ICC fixed and the naive rate rises, because the design effect grows linearly in cluster size.
The provider-mean analysis stays near 0.05 at every ICC you try, which is the definition of a valid test.
The simulated naive rate lands within about 0.01 of 2 x (1 - Phi(1.96 / sqrt(design effect))).

Follow-up

How many providers would you need for 80 percent power on a 0.2 SD difference with 60 members each and this ICC?
Cluster sizes are unequal in reality. The design effect approximation becomes 1 + ((1 + CV^2) x mbar - 1) x ICC. Which direction does that move your sample size and why?
The intervention cannot be withheld from any provider. Sketch a design that still yields a defensible estimate.

Turn overlapping coverage spans into fractional member-month denominators

Medium · intervals · gaps and islands · denominators

member_enrollment holds span_id, member_id, plan_id, product_type, effective_date and termination_date, which is NULL while the span is active. A member can hold several spans in a year, and back-dated plan changes make spans overlap. Return one row per calendar month of 2025 with total member-months, where a member contributes covered days in the month divided by that month's length, and a day covered by two overlapping spans counts once. Both span endpoints are inclusive. Collapse overlaps with window functions before joining a month calendar, and do not assume one span per member.

Approach

Normalise the open end first. COALESCE(termination_date, DATE '9999-12-31') or clip to the reporting end, because a NULL termination_date makes every BETWEEN comparison unknown and drops exactly the members who are still covered.
Merge overlapping spans per member before the calendar join. Order by effective_date, carry a running MAX of prior termination_date with a frame ending one row before, and open a new island where effective_date exceeds that running max plus one day, which keeps adjacent spans contiguous rather than splitting them.
Clip each merged island to the reporting year, then cross join to a twelve-row month calendar and keep pairs that intersect.
Compute covered days as LEAST(island_end, month_end) minus GREATEST(island_start, month_start) plus one, and divide by the actual length of that month rather than a flat 30, so February and the 31-day months are not distorted.
Sum the fractions by month. Merging after the calendar join also works but forces a DISTINCT over member-days, which stops scaling at population size.

Follow-up

Where does a continuous-enrolment filter with an allowable 45-day gap belong, and how does that change the island boundary rule?
One human appears under two member_ids after a master person index merge. What does that do to member-months, and how would you detect it?
How do you treat a span that terminates on the day it starts, and what does that row usually represent?

Report site activation to first randomization without hiding non-enrollers

Medium · grain collapse · left join · censoring

study_site holds one row per (study_id, site_id): site_activation_date, which is NULL for a site selected but never activated, and site_status. study_subject_visit holds subject_visit_id, study_id, site_id, subject_id, randomization_ts (NULL for screen failures), visit_name, visit_number, actual_visit_date and visit_status, with one row per subject per protocol visit, so randomization_ts repeats across every visit of a randomized subject and a site that has screened nobody has no rows there at all. For one study_id and a stated data cut date, return per activated site the activation date, the first randomization_ts and the days between them, with activated sites that have randomized nobody still present. Add a study-level summary giving median days to first randomization and the share of activated sites with no randomization by day 90.

Approach

Take the site universe from study_site, never from study_subject_visit. The visit table can only contain sites that have screened at least one subject, so a universe built from DISTINCT site_id there omits exactly the sites this metric exists to expose. Filter study_site to the study and to site_activation_date IS NOT NULL, then LEFT JOIN the per-site first randomization.
Collapse the grain before aggregating anything on the visit side. The table repeats randomization_ts across every visit of a randomized subject, so any count taken at row grain is inflated by visits per subject. MIN(randomization_ts) per site is safe, COUNT is not.
State the date boundary rule. randomization_ts is a timestamp and site_activation_date is a date, so a randomization at 23:40 local time can land on either side of a day boundary depending on the zone the timestamp is stored in. Cast once, deliberately, and say which zone.
Use PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY days) for the median and be explicit that it ignores NULLs, so it is a median over enrolling sites only. That is exactly why the non-enroller share is reported beside it rather than instead of it.
Censor the day-90 denominator against the data cut. A site activated 40 days before the cut has not had 90 days to randomize, so it cannot be scored on that bar and belongs outside that denominator.

Worked solution (25 min)

CTE sites: site_id and site_activation_date from study_site for the study where site_activation_date IS NOT NULL. The (study_id, site_id) key already gives one activation date per site, so assert that key rather than de-duplicating defensively.
CTE first_rand: site_id and MIN(randomization_ts) over study_subject_visit rows for the study where randomization_ts IS NOT NULL, grouped by site_id.
LEFT JOIN sites to first_rand and compute days_to_first_randomization with an explicit cast of the timestamp to a date.
Summary: PERCENTILE_CONT(0.5) over non-null days, plus the count and share of sites with no randomization by day 90, restricted to sites activated at least 90 days before the data cut.
Compare the site-level row count with COUNT(DISTINCT site_id) in study_subject_visit for the study; the difference is the activated sites that have screened nobody, which is the population the metric is for.

Expected result: One row per activated site with site_id, site_activation_date, first_randomization_ts and days_to_first_randomization, NULL in both randomization columns for sites that have randomized nobody, including sites with no study_subject_visit rows at all; plus one summary row with median_days_over_enrolling_sites, sites_activated_90d_or_more and share_with_no_randomization_by_day_90.

Verify it yourself

Site-level row count equals the number of study_site rows for the study with a non-null site_activation_date, and is greater than or equal to COUNT(DISTINCT site_id) in study_subject_visit for that study. Below it means either an activated site is missing from study_site or a visit row carries a site_id whose activation date is NULL, both data-quality findings.
No days_to_first_randomization is negative. A negative value is a data-quality finding to raise, not a number to clamp at zero.
The day-90 denominator excludes sites activated within 90 days of the data cut. Including them can only add sites that have not had a full 90 days, and any of those without a randomization would be miscounted as a failure.

Follow-up

The same site_id appears under two studies. What breaks, and what is the correct grain for this metric?
Would you present this as a median, as a Kaplan-Meier curve of time to first randomization, or both, and what does the curve show that the median cannot?
A site randomized one subject who withdrew at visit 2. Does that site count as enrolling here, and how does that differ from a randomized-and-retained measure?

How do you prioritize your work when balancing multiple research or commercial requests?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
Decompose the metric into the rates that drive it, and say which one you would check first.
Fix the population and the time window before naming any metric.

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 do you approach signal processing tasks when dealing with noisy biological data?

Medium

Approach

State your assumptions explicitly before working the problem.
Work from the decision backwards to the evidence you would need.
Say what you would check first and why it is the highest-information step.

Follow-up

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

Size a clinic-randomised trial when the intracluster correlation binds

Medium · cluster randomisation · design effect · stepped wedge

A care-coordination change is delivered at the clinic level and cannot be withheld from some members of a participating clinic. Forty-eight clinics are available for 1:1 randomisation. The primary outcome is the 90-day primary care contact rate among newly enrolled members, baseline 0.62, with a target of plus 5 percentage points. Each clinic contributes about 60 eligible members a year, and the intracluster correlation on this outcome is 0.02. Two-sided alpha 0.05, 80 percent power. Give clinics required per arm, say whether 48 suffices, and what you would do.

Approach

Compute the individually randomised sample size first, then inflate it by the design effect 1 + (m - 1) x ICC, where m is the average cluster size. The design effect is the price of correlation within clinics and is the single largest term in this calculation.
Convert the inflated individual N into clusters by dividing by m, and compare with the 24 clinics available per arm.
Check the ceiling before proposing a longer accrual window. As m grows, effective members per cluster approaches 1 / ICC, here 50, so 24 clinics can never contribute more than 1,200 effective members per arm no matter how many members each one enrols. If the required effective N exceeds that, no amount of accrual rescues the parallel design.
Propose the designs that actually change the arithmetic: a stepped wedge in which every clinic crosses over so each contributes a within-cluster comparison, or a smaller randomisation unit (provider pod) if the intervention can be delivered at that level without contamination.
Add the two refinements a reviewer will ask for: use a t-distribution with roughly 2k - 2 degrees of freedom rather than z when k is small (here about 46 df, which raises N by about 4 percent), and use covariate-constrained randomisation on baseline contact rate so 48 clinics do not land badly imbalanced by chance.

Worked solution (30 min)

Individual n per arm = 7.849 x (0.62 x 0.38 + 0.67 x 0.33) / 0.05^2 = 7.849 x 0.4567 / 0.0025 = 1,434.
Design effect = 1 + (60 - 1) x 0.02 = 2.18.
Inflated n per arm = 1,434 x 2.18 = 3,126 members, so clusters per arm = 3,126 / 60 = 52.1, round up to 53.
Compare with 24 clinics available per arm: the study is short by more than a factor of two.
Compute the ceiling: 1 / 0.02 = 50 effective members per clinic, so 24 clinics give at most 1,200 effective members per arm against the 1,434 required.

Expected result: 53 clinics per arm (106 total) against 48 available. The effective-sample ceiling of 1,200 per arm is below the 1,434 needed, so the parallel cluster design is infeasible at any panel size; the answer is a stepped wedge, a smaller randomisation unit, or a pre-registered larger MDE.

Verify it yourself

Verify the design effect directly: 1 + 59 x 0.02 = 2.18.
Verify the diminishing return: doubling m from 60 to 120 raises effective members per cluster only from 27.5 to 35.5, not from 60 to 120.
Recompute with t-based quantiles at 46 df and confirm the requirement moves by a few percent, not by a factor.

Follow-up

Sketch the stepped-wedge analysis model. What does a secular trend do to the estimate if you leave period effects out?
The ICC was estimated from a prior year at 0.02 with a wide interval. How do you size under ICC uncertainty?
Six clinics withdraw after randomisation. What does that do to power compared with losing the same number of members?

First-pass denial rate doubled in one received week

Medium · pipeline failure · segment cuts · denominator definition

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?

Suggested practice checklist

For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.

Day 1 — Build a fixture you can check answers against

Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.

Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.

Day 2 — Joins, filters and NULL semantics

Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.

Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.

Day 3 — Window functions and frames

Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.

Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.

Day 4 — The four analytical query patterns

Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.

Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.

Day 5 — Write SQL the way you will have to write it live

Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.

Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.

Day 6 — One day for everything that is not SQL

Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.

Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.

Day 7 — Full loop rehearsal

Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.

Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.

Rigour against a deadline

Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.

Describe a time you had to explain a complex technical result to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.
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 would you do differently if you ran that project again?

Tell us about a time you had to pivot your strategy due to changing project requirements.

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Name the disagreement or constraint, and how you resolved it with evidence.
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?
How did you know the outcome was caused by your change?

How would you explain your experience with NLP and its application to our current projects?

Medium · behavioural and stakeholder questions

Approach

Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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