Eliassen Group Data Scientist Interview Questions & Guide 2026

The 60-second brief

The Data Scientist role at Eliassen Group is pivotal in leveraging data to drive decision-making and enhance business operations. As a Data Scientist, you will be responsible for building and deploying machine learning models, developing data pipelines, and applying advanced analytical techniques to solve complex problems. This role directly influences product development, user experience, and strategic business outcomes, making it not only crucial but also highly impactful.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

Focus areas

• Turn a vague request into a measurable question
• Size an experiment before anyone launches it
• Define numerator, denominator and window precisely

Overview

In your capacity, you will collaborate with cross-functional agile teams, integrating secure and scalable AI capabilities that optimize various use cases within production environments. Your contributions will help shape the future of how Eliassen Group harnesses data, transforming raw information into actionable insights that enhance organizational efficiency and effectiveness. Expect to work on challenging projects that push the boundaries of technology and analytics, making your work both dynamic and rewarding.

The Eliassen Group interview process

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

Phone Screen

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

• Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
• Whether you name what you have not done instead of stretching to cover every line of the posting
• Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

• Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
• Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
• Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live

Technical Assessment

A 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

In-Person Interview

Because the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.

What to demonstrate

• Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
• Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
• Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs

How to prepare

• For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
• Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
• For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.

Where candidates lose points

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

Slicing a flat experiment until a segment reaches significance

Testing one metric across twenty segments at a nominal 5% level produces a significant result about two thirds of the time when nothing is happening anywhere, and the segment that surfaces is by construction the one with the most favourable noise. The reported effect in that slice is then badly overstated, because selection on significance conditions the estimate on being large. What makes it dangerous rather than merely wrong is that a post-hoc segment always has a plausible story attached, so it survives the meeting. The controls are declaring the small number of segments of interest before launch, correcting across the ones tested, and treating anything discovered afterwards as a hypothesis that needs its own adequately-powered test rather than a finding.

Reporting a mean for a heavy-tailed metric without saying what it hides

For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.

SQL that silently fans out on a one-to-many join

State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.

Questions & practice

Describe a machine learning project you have worked on and the challenges you faced.

Medium · machine learning and modelling

Approach

1. Pick an evaluation metric that matches the cost of each error type, not a default.
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

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

Sessionise an event stream with gap and midnight rules

Hard · sessionisation · vectorisation · event streams

Sessionise a raw event stream. Input: a DataFrame with visitor_id, user_id (often NULL), occurred_at_utc and event_name, unsorted, up to 5 million rows. A session breaks when the gap from that visitor's previous event exceeds 30 minutes, and is force-closed at UTC midnight so no session spans two calendar dates. A gap of exactly 30 minutes does not break. Emit one row per session with session_id, visitor_id, the user_id as of the last event in the session, started_at_utc, ended_at_utc, session_date, duration_seconds and event_count. Vectorise; do not loop per visitor.

Approach

1. Sort by ['visitor_id', 'occurred_at_utc', 'event_id'] once, then express the whole problem as one boolean vector: a row starts a new session when the visitor changed, or the gap exceeds 30 minutes, or the UTC date differs from the previous row's UTC date. Cumsum that vector and you have the session key.
2. Get the comparison direction right on the gap: the rule is strictly greater than 1800 seconds, so an event at exactly 1800 seconds continues the session. Write it as gap > pd.Timedelta(minutes=30), and make the tie a test case rather than an assumption.
3. Derive the midnight break from the date change, not from inserting synthetic boundary rows. A date change implies a break even when the gap is two seconds, which is precisely the force-close rule and is why the two conditions are ORed rather than one subsuming the other.
4. Aggregate with a single groupby on the session key: min and max of occurred_at_utc, size for event_count, and last for user_id, which is correct because the frame is already sorted so 'last' is the final event in the session. That is the identity-as-of-session-end rule.
5. Compute duration_seconds as (max - min).dt.total_seconds(), which makes a single-event session 0 seconds. Say so explicitly, because a downstream mean session duration is sensitive to whether single-event sessions are 0 or excluded.

Follow-up

• Sessions are used as the denominator of a conversion rate. How does moving the inactivity gap from 30 to 45 minutes move that rate, and in which direction?
• A visitor's clock is 40 minutes ahead, so their events arrive with future occurred_at values. What does your sessioniser do, and what would you rather it did?
• The same person signs up mid-session on mobile and continues on desktop. How many sessions and how many users does your output show, and is that the right answer?

Audit a one-day event extract for structural defects

Easy · data quality · late arrival · pandas

You receive a one-day extract of fct_event as a DataFrame with event_id, occurred_at_utc, received_at_utc, visitor_id, user_id, account_id, event_name, is_bot_flagged and surface. Write a function returning one row per data-quality rule with the rule name, the failing row count and the failing share of the extract. Cover at minimum: duplicate event_id, received_at_utc earlier than occurred_at_utc, occurred_at_utc later than the extract's maximum received_at_utc, account_id present while user_id is NULL, and rows whose occurred_at date differs from their received_at date. Do not drop rows; report only.

Approach

1. Compute the extract's own reference clock first: max(received_at_utc). Wall-clock now() is wrong here because the extract may be replayed days later, which would turn every row into a future-dated failure.
2. Express each rule as a boolean Series over the same index so the checks compose, then aggregate with .sum() and divide by len(df). Building a list of (name, mask) pairs keeps the rule set extensible and keeps one code path for counting.
3. For the duplicate rule, decide and state the convention: df.duplicated('event_id', keep=False).sum() counts every member of a duplicated group, df.duplicated('event_id').sum() counts only the surplus copies. Either is defensible; an unstated choice is not. The rest of this item assumes keep=False.
4. Treat received_at < occurred_at as clock skew, not corruption: occurred_at is client-supplied. Separate it from the date-mismatch rule, which is the one that actually breaks a daily metric keyed on occurred_at.
5. Know which rules imply which before you read the counts. A row whose occurred_at exceeds max(received_at_utc) has its own received_at no later than that maximum, so it is necessarily a clock-skew row as well: the future-dated mask is a subset of the skew mask, always. Neither is a subset of the date-mismatch mask, because skew of a few minutes inside one UTC date mismatches nothing.
6. Return a tidy DataFrame sorted by failing_share descending, and add a boolean column saying whether the rule should block publication, so the output is a decision rather than a list of numbers.

Worked solution (20 min)

1. Parse both timestamp columns with utc=True and assert the dtype, since a silently-object column makes every comparison string-wise and wrong.
2. Set ref = df['received_at_utc'].max() and build the five masks against it.
3. Assemble results as pd.DataFrame(rows) with columns rule, failing_rows, failing_share, blocks_publication.
4. Keep the masks addressable (a dict of name to Series) rather than only their sums, so the overlap between rules can be asserted rather than assumed.
5. Verify the function is pure: assert the input frame's shape is unchanged after the call.

Expected result: A DataFrame with one row per rule, failing_share equal to failing_rows divided by len(df) for every row, and the input frame returned unmodified. The rules overlap rather than partition the extract, and one containment is structural: every future-dated row is also a clock-skew row. The counts therefore must not be summed or presented as a total.

Verify it yourself

• Plant violations deliberately and assert an exact expected vector rather than assuming one hit per rule. On a 10-row frame carrying two rows that share an event_id, one same-date skew row, one row whose occurred_at sits an hour past max(received_at_utc), one row with account_id set and user_id NULL, and one row whose occurred_at falls on the previous UTC date: duplicate_event_id is 2 under the keep=False convention, clock_skew is 2 (the planted skew row plus the future-dated row, which cannot avoid tripping it), future_dated is 1, orphan_account is 1, date_mismatch is 1.
• Assert the containment directly rather than by counting: (future_dated_mask & ~clock_skew_mask).sum() is 0. A non-zero value means the reference clock was not max(received_at_utc).
• Feed a clean frame and confirm all counts are 0 and no rule errors on empty masks.
• Confirm len(input) is identical before and after, since the deliverable is a report and not a cleaned frame.

Follow-up

• The date-mismatch count is 2.1 percent on this extract. What late-arrival rule would you write for a daily metric, and how many days would you hold the number open?
• Duplicate event_id values appear only on the 'core_action_completed' event. What upstream cause would you check before deduplicating?
• Which of these rules should fire an alert at the pipeline, and which should only appear in a weekly review?

Provide an example of a complex SQL query you have written and explain its purpose.

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

• What breaks if events arrive late or out of order?
• How would you verify this result without re-running the same query?

How would you optimize a SQL query that is running slowly?

Medium · sql and data manipulation

Approach

1. Check whether any join is one-to-many before aggregating, or the sums inflate.
2. State the window function and its partition and ordering out loud before writing it.
3. Say which table is the grain you start from, and join outward from it.

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?

Describe how you would write a recursive function to solve a problem.

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

• What breaks if events arrive late or out of order?
• How does the query change if the join becomes one-to-many?

Paying accounts with no active seat in 28 days

Medium · anti-join · null-semantics · retention-risk

dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.

Approach

1. Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
2. Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
3. Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
4. Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
5. Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.

Worked solution (25 min)

1. Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
2. Write the active-account spine and the 28-day activity CTE.
3. Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
4. Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
5. Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.

Expected result: One row per silent active account with seats_licensed and days_since_last_core_action, ordered by seats_licensed descending. Accounts that never emitted a core action show NULL and are reported as a separate group rather than as the worst churn risks.

Verify it yourself

• Silent accounts plus accounts with at least one active user equals the total count of active non-internal accounts.
• The NOT IN variant returns zero rows, which is the demonstration that a NULL is present.
• No account_id appears twice in the output.
• Every days_since_last_core_action is either NULL or greater than 28.

Follow-up

• How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
• Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
• Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?

How would you approach building a recommendation system for an e-commerce platform?

Medium · product sense and metrics

Approach

1. Decompose the metric into the rates that drive it, and say which one you would check first.
2. Restate the decision this analysis has to support, and who acts on the answer.
3. Name one primary metric, then the guardrail that stops it being gamed.

Follow-up

• How would you detect that the metric is being gamed rather than genuinely improving?
• What would you do if the primary metric and the guardrail moved in opposite directions?

Given a dataset with missing values, what steps would you take to clean it?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Restate the decision this analysis has to support, and who acts on the answer.
3. State what result would change your recommendation, so the answer is falsifiable.

Follow-up

• How would you detect that the metric is being gamed rather than genuinely improving?
• Which segment would you cut first, and what would that rule out?

How do you prioritize tasks when managing multiple projects?

Medium · product sense and metrics

Approach

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

Follow-up

• How would you detect that the metric is being gamed rather than genuinely improving?
• Which segment would you cut first, and what would that rule out?

How would you design an A/B test for a new feature in a mobile application?

Medium · a/b testing and experimentation

Approach

1. Name the randomisation unit first; it decides the variance and what the test can detect.
2. Say whether units interfere with each other, and switch design if they do.
3. State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

• How would you handle interference between treated and control units?
• What would you conclude if the result is positive but the test is underpowered?

Discuss the difference between supervised and unsupervised learning.

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. Clarify what is being asked and what a complete answer would contain.

Follow-up

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

Choose one success number for a homepage redesign

Easy · conversion · guardrails · mix effects

A homepage redesign is ready to test. Marketing wants visit-to-signup conversion as the single success metric: distinct fct_session.visitor_id with a 'signup_completed' event in fct_event, over distinct visitor_id with a session started in the window where is_bot_flagged = FALSE and consent_state <> 'denied'. Sessions carry referrer_channel and device_type. Argue for or against that metric, name what you would decide on instead, and give the one guardrail you would refuse to ship without. Deliverable: the metric, the guardrail, and the failure mode in writing.

Approach

1. List everything that moves this rate without the page changing: referrer_channel mix, device_type mix, cookie lifetime and bot-rule changes, then pre-register the segments you will decompose on so the decomposition is not chosen after seeing the result.
2. Argue that signup is an intermediate outcome the homepage can inflate by over-promising, and propose activated signups per 1,000 eligible visitors — the same numerator further restricted to users clearing the seven-day activation bar — as the number the decision actually rests on.
3. Explain the consent exclusion rather than copying it: a 'denied' session can never be joined forward to a user, so leaving it in the denominator puts visitors there who have no path into the numerator and depresses the level permanently.
4. Separate what the metric can and cannot be used for: inside a randomised comparison over one window it is fine, but as a trend line across a platform privacy change it will step down because visitor_id resets more often, with no behaviour change behind it.
5. State the trade you would accept in advance: a smaller conversion gain with flat activation beats a larger conversion gain with activation down, and write the threshold before the readout.

Worked solution (15 min)

1. Restate the proposed metric and list every input that can move it with the page unchanged.
2. Compute the mix-held-fixed counterfactual: reweight this period's per-segment rates by last period's segment volumes, and report the difference against the pooled rate as the mix effect.
3. Define the decision metric: activated signups per 1,000 eligible visitors, with the seven-day activation predicate spelled out.
4. Write the guardrail and the explicit trade-off sentence you would put in the ship decision.
5. Record the denominator exclusions with the reason for each, not just the predicate.

Expected result: Conversion kept as a diagnostic, activated signups per 1,000 visitors named as the decision metric, a specified mix decomposition, and one guardrail carrying a written trade-off.

Verify it yourself

• Weekly rates rolled up by re-summing numerator and denominator differ from the average of the weekly rates whenever weekly traffic is uneven. If the two agree, a rate-of-rates has been computed somewhere.
• Per-segment numerators must sum to the pooled numerator. If they do not, a visitor is appearing in two segments and the segmentation is not a partition.
• Activation can only be read eight days after the last signup in the window, so the earliest honest decision date is eight days after the test stops.

Follow-up

• The redesign wins overall but the entire gain sits in paid social. What do you do, and what would change your mind?
• How do you roll four weekly conversion rates up to a month, and why does the obvious way give a different answer?
• Where does holding the segment mix fixed stop working as a correction?

Gross revenue churn doubled with no cancellations behind it

Hard · billing-periods · fx · revenue-churn · decomposition

Gross monthly revenue churn computed from fct_subscription_period doubled from 1.8% to 3.6% in one month. The support queue shows no rise in cancellations and renewals look normal. You have fct_subscription_period with subscription_id, account_id, period_start_utc, period_end_utc, mrr_cents, mrr_cents_constant_fx, seats_billed, period_status, change_reason and canceled_at_utc, plus dim_account. Decompose the 1.8-point rise into named mechanisms, size each in points of the headline, and state the remainder you cannot explain.

Approach

1. Reconstruct the numerator row by row and group it by change_reason before arguing about causes. A mid-period plan or seat change closes the current period row and opens a new one, so any implementation that treats a closed row as lost revenue books upgrades, downgrades and seat changes as churn; the change_reason breakdown of the numerator makes that visible in one query.
2. Check the recognition timestamp. Churn belongs at period_end_utc, because revenue continues until the period ends, not at canceled_at_utc when the button was pressed. Then count period_end_utc rows per month across thirteen months: annual cohorts concentrate their period ends in the month twelve months after they were signed, so a spike that repeats in the same month last year is seasonality in the book, not an event this month.
3. Recompute the whole numerator and denominator on mrr_cents_constant_fx. If the constant-currency figure is materially flatter, the move is an exchange-rate translation and belongs nowhere in a churn narrative.
4. Check period_status handling. Rows with period_status = 'past_due' are dunning, not cancellation; a status-based rule counts them as loss while a period-end rule does not, and a dunning backlog can move the number by itself.
5. Express every mechanism in points of the headline, sum them, and print the residual explicitly next to the month-to-month standard deviation of the prior twelve months. A decomposition without a stated remainder is a story, not an accounting.

Follow-up

• Which of these mechanisms should be fixed in the metric definition and which should be reported as a genuine business fact?
• How would you present a month whose churn is dominated by an annual cohort anniversary without the audience concluding the business is deteriorating?
• What would you change so that an upgrade can never enter the churn numerator again, and how would you test that it worked?

Suggested practice checklist

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.

Day 1 — Fix 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.

Day 2 — One 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.

Day 3 — Only 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.

Day 4 — One 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.

Day 5 — Your 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.

Day 6 — The 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.

Day 7 — Taper

• 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.

Bring numbers, including the ones you are unsure of

An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.

What motivates you to excel in your work as a Data Scientist?

Medium · behavioural and stakeholder questions

Approach

1. Close with what you would do differently, concretely.
2. Pick a story where you drove the decision, not one where you observed it.
3. Quantify the outcome, including what you would not claim credit for.

Follow-up

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

Defend a flat experiment readout against a post-hoc segment

Medium · experiments · segmentation · pushback

A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.

Approach

1. Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
2. Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
3. Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
4. Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
5. Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.

Follow-up

• The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
• Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
• Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?

Turn an ambiguous onboarding question into a measurable metric

Easy · scoping · metric definition · stakeholder

Two days before a planning review, a director asks whether onboarding is working. You have dim_user (account_created_at_utc, signup_surface, is_internal), fct_event (is_core_action, flow_id, flow_instance_id, event_name, occurred_at_utc, received_at_utc) and fct_session. No further meeting with the director is possible before you start work. Deliver three clarifying questions you would send in writing, the metric you will compute in the meantime with its numerator, denominator, window and exclusions, and one sentence naming the question you are deliberately not answering.

Approach

1. Recognise what is being probed: whether you convert a goal into a computable predicate without stalling for requirements or guessing in silence. Listing clarifying questions is the generic answer; shipping a defensible default alongside them is the strong one, because the review is in two days and it will happen with or without you.
2. Infer the decision behind the request. A question about whether onboarding works, arriving before a planning cycle, usually means whether to staff it next quarter. That points at a rate with visible headroom over several cohorts, not at a descriptive dashboard.
3. Write the three questions so that each one changes the SQL. Which population, all signups or only self-serve from dim_user.signup_surface. What counts as working, reaching a core action or completing the onboarding flow_id. Against what bar, last quarter's cohorts or a stated target.
4. Propose the default explicitly: seven-day activation on weekly signup cohorts. Numerator, users with is_core_action = TRUE events on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Denominator, the signup cohort with is_internal = FALSE. Publish with an eight-day lag, and state that the two-distinct-days threshold is a frozen choice rather than a discovery.
5. Name the exclusion in the same breath as the number. The series shows whether users activate; it does not establish that onboarding caused the level, which needs a staged rollout or an experiment.

Follow-up

• The director replies that they meant the onboarding flow specifically, not activation. What changes in the query and in the caveats?
• Your cohort metric needs an eight-day lag and the review is in two days. What do you present, and how do you label it?
• Two of your three questions come back unanswered. Which one do you refuse to proceed without?