Bloomberg Data Scientist Interview Questions & Guide 2026

The 60-second brief

At Bloomberg, data is not just an asset—it is the core of the entire business. As a Data Scientist, you will operate at the intersection of finance, technology, and media, building the models and analytical tools that power the world’s most influential financial platform. Whether you are working on the Bloomberg Terminal, optimizing advertising delivery for media products, or developing predictive analytics for global markets, your work will directly influence how decision-makers interpret complex information in real time.

SQL is seldom the hardest round and is often the one that eliminates people. The working bar is usually window functions, correct deduplication, and joins that do not silently fan out rows, rather than obscure syntax.

Focus areas

• Decompose a metric move by segment and mix
• Turn a vague request into a measurable question
• Pick a randomisation unit that respects interference

Overview

The data ecosystem here is vast and highly sophisticated, requiring you to handle massive, high-velocity datasets. Unlike traditional tech companies where data science might be siloed, data science at Bloomberg is deeply integrated with engineering, product management, and business operations. For instance, working alongside roles like the Senior Product Manager, Advertising Measurement and Data Science, you will design advanced measurement frameworks that attribute value and uncover insights across complex global campaigns.

To succeed in this role, you must possess a rare combination of rigorous quantitative skills and practical business acumen. You will be expected to translate ambiguous financial and behavioral questions into structured data experiments, write production-grade code, and communicate your findings to both technical peers and non-technical stakeholders. It is a highly collaborative, fast-paced environment where your models will have an immediate, visible impact on global financial markets.

The Bloomberg interview process

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

HR Screening

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

Team Leader Chat

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.

Core 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

Data Diagnostic and Business Case

Underneath the business framing, this round is usually asking whether you can turn a fuzzy goal into a quantity that could be computed from data such a business would plausibly hold. That means a metric with a stated numerator, denominator, eligibility rule and time window, plus an honest account of the conditions under which it would mislead you. Answers come apart when a candidate names a familiar metric and never defines it, because every follow-up then lands on an ambiguity that was left open and the candidate has to invent the definition under pressure.

What to demonstrate

• Whether a named metric arrives with its denominator, eligibility rule and window attached rather than assumed
• Whether the measure follows from the mechanism you proposed, or is a recognisable metric retrofitted to it afterwards
• Whether you name a guardrail that would reveal the gain came from somewhere you did not want it to come from
• Whether you can say what data the plan requires and what you would settle for if that logging were never implemented

How to prepare

• Take five metrics you reach for by reflex and write each as one sentence containing numerator, denominator, eligibility rule and time window. The ones you cannot finish are the ones that will fail under follow-up.
• For a product you use daily, write the measurement plan you would propose for a change to it: primary metric, one guardrail, the unit of analysis, and the table the numbers would come from.
• Practise the substitution question. For three metrics you like, write what you would measure instead if the event you depend on were not being logged.

Final Round

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

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.

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Reading an observational correlation as a causal effect

Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.

Questions & practice

If you roll two fair six-sided dice, what is the probability that the sum of the numbers is greater than eight?

Medium · statistics and probability

Approach

1. Translate the result into the decision it informs, in one plain sentence.
2. Quantify uncertainty explicitly rather than reporting a point estimate alone.
3. Write down the assumption the method needs before you use the method.

Follow-up

• Which assumption here is most likely to be violated in practice?
• What sample size would you need to detect an effect half this size?

Solve a logical sequence puzzle to demonstrate your step-by-step mathematical deduction process.

Medium · statistics and probability

Approach

1. Quantify uncertainty explicitly rather than reporting a point estimate alone.
2. Write down the assumption the method needs before you use the method.
3. Sanity-check the answer against a simple bound or a simulated case.

Follow-up

• What sample size would you need to detect an effect half this size?
• Which assumption here is most likely to be violated in practice?

Explain how you would implement a binary search algorithm and describe its time complexity.

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. Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

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

Implement seven-day activation from its written definition

Medium · metric definition · cohorts · pandas · joins

Implement the seven-day activation rate. Inputs: dim_user with user_id, account_created_at_utc and is_internal; fct_event with user_id, occurred_at_utc and is_core_action. A user activates when core-action events carrying a non-NULL user_id fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). The denominator is every non-internal user whose account_created_at_utc lands in the cohort week, including users with no events at all. Return one row per cohort week with numerator, denominator and rate, publishing only weeks whose last signup is at least eight days old.

Approach

1. Build the denominator first, from dim_user alone, filtered on is_internal = False. Deriving it from the join is the standard way to lose every user who never fired an event, which is exactly the population the metric is about.
2. Join events to users on user_id with a left join from the user side, then apply the window as a half-open interval: occurred_at >= created AND occurred_at < created + 7 days. The right bound is exclusive, so an event at exactly created + 7 days does not count.
3. Count distinct UTC dates per user, not distinct events. Floor occurred_at_utc to date before the nunique, and do it in UTC rather than local time so the threshold does not move with the user's country.
4. Apply the >= 2 threshold, aggregate to cohort week, and compute the rate by re-summing numerator and denominator per week rather than averaging any per-user or per-day rate. Fix the week anchor explicitly: cohort_week is the Monday of the signup week in UTC, which is what Postgres DATE_TRUNC('week') returns and what any SQL version of this metric will produce. In pandas, subtract dt.weekday days from the floored timestamp. If you reach for periods instead, the anchor that matches is to_period('W') (equivalently 'W-SUN'), whose weeks end Sunday and therefore start Monday; to_period('W-MON') labels weeks that end on Monday, so it runs Tuesday through Monday and its start_time is a Tuesday. Mixing the two shifts every cohort label by one day and silently moves Mondays into the previous week.
5. Suppress immature weeks: drop any cohort week whose maximum account_created_at_utc is within 8 days of the data cut, and return them as absent rather than as a partial number.

Worked solution (30 min)

1. users = dim_user[~dim_user.is_internal].copy(); created = users['account_created_at_utc']; users['cohort_week'] = created.dt.floor('D') - pd.to_timedelta(created.dt.weekday, unit='D'), which is the Monday-start week. The period spelling that agrees with it is created.dt.to_period('W').dt.start_time; 'W-MON' does not agree and is off by a day.
2. ev = events[events.is_core_action & events.user_id.notna()]; merge onto users on user_id with how='inner' for the numerator side only.
3. Filter to the half-open window, add ev_date = occurred_at_utc.dt.date, group by user_id and count distinct dates, keep users with >= 2.
4. numer = users.merge(activated_user_ids, how='left', indicator=True) then group by cohort_week and sum the indicator; denom = users.groupby('cohort_week').size().
5. rate = numer / denom; drop weeks where users.groupby('cohort_week')['account_created_at_utc'].max() > data_max - 8 days.

Expected result: One row per mature cohort week with numerator, denominator and rate, where denominator equals the count of non-internal signups in that week regardless of activity, numerator is less than or equal to denominator, and the most recent 1 to 2 weeks are absent rather than reported low.

Verify it yourself

• For a synthetic user with two core actions 90 minutes apart on the same UTC date, activation is False.
• For a synthetic user with one core action on day 0 and one at exactly created + 7 days, activation is False; move the second to created + 6 days 23 hours and it becomes True.
• Pin the week anchor with two users created at Sunday 23:59 UTC and the following Monday 00:01 UTC: they must land in different cohort weeks, and the Sunday user's cohort_week must be the Monday six days before them. to_period('W-MON') puts both in one period, which is the off-by-one this check exists to catch.
• The denominator summed across all published weeks equals the count of non-internal users created in that date range.
• Re-summing numerator and denominator to a monthly figure gives a different answer from averaging the weekly rates; the re-summed one is the metric.

Follow-up

• The threshold is 2 distinct days. What changes in the reported history if someone moves it to 3, and how would you publish that change?
• Invited seats and SSO-provisioned users have no pre-signup session. Should they be in this denominator at all, and what does including them do to the rate for sales-assisted accounts?
• How would you produce the same metric at account grain, and which of the two would you put on the dashboard?

Given an array of integers, write a function to find the first recurring element.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Say which table is the grain you start from, and join outward from it.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

• How would you verify this result without re-running the same query?
• How does the query change if the join becomes one-to-many?

How would you identify and remove duplicate records from a highly unstructured dataset using Python?

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

Walk me through how you would handle missing or corrupted values in a financial transaction dataset.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Weekly visit-to-signup conversion split by acquisition channel

Easy · funnel · conversion · aggregation

From fct_session (session_id, visitor_id, started_at_utc, referrer_channel, is_bot_flagged, consent_state) and fct_event (visitor_id, occurred_at_utc, event_name), compute visit-to-signup conversion for one ISO week, split by channel. session_id is the unique key of fct_session. Denominator: distinct visitor_id with a session starting in the week, is_bot_flagged = FALSE and consent_state <> 'denied'. Numerator: those visitors with a 'signup_completed' event in the same week. Label each visitor with the referrer_channel of their first session in the window. Return channel, visitors, signups and rate, plus one all-channel total row.

Approach

1. Build a visitor spine that is one row per visitor: filter sessions to the week, drop is_bot_flagged and consent_state = 'denied', then take the first session per visitor with ROW_NUMBER() OVER (PARTITION BY visitor_id ORDER BY started_at_utc, session_id) = 1 to carry the channel label. Collapsing to one row here is what makes the channel buckets mutually exclusive and the totals additive.
2. session_id is the unique key, so that ordering is total and the label is reproducible. If the table carried no unique key you would have to write an explicit tie rule instead, because two sessions on different channels at the identical timestamp would otherwise label the visitor differently between runs.
3. Attach the outcome as a semi-join (EXISTS on a signup_completed event for that visitor inside the same week) rather than a join to the event table, so a visitor who fires the event twice does not count twice and inflate the numerator past the denominator.
4. Aggregate with COUNT() as visitors and COUNT() FILTER (WHERE signed_up) as signups, and compute the rate as signups::numeric / NULLIF(visitors, 0) so an empty channel returns NULL rather than a division error.
5. Produce the total with GROUP BY GROUPING SETS ((channel), ()), which re-sums numerator and denominator for the total row. Averaging the channel rates gives a different and wrong number whenever channel volumes differ, which they always do.
6. Verify the spine before trusting the output: COUNT(*) must equal COUNT(DISTINCT visitor_id), and the per-channel visitor counts must sum to the total row.

Worked solution (20 min)

1. Write the filtered session CTE and check its row count against an unfiltered count, so you know how much volume the bot and consent filters removed.
2. Add the ROW_NUMBER first-session pick and assert one row per visitor.
3. Add the EXISTS outcome flag and aggregate with FILTER.
4. Add GROUPING SETS for the total and format the rate to four decimal places.
5. Spot-check one channel by hand: pull its visitor list, count signups directly, compare.

Expected result: One row per referrer_channel plus a total row, with visitors, signups and a decimal rate. Channels with zero signups appear with rate 0, not missing. The total rate lies strictly between the minimum and maximum channel rate unless one channel holds all the volume.

Verify it yourself

• Per-channel visitor counts sum exactly to the total row's visitor count.
• COUNT(*) equals COUNT(DISTINCT visitor_id) on the spine CTE.
• Replacing the EXISTS with a plain inner join changes the signup count only if duplicate signup_completed events exist; if it does change, say by how much.
• Every rate is between 0 and 1.
• Re-running the first-session pick with session_id removed from the ORDER BY returns the same labels; if it does not, visitors hold same-timestamp sessions on different channels and the tie rule is doing real work.

Follow-up

• The denominator is distinct visitors. If a browser release shortens cookie lifetime, what happens to this rate, and how would you tell that apart from a genuine drop?
• A visitor's first session is direct and their signup session is paid search. Your label says direct. When is that the wrong answer for the decision being made?
• How do you roll four weeks into a month, and why is averaging the four weekly rates wrong?

Given a dataset of user interactions, how would you write a Pandas script to calculate the monthly active users (MAU) and churn rate?

Medium · product sense and metrics

Approach

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

Follow-up

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

How do you optimize memory usage when loading a massive CSV file into a Pandas DataFrame?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. State what result would change your recommendation, so the answer is falsifiable.
3. Decompose the metric into the rates that drive it, and say which one you would check first.

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?

Estimate a threshold-triggered programme with regression discontinuity

Hard · regression discontinuity · instrumental variables · identification

Accounts reaching seats_licensed >= 25 in dim_account are automatically assigned a dedicated onboarding specialist; below 25 they are not. Leadership wants the programme's effect on 12-month net revenue retention and will not randomise coverage away from any account. Three years of dim_account and fct_subscription_period rows are available, including mrr_cents_constant_fx. Specify the design, the estimand it identifies, two threats that would invalidate it, and what changes when 8% of accounts below the threshold received a specialist anyway.

Approach

1. Set up a regression discontinuity on the running variable seats_licensed with a cutoff at 25, comparing accounts just below with accounts just above. The identifying assumption is continuity: absent the programme, expected 12-month NRR would be a continuous function of seat count through 25.
2. Name the estimand honestly and early. This is a local average treatment effect at 25 seats. It says nothing about a 5-seat or a 200-seat account, and that belongs in the first line of the answer rather than a footnote.
3. Estimate with local linear regression on each side, a triangular kernel, an MSE-optimal bandwidth and robust bias-corrected confidence intervals. Do not fit a high-order global polynomial; it imports weight from observations far from the cutoff and is known to manufacture discontinuities.
4. Test the two threats that actually apply. Manipulation: an account that wants the specialist can buy a 25th seat, which piles density just above the cutoff, so run a density test on seats_licensed around 25 and look for a spike at exactly 25. Bundling: if a price break, a plan tier or a support SLA also switches at 25 seats, the discontinuity measures the whole bundle, so check current_plan_tier and the price schedule at the cutoff.
5. Treat the 8% crossover as a fuzzy design. Treatment probability jumps at the cutoff without going from 0 to 1, so divide the jump in NRR by the jump in the probability of receiving a specialist. That is a Wald instrumental-variables estimator with the cutoff indicator as the instrument; it needs exclusion, which is exactly what the bundling check is about, plus monotonicity, and it narrows the estimand further to compliers at the cutoff.

Worked solution (45 min)

1. Fix the running variable as seats_licensed at the moment the assignment rule was evaluated, and fix the outcome as 12-month NRR computed from fct_subscription_period on mrr_cents_constant_fx for the account's cohort.
2. Plot mean NRR in one-seat bins on each side of 25 with a local linear fit. The picture comes before the estimate, because a discontinuity invisible in the binned plot is rarely real.
3. Run the density test at 25 and a continuity check on pre-cutoff characteristics such as billing_country, account_type and pre-programme MRR; these must be smooth through the cutoff.
4. Estimate the sharp RD with an MSE-optimal bandwidth and robust bias-corrected intervals, then repeat at half and double the bandwidth as a sensitivity check.
5. Estimate the first stage, the jump in specialist assignment at 25, and report the fuzzy estimate as the ratio with the estimand stated as the complier effect at the cutoff.

Expected result: A fuzzy regression discontinuity at 25 seats, estimated as the ratio of the NRR jump to the specialist-probability jump, identifying the local complier average effect at the cutoff under continuity, exclusion and monotonicity. With 8% crossover below the cutoff and near-full compliance above it, the first stage is about 0.92, so the fuzzy estimate is roughly 1.09 times the reduced-form jump and its standard error inflates by the same factor.

Verify it yourself

• Characteristics fixed before the cutoff is crossed must not jump at 25. If account_type mix or billing_country jumps, the local comparison is not as-good-as-random.
• The estimate should be stable across half and double the MSE-optimal bandwidth. Strong bandwidth sensitivity means the functional form is doing the work, not the discontinuity.
• The reduced-form jump divided by the first-stage jump must reproduce the fuzzy point estimate exactly; if it does not, the two are being computed on different samples.
• The effective sample inside the bandwidth, not the full account count, drives the standard error. Local linear RD typically needs on the order of three to four times the sample a randomised comparison would for the same power.

Follow-up

• The density test shows a spike at exactly 25 seats. Is the design dead, and what would you do next?
• You have 40,000 accounts but the bandwidth keeps 900. How does the detectable effect compare with a randomised comparison of the same nominal size?
• Seat counts change over time. Which value of seats_licensed is the running variable, and what breaks if you pick the wrong one?

Size every candidate cause of a trial-to-paid decline

Hard · cohort-maturity · experiment-readout · mix-shift · contribution-sizing

Trial-to-paid conversion on weekly trial-start cohorts from fct_subscription_period reads 3.1 points below the trailing eight-week mean for the three most recent cohorts. Three things happened in that window: a pricing experiment reached 50% of new trials, a payment processor migration added settlement delay, and paid_search spend tripled. Using fct_subscription_period, fct_experiment_exposure and dim_user, rank the causes by their contribution in points of the headline, state the remainder, and give the decision you would take on Monday.

Approach

1. Kill the immature cohorts first, because everything downstream is computed on them. The metric is lagged by the trial length plus a 14-day conversion window plus a settlement allowance, and a processor migration lengthens exactly that last term; recompute each cohort at a fixed cohort age rather than as of today, and confirm the newest cohort's value is still climbing day over day.
2. Hold the experiment analysis to the exposed population. Join fct_experiment_exposure on unit_id with is_in_analysis_population = TRUE rather than reading an assignment log, then check the variant split for a sample-ratio mismatch before believing any effect at all. Contribution to the headline is the variant effect multiplied by the exposed share, which is not the same number as the variant effect.
3. Decompose the cohort mix by dim_user.first_touch_channel using the same weight-times-rate arithmetic as any other mix question, so the paid_search increase is sized as a weight change at a measured conversion rate rather than asserted from the spend figure.
4. Convert all three to points of the headline, sum them, and print the residual against the historical week-to-week standard deviation of the metric. If the residual is inside that band, say so and stop looking; if it is outside, name what you would investigate next rather than leaving it implied.
5. Land the decision. Only one of the three is actionable on Monday, so state whether the experiment has accrued enough exposed units to stop at the pre-declared horizon, and state separately what the settlement-lag correction does to the published series and its lag rule.

Follow-up

• How do you choose the fixed cohort age, and what do you lose by choosing it too long?
• If the pricing variant is genuinely 1.2 points worse, does that settle whether to stop it? What else is on the other side of that decision?
• The trailing eight-week mean spans the processor migration. What is the right baseline instead?

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.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Describe a project where you faced highly ambiguous requirements. How did you structure your approach to deliver a successful outcome?

Medium · behavioural and stakeholder questions

Approach

1. Name the disagreement or constraint, and how you resolved it with evidence.
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

• How did you know the outcome was caused by your change?
• What would you do differently if you ran that project again?

Handle a request for numbers supporting a decision already made

Hard · integrity · framing · stakeholder

A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

Approach

1. Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
2. Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
3. Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
4. Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
5. Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
6. Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.

Follow-up

• The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
• What changes if the honest analysis says the sunset is clearly the wrong call?
• How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?

Disagree with a product manager's roadmap claim using data

Medium · causal inference · selection bias · stakeholder

A product manager proposes building a feature on the argument that accounts connecting an integration in week one retain three times better at week four. The figure is correctly computed from dim_user and fct_event, and it has already been shown to leadership. You have one scheduled 1:1 before the roadmap locks. Deliver the specific analysis you would run to test whether the relationship is causal, the result that would change your own mind, and how you open the conversation so that the PM is not put in the position of defending the number in public.

Approach

1. Recognise what is being probed: whether you can separate a number being right from an inference being wrong, and do it without costing the PM face. The generic answer recites that correlation is not causation; the strong one names the specific confound and proposes the cheapest design that could distinguish the explanations.
2. State the alternative concretely. Accounts that connect an integration in week one are accounts that already have a workflow and a technical owner, so week-one intent plausibly drives both the connection and week-four retention. The selection is on intent, which no amount of post-hoc adjustment observes.
3. Order the discriminating analyses by cost. First, condition on pre-connection activity by comparing retention within strata of week-one core-action count, which removes the crude version of the confound but not unobserved intent. Second, look for variation in integration availability that was unrelated to intent, such as a staggered release or an outage window. Third, an encouragement design that randomises a prompt to connect and reads the intent-to-treat effect on week-four retention, which is the only version that identifies an effect.
4. Run the timing check, because it is nearly free and it is the most persuasive single piece of evidence. If the retention advantage among connectors is already visible before any of them connected, the causal story is largely finished.
5. Pre-commit to what would change your mind and say it before you show anything: if the gap survives stratification and the encouragement arm moves week-four retention at all, the feature has a case and you will say so.
6. Open the 1:1 by agreeing with the true part, that the correlation is real and worth chasing, then ask what effect size the roadmap plan assumes. That makes the size of the claim the topic instead of its authorship.

Follow-up

• The encouragement test needs six weeks and the roadmap locks in two. What do you recommend in the interim?
• Stratifying on week-one activity closes half the gap. What do you conclude, and what do you still not know?
• How would you word this in the roadmap document so the PM's original number is reframed rather than deleted?