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Alibaba Group Data Scientist Interview Questions & Guide 2026

Data scientist interview prep for Alibaba Group: original practice on experiment design, SQL and metric reasoning, with worked solutions.

The 60-second brief

A Data Scientist at Alibaba Group operates at the intersection of massive-scale e-commerce, cloud computing, logistics, and financial technology. Your primary mission is to transform petabytes of unstructured and structured data into actionable business intelligence, optimized algorithms, and predictive models. Whether you are embedded in Taobao, Tmall, Alibaba Cloud (Aliyun), or Cainiao, your work directly impacts hundreds of millions of active users and millions of merchants globally.

Most of the loop measures decision-making under uncertainty rather than recall. You are scored on whether you state your assumptions, commit to an estimate you can defend, and say explicitly what evidence would change it.

Focus areas

Pick a randomisation unit that respects interference
Decompose a metric move by segment and mix
Separate novelty effects from durable behaviour change

Overview

The scale of Alibaba Group requires data scientists to build robust, highly scalable solutions rather than theoretical prototypes. You will design, evaluate, and deploy machine learning models that handle real-time recommendation engines, dynamic pricing, fraud detection, and supply chain logistics. To succeed, you must possess deep technical capabilities, a strong product sense, and the resilience to operate in an incredibly fast-paced, high-performance environment.

For those who thrive on complex, high-impact data challenges, this role offers unprecedented opportunities. You will collaborate closely with software engineers, product managers, and business leaders to solve problems that are unique to one of the world's largest digital economies.

The Alibaba Group interview process

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

Online 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

Technical Phone Screening

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

Technical Video Interviews

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

Hiring Manager Conversation

This conversation decides whether you can be handed a problem nobody has finished defining and left alone with it for a few weeks. The manager is listening for how you behave when the brief is thin: what you clarify before starting, and what you settle on your own rather than escalating. Most candidates over-index on technical depth here and under-describe the decisions they actually owned. Say who wanted the work, what you chose not to do, and where you would have stopped and asked. A clean account of your own judgement carries this round further than a longer project list.

What to demonstrate

Whether you can name a decision that was yours alone, as opposed to one the team arrived at
How you respond to a request that arrives with no success metric attached to it
Whether the effort you estimate for a piece of work matches the work you just described doing
What you escalate, and how long you sit on a problem before you do

How to prepare

For each project you plan to raise, write one sentence saying what would not have happened if you had not been on it, and check that the sentence is about an outcome rather than an artefact
List the decisions in your last project that were genuinely yours, and for each one write down the option you rejected and why
Prepare the project that went badly: the point at which you knew, who you told, and what it cost before it was caught

HR Interview

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

Where candidates lose points

Comparing cohort retention curves of different maturities, or building the curve from users who are still present

A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.

Watching an experiment daily and stopping when it crosses significance

A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.

Reading experiment results before checking the arm split

Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.

Dropping rows with missing values without naming the mechanism

Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.

Questions & practice

Explain the mathematical and practical differences between L1 (Lasso) and L2 (Ridge) regularization.

Medium · statistics and probability

Approach

Write down the assumption the method needs before you use the method.
Say what the estimate is of, and over what population it generalises.
Quantify uncertainty explicitly rather than reporting a point estimate alone.

Follow-up

What sample size would you need to detect an effect half this size?
How would you explain this result to someone who does not know statistics?

What is a Support Vector Machine (SVM), and how does the kernel trick allow it to handle non-linear decision boundaries?

Medium · statistics and probability

Approach

Say what the estimate is of, and over what population it generalises.
Translate the result into the decision it informs, in one plain sentence.
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?
How would you explain this result to someone who does not know statistics?

What are the key assumptions of linear regression, and how do you test for multicollinearity in a high-dimensional feature space?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Explain the architecture of a transformer-based NLP language model and how self-attention mechanisms function.

Medium · machine learning and modelling

Approach

Check what information would not exist at prediction time, and exclude it.
Set a baseline first, so any model has something honest to beat.
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?
What would you monitor after launch to know the model is still valid?

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

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.
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.
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.
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.
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.

Worked solution (35 min)

df = df.sort_values(['visitor_id','occurred_at_utc','event_id']).reset_index(drop=True).
new_visitor = df.visitor_id.ne(df.visitor_id.shift()); gap = df.occurred_at_utc.diff(); new_day = df.occurred_at_utc.dt.date.ne(df.occurred_at_utc.dt.date.shift()).
is_start = new_visitor | (gap > Timedelta(minutes=30)) | new_day; df['session_key'] = is_start.cumsum().
g = df.groupby('session_key'); out = g.agg(visitor_id=('visitor_id','first'), user_id=('user_id','last'), started_at_utc=('occurred_at_utc','min'), ended_at_utc=('occurred_at_utc','max'), event_count=('event_id','size')).
out['session_date'] = out.started_at_utc.dt.date; out['duration_seconds'] = (out.ended_at_utc - out.started_at_utc).dt.total_seconds(); assign session_id from the sorted index.

Expected result: One row per session where session_count equals is_start.sum(), event_count sums exactly to len(input), every session's started_at and ended_at share one UTC date, and duration_seconds is at least 0 and strictly less than 86400.

Verify it yourself

out['event_count'].sum() equals len(df); a mismatch means rows were lost to a join or a dropna.
started_at_utc.dt.date equals ended_at_utc.dt.date on every row.
For a planted visitor with events at 12:00:00 and 12:30:00 exactly, one session is emitted; move the second to 12:30:01 and two are emitted.
For a planted visitor with events at 23:59:50 and 00:00:10, two sessions are emitted on consecutive dates despite a 20-second gap.

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?

Write a SQL query using window functions to find the top-performing merchant categories by daily transaction volume.

Medium · sql and data manipulation

Approach

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

Follow-up

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

Implement a basic hash table from scratch, explaining how you would handle collisions.

Medium · sql and data manipulation

Approach

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

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?

Given a dataset of user clickstreams, write a Python script to identify the longest continuous path of product views.

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
State the window function and its partition and ordering out loud before writing it.

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?

Seven-day activation rate by weekly signup cohort

Easy · cohorts · activation · date-bounds

dim_user holds user_id, account_created_at_utc, is_internal. fct_event holds user_id, occurred_at_utc, is_core_action. A user is activated when core-action events fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). Return, for the last twelve complete weekly signup cohorts, the cohort week, cohort size, activated users and the activation rate. Exclude is_internal users. Every signup in the cohort week stays in the denominator, including users who never returned.

Approach

Start from dim_user as the denominator spine with is_internal = FALSE and DATE_TRUNC('week', account_created_at_utc) as the cohort key. Driving the query from the event table instead would silently condition on having events and delete the entire non-activating population.
Join fct_event on user_id with is_core_action = TRUE and a per-user bound, occurred_at_utc >= u.account_created_at_utc AND occurred_at_utc < u.account_created_at_utc + interval '7 days'. The bound is correlated to each user's own signup timestamp, not a single global date range.
Aggregate per user with COUNT(DISTINCT occurred_at_utc::date) >= 2, then LEFT JOIN that back onto the spine and COALESCE the flag to FALSE so non-activators contribute a zero rather than vanishing.
Restrict the published cohorts to those whose week ended at least eight days ago. A cohort younger than that has not finished its seven-day window, so its rate is mechanically low and reads as a decline.
Roll up by summing the numerator and denominator per cohort week, and state the two-distinct-days threshold next to the number since it is a choice that re-bases the whole history if changed.

Worked solution (20 min)

Write the cohort spine and confirm its total equals the count of non-internal signups in the date range.
Write the per-user distinct-active-days CTE with both interval bounds and inspect a handful of users manually.
LEFT JOIN, COALESCE the flag, aggregate to cohort week.
Apply the eight-day publication lag and drop the incomplete cohort.
Re-run with a closed upper bound (<= +7 days) and note how many users change state, to show the boundary is doing work.

Expected result: Twelve rows, one per cohort week, each with cohort_size, activated and rate. Cohort sizes sum to the total non-internal signup count across those twelve weeks. No cohort is younger than eight days.

Verify it yourself

activated is never greater than cohort_size in any row.
A user with three core actions on a single day is not counted as activated.
Removing the LEFT JOIN in favour of an inner join raises every rate, which demonstrates the denominator was being conditioned on survival.
Cohort sizes sum to an independent COUNT(*) over dim_user for the same range.

Follow-up

Why two distinct days rather than one event? What happens to the published history if someone changes it to three?
Invited seats and SSO-provisioned users get an account_created_at_utc at provisioning and may never sign in. Should they be in this denominator?
The rate rose 3 points this week. What do you check before believing it?

How do you implement and optimize a queue data structure, and in what real-time data streaming scenarios would you use it at Alibaba Group?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.
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?

A surrogate for twelve-month value inside a three-week test

Hard · surrogate metrics · experimentation · long-term value

A monetisation change — an earlier and harder paywall — will be tested for three weeks. The decision quantity is twelve-month cumulative net revenue per acquired account, which cannot be observed in three weeks. Available: fct_event, fct_session, fct_subscription_period (mrr_cents_constant_fx, period_status, change_reason, is_first_paid_period), dim_account, and fct_experiment_exposure (unit_type, unit_id, variant, first_exposed_at_utc, is_in_analysis_population). Construct a surrogate index readable at three weeks, state the assumption that makes it valid, name the mechanism that breaks it, and give the pre-registered rule for refusing to decide. Deliverable: the index, the assumption, and the refusal rule.

Approach

State the surrogacy condition before building anything: a surrogate is valid only if the treatment's entire effect on twelve-month revenue runs through it. Then say where it fails here, because a paywall moves short-run revenue directly and long-run revenue through churn and through who becomes a payer at all.
Fit the index on history rather than on intuition: regress twelve-month cumulative net revenue per account on features observable by day 21 — first-paid flag, activation days, week-3 core-action days, seats billed — using cohorts old enough to have a twelve-month outcome, and hold out a later cohort.
Report the out-of-sample fit as the headline rather than the point estimate: the held-out R-squared and the calibration of predicted against actual deciles are what license any use of the index, and a decile plot catches the mis-calibration an R-squared hides.
Name the bias direction explicitly: the relationship was estimated under the old paywall, so under a harder paywall the marginal payer is a different person and the index over-predicts their value, biasing the treatment arm optimistic.
Surround the index with the direct three-week readouts it cannot contain — revenue per exposed unit, cancel-within-first-period rate, and the free-side signup and activation counts — and pre-register a numeric refusal rule, because the failure mode of a surrogate is being used confidently in exactly the case it was not fit for.

Worked solution (40 min)

Assemble the training set: accounts with first_paid_at_utc at least twelve months old, their realised twelve-month cumulative net revenue, and the day-21 features.
Fit on pre-change cohorts, evaluate on a held-out later cohort, and report both out-of-sample R-squared and predicted-versus-actual decile calibration.
Write the surrogacy assumption as a falsifiable sentence, then write the specific mechanism in this treatment that violates it.
Specify the direct arm readouts on fct_experiment_exposure with is_in_analysis_population = TRUE: revenue per exposed unit, cancel-within-first-period rate, and free-side signup and activation counts.
Write the refusal rule with numbers attached, covering both the fit threshold and the free-side divergence case.

Expected result: A fitted surrogate with a reported out-of-sample R-squared and calibration, a written surrogacy assumption plus the mechanism that violates it, the resulting bias direction, and a numeric refusal rule agreed before launch.

Verify it yourself

Standard errors are clustered on fct_experiment_exposure.unit_id, the identifier of the randomisation unit. unit_type is the kind label for that unit — a single value such as 'user' across the whole table — so clustering on it collapses the design to one or two clusters and carries no information about who was randomised together. A per-event or per-session standard error on a user-randomised test is the opposite error: too small, and it will manufacture significance.
The analysis population is the exposed set with is_in_analysis_population = TRUE, and a sample-ratio check against the intended split passes before any outcome is read.
The index applied to the control arm should reproduce the historical relationship. If control is already mis-calibrated, the treatment estimate is not interpretable at all.

Follow-up

What evidence would make you trust a surrogate for this particular change rather than a different one?
The index reads plus 8 percent while three-week cancel-within-period is up. Which do you act on, and what do you tell the decision-maker?
How would you size this test, on what unit, and what does that do to the three-week horizon?

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

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.
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.
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.
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.
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

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

Explaining a method to someone who will not follow the maths

Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.

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

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.
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.
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.
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.
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?

Explain a wide interval to a non-technical executive

Medium · communication · uncertainty · pricing

A pricing change is under consideration. Your best estimate of its effect on trial-to-paid conversion is a 1.8pp drop, with a 95% interval from a 4.6pp drop to a 1.0pp rise, read from a geo holdout rather than a randomised test. An executive preparing a board slide asks you for 'the number'. You have ninety seconds and one slide, and the words confidence interval, p-value and significance are not usable with this audience. Deliver the slide headline, the single supporting line, and what you say aloud.

Approach

Recognise what is being probed: whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it. The generic answer promises to explain the interval in plain English; the strong one replaces the question 'what is the number' with 'across this range, where does the decision change'.
Find the threshold before you draft anything. Ask what the pricing case assumes, then compute the conversion drop at which the higher price stops adding revenue: price uplift on the conversions kept against the revenue lost from conversions forgone. That single figure is what makes the range legible.
Restate the estimate and both bounds in the unit the audience already reasons in. Convert percentage points into monthly first-paid conversions at current trial volume, then into mrr_cents_constant_fx, so the slide reads as money per month rather than as statistics.
Place the range against the break-even and say which part of it sits on each side. If most of the range clears the threshold, that is a recommendation to proceed with a monitoring plan; if the range straddles it, that is a recommendation to narrow the range first.
Name what would narrow it and what that costs in weeks, then give one recommendation with an explicit condition for revisiting it. Uncertainty stated without a next step is read as indecision and the midpoint gets used anyway.

Follow-up

The executive says to give the midpoint and they will manage the risk. What do you do?
How does the slide change if the interval were a 4.6pp to 0.2pp drop, with no positive outcomes in range?
Why is a geo holdout the credible read here rather than the attributed channel numbers you already have?

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

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.
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.
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.
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.
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?