Interview guides · Moengage

Moengage Data Scientist Interview Questions & Guide 2026

Moengage data scientist prep on data pitfalls: selection bias, target leakage, and checking an outlier's provenance before you drop it.

The 60-second brief

As a Data Scientist at Moengage, you work on the company's mission to help brands personalize customer engagement at scale. You will work with massive datasets to derive actionable insights, build predictive models, and optimize the algorithms that power Moengage's customer engagement platform. Your work directly influences how Moengage's clients interact with their users across multiple channels, making your contributions highly visible.

Scope your preparation by the data you would actually touch, because the title will not tell you. A seat that lives in event logs and weekly readouts rewards fluency in aggregation and metric definitions; a seat that owns a model in production rewards fluency in train/serve skew, retraining cadence and drift monitoring. The fastest way to find out which one you are interviewing for is to ask what the team shipped last quarter and what it gets paged about.

Focus areas

Measure churn only on renewal-eligible accounts
Separate contracted seats from actively used seats
Analyse at the account grain, cluster errors

Overview

This role requires a blend of rigorous mathematical foundations and practical engineering prowess. You are not just building models in isolation; you are deploying solutions that must perform under real-world constraints. Whether you are improving churn prediction models, optimizing recommendation engines, or uncovering behavioral patterns, you will operate at the intersection of product strategy and technical execution. Moengage looks for individuals who are comfortable with ambiguity and motivated to solve complex problems in a fast-paced environment.

While accuracy is vital, Moengage interviewers prioritize your ability to explain your methodology and trade-offs. Candidates are expected to understand the "why" behind their models as much as the "how."

Where to focus your preparation

Treat the sections below as preparation areas and confirm the format with your recruiter.

Preparation focus

No round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.

What to demonstrate

Breadth across SQL, experimentation and product reasoning
Ability to state assumptions before choosing a method

How to prepare

Drill the practice exercises below and time yourself
Prepare three quantified stories about decisions you drove

Where candidates lose points

Randomising an experiment at the user level when users share an account

Two problems fire at once. Colleagues in one workspace see each other's work and talk to each other, so a treated user changes the behaviour of a control user in the same account, which violates the no-interference assumption and biases the estimate toward zero. Separately, outcomes within an account are strongly correlated, so the effective sample size is roughly n / (1 + (m - 1) * rho) for m users per account and intra-class correlation rho, not n. With rho around 0.3 and twenty users per account that is a design effect near 6.7, meaning a user-level confidence interval is about two and a half times narrower than it should be and results cross significance thresholds on noise alone. Randomise the account and cluster the standard errors.

Comparing accounts that received a sales or customer-success touch against those that did not

Assignment of coverage is deliberate and pulls in both directions at once: the largest accounts get a named owner because they are valuable, and the accounts showing distress get one because they are at risk. The comparison therefore mixes a strong positive selection with a strong negative one, and the naive estimate can come out with either sign depending on which assignment rule dominated during the period examined. Nothing about matching on observed size fixes this, because the risk signal that triggered coverage is usually the same signal that predicts the outcome. It needs either an actual randomised or staggered rollout of coverage, or a design built on a capacity constraint or territory boundary that assigns coverage for reasons unrelated to account health.

Solving silently instead of narrating the reasoning

Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.

Answering a product-sense question with a list of features

Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.

Questions & practice

Explain the bias-variance trade-off and how it influences model selection.

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Check what information would not exist at prediction time, and exclude it.
Set a baseline first, so any model has something honest to beat.

Follow-up

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

Explain the difference between bagging and boosting techniques.

Medium · machine learning and modelling

Approach

Pick an evaluation metric that matches the cost of each error type, not a default.
Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.

Follow-up

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

How do you evaluate the performance of a model beyond simple accuracy?

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.
Say how the offline result would be validated online before it is trusted.

Follow-up

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

Describe the mathematical intuition behind a specific algorithm, such as Random Forest or Gradient Boosting.

Medium · machine learning and modelling

Approach

Set a baseline first, so any model has something honest to beat.
Pick an evaluation metric that matches the cost of each error type, not a default.
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?

Quantify billable volume created by retries after server errors

Hard · telemetry-hygiene · pandas · unit-economics

From fct_api_request (request_id, account_id, endpoint, idempotency_key, is_retry, http_status, request_at, billable_units), measure how much billable volume in a 28-day window is retry traffic that followed a 5xx. Group requests into attempt chains by (account_id, endpoint, idempotency_key) ordered by request_at; a request is error-driven if any earlier attempt in its chain returned 5xx. Requests with a null idempotency_key cannot be chained, so report them as their own class rather than assuming each is unique. Return billable_units split into first-attempt, error-driven retry, other retry and unchainable, per account.

Approach

Split the population before measuring anything. A null idempotency_key is not a chain of one, it is an unknown; report its share of billable_units first, because if it is 40% of volume then the headline estimate is a lower bound and the deliverable has to say so.
Within chainable rows, sort by (account_id, endpoint, idempotency_key, request_at) and derive 'any earlier attempt failed' with arithmetic rather than a per-group lambda: with is5 = (http_status >= 500), the per-chain cumsum minus the row's own value is positive exactly when an earlier attempt in that chain returned 5xx. A groupby-apply gives the same answer and is unusable at five million rows.
Do not take is_retry as the definition. It is set by the client whenever an idempotency_key is resent, which covers retries after client-side timeouts and after 4xx as well; compute the flag yourself and then cross-tabulate it against is_retry, because the disagreement is a finding in its own right.
Aggregate billable_units by (account_id, class) and assert the classes sum to each account's total. The spine sets billable_units to zero on 5xx responses, so the failed attempt contributes nothing and the whole inflation sits in the successful retry that follows it.
Report the per-account share and look at its distribution, not the fleet total. One account in a retry storm dominates any blended figure, which is the same failure that makes a fleet-wide error rate useless.

Worked solution (40 min)

unchainable = df.idempotency_key.isna(); report df.loc[unchainable].groupby('account_id').billable_units.sum() before proceeding.
keys = ['account_id','endpoint','idempotency_key']; c = df[~unchainable].sort_values(keys + ['request_at'], kind='mergesort'); c['is5'] = (c.http_status >= 500).astype(int)
c['attempt_no'] = c.groupby(keys, sort=False).cumcount(); c['prior_5xx'] = (c.groupby(keys, sort=False).is5.cumsum() - c.is5) > 0
c['cls'] = np.where(c.attempt_no == 0, 'first_attempt', np.where(c.prior_5xx, 'error_driven_retry', 'other_retry')); out = pd.concat([c, df[unchainable].assign(cls='unchainable')]).groupby(['account_id','cls']).billable_units.sum().unstack(fill_value=0)

Expected result: A per-account table of four columns summing exactly to that account's total billable_units in the window. error_driven_retry is zero for any account with no 5xx; the fleet-level error-driven share is typically low single digits while a small number of accounts sit far above it.

Verify it yourself

out.sum(axis=1) equals df.groupby('account_id').billable_units.sum() for every account.
Every row classed error_driven_retry has attempt_no >= 1.
An account with no http_status >= 500 in the window has error_driven_retry == 0.
Cross-tabulating cls against is_retry: no first_attempt row should carry is_retry True, and any that do mark chains truncated by the window boundary.

Follow-up

An account's error-driven share is 22%. Is that the platform's fault or the client's, and what do you look at next?
How would you define a consumption-based north-star metric that an outage cannot inflate?
Chains straddle the 28-day boundary. How large is that bias and in which direction?

Seven-day activation rate by signup cohort week

Easy · funnel · cohorts · censoring

dim_account carries account_id, created_at, is_internal and is_current; fct_api_request carries account_id, request_at, http_status, api_key_id and traffic_class. Build a signup cohort by the ISO week of created_at over accounts with is_internal = false. An account counts as activated when it issues a request with http_status < 400, a non-null api_key_id and traffic_class <> 'synthetic_monitor' within 168 hours of its own created_at. Return cohort accounts, activated accounts and the rate per week, and exclude any week that has not yet fully elapsed its 168-hour window.

Approach

Collapse dim_account to one row per account_id before anything else. It is a type 2 dimension, so a plan or status change gives the same account several rows; filtering to is_current = true is the cheapest correct choice here because created_at does not change across versions.
Express the window as interval arithmetic on the timestamptz column: request_at >= created_at AND request_at < created_at + interval '168 hours'. A date-difference of 7 days is a different and wrong condition for accounts created mid-day.
Test activation with EXISTS rather than a join to MIN(request_at). EXISTS short-circuits, keeps the cohort at one row per account, and cannot fan out.
Aggregate by date_trunc('week', created_at AT TIME ZONE 'UTC'), counting accounts and activated accounts, and divide as a ratio of counts.
Drop unreportable weeks: the last account in a cohort week is created just under week_start + 7 days, so the week is only complete once now() >= week_start + interval '14 days'. Without that filter the newest week always looks like a regression.

Worked solution (20 min)

Write the cohort CTE: SELECT account_id, created_at FROM dim_account WHERE is_current AND NOT is_internal AND created_at >= <window start>.
Add the activation predicate as a correlated EXISTS over fct_api_request on account_id with the four conditions: http_status < 400, api_key_id IS NOT NULL, traffic_class <> 'synthetic_monitor', and the 168-hour bracket.
Group by date_trunc('week', created_at AT TIME ZONE 'UTC'); select count() AS cohort_accounts, count() FILTER (WHERE activated) AS activated_accounts, and the ratio cast to numeric.
Add HAVING or an outer WHERE that keeps only weeks where week_start + interval '14 days' <= now().
Spot-check one account that activated on hour 167 and one that activated on hour 169 to confirm the boundary is exclusive at the top.

Expected result: One row per ISO cohort week with cohort_accounts, activated_accounts and activation_rate between 0 and 1; the newest row is at least 14 days old, and accounts with no rows at all in fct_api_request appear in the denominator with activated = 0 rather than disappearing.

Verify it yourself

Summing cohort_accounts across weeks equals the count of non-internal current-version accounts created in the same window.
Re-running the query a day later leaves every previously reported week byte-identical; any movement means the censoring filter is wrong.
activated_accounts is never greater than cohort_accounts, and the count of distinct account_id in the activated set matches a separate COUNT DISTINCT over the fact table.

Follow-up

The median time-to-first-successful-call is more informative than a fixed-window rate. Why can you not compute it from this query, and what estimator does it need?
How would you separate accounts that never called from accounts that called and got only 4xx responses, and which of those is a product problem?

Running commitment burn-down and the date consumption crosses it

Medium · window functions · running totals · committed consumption

fct_subscription_period gives committed_amount_cents, term_start_date and term_end_date for each account's current version where pricing_model = 'committed_consumption'. fct_usage_daily gives account_id, workspace_id, sku_code, usage_date and net_amount_cents, at one row per workspace and SKU per day. Inside each account's current term, return the running total of net_amount_cents by usage_date, the first usage_date on which that running total reaches committed_amount_cents, and the fraction of the term elapsed at that point. Accounts that have not reached their commitment must still appear, with a null crossing date.

Approach

Collapse usage to one row per (account_id, usage_date) first. The fact is grained by workspace and SKU, so a raw running total leaves several rows per date and the first-crossing date becomes dependent on the arbitrary order of rows inside that day.
Restrict to the term with usage_date BETWEEN term_start_date AND term_end_date on the account's current row, so no prior term's consumption leaks into this term's burn-down.
Compute SUM(net_cents) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). Name ROWS explicitly: the default frame is RANGE, which includes all peer rows at the same ORDER BY value and would hand back the whole day's total on each of that day's rows.
Extract the crossing with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) grouped per account, which returns NULL for accounts still under commitment instead of dropping them.
Elapsed fraction is (crossing_date - term_start_date)::numeric / NULLIF(term_end_date - term_start_date, 0); Postgres date subtraction yields whole days, so the guard matters for same-day terms.
Exclude the trailing days still inside the metering settling window, measured from first_written_at against restated_at, and say how many days you cut and why.

Follow-up

Turn this into an end-of-term overage forecast. What breaks if you extrapolate a linear run rate on a consumption product?
An account crosses its commitment at 40 percent of the term. Is that an expansion signal or a billing-surprise risk, and what would you check to tell them apart?

Separate novelty from durable lift in a console redesign

Medium · novelty-effect · time-heterogeneity · holdout

A redesigned request console was tested for four weeks, randomised at the account level. The lift in weekly successful interactive requests per account was +9.2%, +5.1%, +1.8% and +0.4% in experiment weeks one through four, and the week-four 95% interval spans plus or minus 3.1 percentage points. Accounts entered on a rolling basis as they next signed in, so week one is a different calendar week for different accounts. Say what the decaying series can and cannot establish, specify the readout that would settle it, and state what you would ship on Monday.

Approach

Name the distinct explanations the same curve is consistent with, because the shipping decision differs across them: a novelty effect decaying to zero; a novelty effect decaying to a small positive plateau the test has no power to see; and a genuine effect whose measured size shrinks as control accounts learn about the change from colleagues or release notes.
Fix the time axis before interpreting anything. With rolling entry, calendar week and weeks-since-first-exposure are different variables, and pooling them mixes the decay curve with a change in cohort composition, since the accounts that signed in on day one are systematically the most engaged. Recut on weeks-since-first-exposure and verify that plan_tier and pre-period usage mix are stable across entry cohorts.
Read the interval width honestly. Plus or minus 3.1 percentage points at week four cannot separate a durable +1% from zero. The defensible statement is that no durable effect larger than roughly 3.5% was detected, not that there is no durable effect, and those two sentences lead to different decisions.
Separate novelty from primacy using account tenure. Accounts created after launch have no prior console to be surprised by, so they cannot show novelty; if the redesign is genuinely better their curve should be flat or rising, while established accounts show the spike and decay. If both cohorts decay to zero, the effect really was novelty.
Specify the settling readout rather than arguing about the four weeks you have. Hold back 5% of accounts as a long-run holdout for 90 days, and compare whole ISO weeks only: usage in this domain follows a hard five-to-two weekday cycle, and a window containing four business days instead of five moves this metric by several percent with no product change.
Give the Monday answer. Ship if guardrails are clean and maintenance cost is low, because a decayed-to-zero effect with no harm is a neutral trade; but book none of the +9.2% in any forecast, and do not run a follow-up test on the same accounts inside the novelty window, because their baseline has not returned to steady state.

Follow-up

What sample or horizon would you need to rule out a durable +1.5% at 80% power, given the week-four interval you have?
How would you distinguish a novelty effect from a control arm that gradually learned about the change?
The metric that decides renewal is up to eleven months away on annual contracts. What proxy do you use in the meantime, and how do you validate it once renewals land?

Cut variance with pre-period usage before the test starts

Medium · cuped · variance-reduction · stratification

You are planning an account-randomised test on 2,800 paying accounts. The outcome is a 28-day sum of billable_quantity for sku_code = 'compute_hours' from fct_usage_daily, and the same account's 28-day pre-period sum correlates 0.80 with it. 420 accounts were created inside the pre-period and have partial or no history. Specify the variance-reduction plan: the adjusted estimator and where its coefficient comes from, how assignment is stratified, how the 420 incomplete accounts are handled, and what must change in the pre-period window given that fct_usage_daily rows are restated after first write.

Approach

Write the estimator explicitly: Y_adj = Y - theta * (X - mean(X)), with theta = Cov(X, Y) / Var(X). Estimate theta from pre-experiment history or pooled across arms, never separately by arm and never from post-treatment outcomes. Fitting theta on treatment-arm outcomes folds the effect being measured into the adjustment and biases the result toward whatever the treatment did.
Quantify the gain and convert it into the currency the team cares about. The residual variance multiplier is 1 - 0.80^2 = 0.36, so the standard error falls to 0.60 of its unadjusted value and the MDE falls with it. That is the same precision as running with 1 / 0.36, about 2.8 times as many accounts, which matters because the account population is fixed and cannot be bought with a longer run.
Stratify assignment on pre-period usage decile crossed with the three paid plan tiers, giving 30 cells at roughly 93 accounts each, and collapse any cell below about 20. Use the same strata in the analysis through strata fixed effects or post-stratification: stratified assignment analysed pooled discards much of the gain, and stratified analysis without stratified assignment risks empty cells in the top decile, which is precisely where the revenue sits.
Handle the 420 incomplete accounts by imputing X at the stratum mean and adding a binary indicator for missing pre-period, rather than dropping them. Dropping silently redefines the population to established accounts, which is usually the opposite of the segment a new feature targets, and it makes the result non-generalisable in a way the readout will not disclose.
Fix the window against restatement. Measure the empirical settling time by comparing a usage_date's total at first_written_at against its value after restated_at has stopped moving, then end the pre-period that many days before assignment. A pre-period whose last days are still settling carries recency-correlated measurement error in the covariate, which both weakens rho and can correlate with assignment date.
Pre-register the whole plan before assignment: the estimation set for theta, the strata definition and collapsing rule, the imputation rule, the winsorisation cap and the trailing exclusion. Every one of these can be tuned after the fact to move a p-value, which is why they are worth nothing if decided afterwards.

Worked solution (30 min)

Estimate rho on a historical pair of adjacent 28-day windows, applying exactly the traffic-class and is_internal filters the live experiment will use.
Compute the variance multiplier 1 - 0.80^2 = 0.36 and translate it to a standard-error multiplier of 0.60 and an effective-sample multiplier of about 2.8.
Build 30 strata from ten pre-period usage deciles crossed with three paid plan tiers, inspect the minimum cell count and collapse cells below about 20 accounts.
Measure the metering settling time from first_written_at against restated_at and shift the pre-period window back by that many days.
Write the pre-registration: theta source, strata, imputation for the 420 incomplete accounts, winsorisation cap, trailing exclusion.

Expected result: Standard-error multiplier 0.60 and effective-sample multiplier about 2.8 at rho = 0.80, with 30 strata collapsed where thin, the 420 incomplete accounts retained through stratum-mean imputation plus a missingness indicator, and the pre-period ending one measured settling window before assignment.

Verify it yourself

Adjusted and unadjusted point estimates must be close under valid randomisation, because this reduces variance rather than moving the estimate. A large shift means theta was fitted on post-treatment data or that randomisation itself failed.
The multiplier 1 - rho^2 is monotone in the absolute correlation and equals 1 at rho = 0, so at rho = 0.30 the standard error can only fall by about 5%. Any larger claimed reduction at that correlation is arithmetically wrong.
Computing the pre-period sum twice, a week apart, should return the same number once the settling window is excluded. If it does not, the exclusion window is too short.

Follow-up

Once continuous-integration and synthetic traffic are excluded, rho turns out to be 0.45 rather than 0.80. What is the revised variance reduction, and is the added complexity still worth it?
How would you extend this to more than one covariate, and what stops you from adding twenty?
Does this adjustment repair an imbalance you discover after assignment, or only reduce variance? Be precise about the difference.

Net revenue retention jumps sixteen points in one month

Medium · versioned tables · fan-out · metric definition

Trailing-twelve-month net revenue retention printed around 108 percent for months and now reads 124 percent, with no unusual deals closed. The query sums arr_cents from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, superseded_by_id, is_current, booked_at) filtered on is_current = true at month M, across accounts holding arr_cents > 0 at month M-12. Find the defect, correct the number, and rewrite the definition so the next person cannot reintroduce it.

Approach

Audit the grain before the arithmetic: count account_ids holding more than one row with is_current = true and superseded_by_id null. A versioned contract table that double counts one amendment batch inflates the numerator while leaving the denominator untouched.
Replace is_current with an as-of selection on both dates, taking the version whose term_start_date and term_end_date bracket the reporting date and tie-breaking on latest booked_at. The numerator is read as of M and the denominator as of M-12; neither uses today's live version.
Verify the cohort is frozen. The account set is fixed at M-12 and nothing acquired since may enter the numerator, so check that no join to a current-period table quietly re-admits new accounts.
Confirm the estimand is a ratio of sums rather than a mean of per-account ratios. Contraction is floored at zero while expansion is unbounded, so the two constructions differ systematically and the second is far noisier.
Reissue the definition with the failure modes written into it: exactly one row per account per date by construction, cohort frozen at M-12, churned accounts contributing zero rather than dropping out of the numerator.

Follow-up

A churned account should contribute zero rather than disappear. What does the ratio do under each treatment, and which one is correct?
How would you unit-test this metric so a future amendment batch with the same defect fails a check instead of reaching a board slide?

Suggested practice checklist

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

Day 1 — Build a fixture you can check answers against

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

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

Day 2 — Joins, filters and NULL semantics

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

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

Day 3 — Window functions and frames

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

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

Day 4 — The four analytical query patterns

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

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

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

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

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

Day 6 — One day for everything that is not SQL

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

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

Day 7 — Full loop rehearsal

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

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

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.

How do you handle imbalanced datasets in a classification problem?

Medium · behavioural and stakeholder questions

Approach

Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.
Quantify the outcome, including what you would not claim credit for.

Follow-up

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

State the measured impact of your own work honestly

Hard · impact · causal inference · self-assessment

You are asked for the business impact of a renewal-risk worklist you shipped nine months ago. Customer success used it, and renewals in the covered segment came in 6 points above the prior year. Coverage was assigned by the team itself: they worked the top of your list and also the accounts they were already worried about. Produce the impact claim you are willing to defend, the number you refuse to claim, and the design you would have asked for at the start.

Approach

The interviewer is probing whether you can separate what you shipped from what you caused, and whether you would have built the measurement in rather than reconstructing it afterwards. Both halves are being scored.
Name the confound precisely. Coverage assignment is doubly selected: the largest accounts get an owner because they are valuable, and distressed accounts get one because they are at risk. The naive covered versus uncovered comparison mixes a strong positive selection with a strong negative one and can come out with either sign depending on which rule dominated. Matching on account size does not fix it, because the risk signal that triggered coverage is the same signal that predicts the outcome.
Split the claims by what each needs to be true. Ranking quality is defensible from precision at k on out-of-time renewals. Adoption is defensible from timestamps showing what share of listed accounts were contacted. The outcome claim is not defensible without a design, and saying so is the point of the exercise.
Look for identification before giving up on it. A capacity cut-off, a territory boundary, or a period in which the list existed but was unstaffed can assign coverage for reasons unrelated to account health, and any of those supports a bounded estimate.
State the design you would ask for now and its price: a randomly withheld slice of the list, held for two renewal quarters, with the expected cost in renewals stated openly. That cost is what it takes to be able to answer this question at all.
Give a bounded number rather than none. Six points with an explicit statement of how much of it you can attribute is more useful than either claiming the whole figure or declining to quantify anything.

Follow-up

Your manager wants the 6 points in a promotion packet. What wording do you accept, and what do you strike?
What would have had to be true for the naive covered versus uncovered comparison to be valid?
If the holdout costs the team real renewals, how do you justify asking for it, and to whom?

Allocate one analyst week across three competing requests

Easy · prioritisation · stakeholders · scoping

Three requests arrive the same morning and you have one week. Finance wants per-account gross margin from fct_usage_daily for a pricing review in three weeks. Sales wants a renewal-risk list for accounts with term_end_date inside 60 days. A product manager wants an experiment readout for a decision being taken on Thursday. Produce your allocation with hours attached, what you say to whoever receives less, and one thing you refuse to do this week, with the reason each decision is defensible to the person it costs.

Approach

The interviewer is probing whether you prioritise on decision timing and reversibility or on who asked most forcefully. Sort by the date each decision is actually taken and by what the default outcome is if nothing arrives.
Apply that sort concretely. The Thursday readout has a hard irreversible deadline and no value afterwards. The pricing review has three weeks of slack. The renewal list has a rolling deadline set by term_end_date, so part of it is urgent this week and the rest is not, which means it can be split rather than deferred whole.
Find the cheapest sufficient version of each request rather than the full version. The readout goes in full. The renewal list ships as a filtered query over renewal-eligible accounts ranked by two inspectable signals rather than as a model. The margin work is scoped to the accounts that dominate the pricing decision, since revenue is heavily skewed and the tail will not change the conclusion.
Make the trade visible in one written note to all three at once, with dates. Telling each person separately that they are the priority is how an allocation becomes a credibility problem.
Refuse something explicitly and say why. The model version of the renewal list is the usual candidate, because it cannot be evaluated without a holdout nobody has agreed to yet, and building it this week forecloses that.
Leave slack. A plan with none is a plan to miss the one deadline that cannot move.

Follow-up

The sales leader escalates to your manager. What did you already do that makes that a short conversation?
Which of the three deadlines would you push back on, and what exactly would you ask for?
What would you change about how these requests reach you so next week is not the same?