Argus Information & Advisory Services Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at Argus Information & Advisory Services, you are stepping into a pivotal role at the intersection of advanced analytics and financial strategy. Argus is renowned for managing massive, complex datasets related to banking, payments, and consumer credit behaviors. In this role, your primary objective is to transform this proprietary financial data into actionable, predictive insights that guide major financial institutions in their strategic decision-making.

In modelling rounds the live question is usually why this model class for this problem, and how you would know six weeks after launch that it is still working. Deriving gradients by hand is rarely what is being probed.

Focus areas

• Size an experiment before anyone launches it
• Decompose a metric move by segment and mix
• Turn a vague request into a measurable question

Overview

Your impact extends directly to the core products and advisory services Argus provides to its clients. You will build predictive models that assess credit risk, optimize marketing spend, forecast customer behavior, and detect anomalies in payment ecosystems. Because Argus operates as a trusted advisor to the financial services industry, the models and insights you generate must be both mathematically rigorous and highly interpretable for business stakeholders.

What makes this position uniquely challenging and rewarding is the sheer scale of the financial data and the direct business implications of your work. You are not just building models in a vacuum; you are solving high-stakes problems for top-tier banks and payment networks. Expect to navigate complex regulatory environments, handle highly sensitive data, and translate deep technical findings into straightforward advisory strategies.

The Argus Information & Advisory Services interview process

Argus Information & Advisory Services candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Technical Phone Screen

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

• Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
• Whether your reason for wanting the role points at the work itself rather than the company's reputation
• Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

• Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
• Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
• Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands

Onsite Interview

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What to demonstrate

• Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
• Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
• Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
• Whether you ask what decision is actually on the table before explaining anything

How to prepare

• Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
• For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
• Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else

Where candidates lose points

Counting on an identity key that changes underneath the metric

visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.

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.

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

Reporting a p-value with no effect size or interval

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

Questions & practice

What strategies do you use to deal with highly skewed data distributions?

Medium · statistics and probability

Approach

1. Quantify uncertainty explicitly rather than reporting a point estimate alone.
2. Sanity-check the answer against a simple bound or a simulated case.
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?
• How would you explain this result to someone who does not know statistics?

Describe the difference between generative and discriminative models.

Medium · statistics and probability

Approach

1. Say what the estimate is of, and over what population it generalises.
2. Translate the result into the decision it informs, in one plain sentence.
3. Sanity-check the answer against a simple bound or a simulated case.

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?

How do you handle overfitting in a gradient boosting model?

Medium · machine learning and modelling

Approach

1. Set a baseline first, so any model has something honest to beat.
2. Say how the offline result would be validated online before it is trusted.
3. Pick an evaluation metric that matches the cost of each error type, not a default.

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?

How do you determine the optimal number of clusters in a K-Means model?

Medium · machine learning and modelling

Approach

1. Pick an evaluation metric that matches the cost of each error type, not a default.
2. Say how the offline result would be validated online before it is trusted.
3. Frame the prediction: the label, the moment of prediction, and the action it triggers.

Follow-up

• What would you monitor after launch to know the model is still valid?
• How would you choose the decision threshold, and who owns that choice?

Audit a one-day event extract for structural defects

Easy · data quality · late arrival · pandas

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

Approach

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

Worked solution (20 min)

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

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

Verify it yourself

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

Follow-up

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

Write a SQL query using a window function to calculate a moving average of daily transactions.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. Check whether any join is one-to-many before aggregating, or the sums inflate.

Follow-up

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

How do you approach feature selection when you have thousands of potential variables?

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. State the window function and its partition and ordering out loud before writing it.
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 your typical data cleaning pipeline in Python or R.

Medium · sql and data manipulation

Approach

1. Check whether any join is one-to-many before aggregating, or the sums inflate.
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

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

Read an experiment from first exposure, not assignment

Hard · experiments · window-functions · srm

fct_experiment_exposure holds experiment_id, unit_type, unit_id, variant, user_id, assigned_at_utc, first_exposed_at_utc, is_in_analysis_population and planned_end_utc. fct_event holds user_id, occurred_at_utc, is_core_action, and carries events up to a known data cut, :data_cut_utc. For one experiment randomised on unit_type = 'user', return per variant: exposed units, units with at least one core action in the seven days after that unit's own first exposure, the rate, and the variant share of exposed units. Only units whose seven-day window has fully elapsed as of the data cut belong in the readout. Units appearing under more than one variant are excluded from both arms and counted separately.

Approach

1. Run the contamination pass as an aggregate, not a window: SELECT unit_id FROM fct_experiment_exposure WHERE experiment_id = :exp GROUP BY unit_id HAVING COUNT(DISTINCT variant) > 1, then anti-join it away. PostgreSQL rejects COUNT(DISTINCT variant) OVER (PARTITION BY unit_id) outright, since DISTINCT is not implemented for window functions; if you want the test inline, MIN(variant) OVER (PARTITION BY unit_id) <> MAX(variant) OVER (PARTITION BY unit_id) is the equivalent that does run.
2. Do not resolve contamination by keeping the earliest variant. A unit that saw both arms carries treatment from both, so assigning it to either one biases that arm.
3. Define the population as is_in_analysis_population = TRUE AND unit_type = 'user' AND first_exposed_at_utc < planned_end_utc AND first_exposed_at_utc + interval '7 days' <= :data_cut_utc. The horizon filter is what makes the readout reproducible next week instead of drifting with every re-run; the data-cut filter is the one that actually buys seven days of follow-up, since a unit exposed an hour before the horizon otherwise contributes an hour of observation to a seven-day rate.
4. Measure the outcome on a per-unit relative window: LEFT JOIN fct_event on user_id with is_core_action = TRUE and occurred_at_utc in [first_exposed_at_utc, first_exposed_at_utc + interval '7 days'). LEFT JOIN so units with no outcome stay in the denominator at zero rather than being deleted by an inner join.
5. Check the sample ratio before reading the effect: variant share of exposed units against the intended split, tested as a binomial. Run it on the truncated population as well as on the full exposed set, because if one arm exposes later on average the data-cut filter removes more of that arm and can manufacture a ratio mismatch the randomisation did not have. A mismatch on the full set means the exposure data is not a valid randomisation and invalidates the readout rather than being a footnote under it.
6. Report the per-variant rate, the absolute difference, and the fact that the variance unit is unit_id. That is straightforward here only because the grain is already one row per user; a per-session outcome under user randomisation would need a delta-method or bootstrap standard error instead.

Worked solution (35 min)

1. Count units per variant and units with more than one variant; report the contaminated count before anything else.
2. Build the exposed population CTE with both the horizon filter and the seven-days-before-data-cut filter, assert one row per unit_id, and record per variant how many units the second filter removed.
3. LEFT JOIN the bounded outcome events and flag conversion with a boolean aggregate.
4. Aggregate per variant to exposed, converted, rate and share, and run the binomial check on the share, both before and after the truncation.
5. Re-run with a fixed calendar window from experiment_start_utc and record the difference as the size of the dilution you avoided.

Expected result: One row per variant with exposed, converted, rate and share, plus a scalar count of excluded contaminated units. Every retained unit was first exposed at least seven days before the data cut, so each one has a full seven days of observation and the rates are directly comparable.

Verify it yourself

• Exposed counts across variants sum to the population count minus the contaminated units.
• MAX(first_exposed_at_utc) among retained units is at least seven days before :data_cut_utc.
• Variant shares sum to 1.
• No unit_id contributes to two variant rows.
• The truncation drops a similar share of each arm; if it does not, note the imbalance alongside the SRM result rather than reading the effect.
• Swapping the relative window for a calendar one changes the rates; if it does not, exposure timing is balanced and you can say so.

Follow-up

• Some units were assigned days before they were exposed. What does analysing the assigned set instead do to the estimated effect, and in which direction?
• The split is 51/49 on 400,000 exposed units. Do you read the result?
• The treatment arm exposes on average two days later than control. What does that do to a fixed calendar outcome window, and which arm does it favour?

How would you measure the success of a targeted marketing campaign sent to a subset of retail banking customers?

Medium · product sense and metrics

Approach

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

Follow-up

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

If a client wants to increase the usage of a specific rewards card, what data points would you analyze?

Medium

Approach

1. Say what you would check first and why it is the highest-information step.
2. Clarify what is being asked and what a complete answer would contain.
3. Work from the decision backwards to the evidence you would need.

Follow-up

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

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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)

1. 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.
2. 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.
3. Write the surrogacy assumption as a falsifiable sentence, then write the specific mechanism in this treatment that violates it.
4. 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.
5. 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?

Pooled signup conversion fell while every segment rose

Medium · mix-shift · simpsons-paradox · decomposition

Weekly visit-to-signup conversion, counted on distinct fct_session.visitor_id with is_bot_flagged = TRUE and consent_state = 'denied' sessions excluded, fell from 4.4% to 3.9% week over week. Split by device_type and referrer_channel, all twelve cells are flat or up. A paid_social campaign launched on Monday. Using fct_session and fct_event, quantify how much of the 0.5-point fall is mix and how much is within-segment rate, then state what you would tell the growth lead.

Approach

1. Write the pooled rate explicitly as the sum over segments of weight times segment rate, and materialise both weeks' weights and rates into one table. Until that table exists there is nothing to decompose, only opinions.
2. Compute three quantities and report all three: the rate effect holding the prior week's weights fixed, the mix effect holding the prior week's rates fixed, and the interaction residual. Reporting only the first two hides a term that can be material when both weights and rates move a lot.
3. Rank segments by their individual mix contribution, computed as the change in that segment's weight multiplied by its prior-period rate. This is what lets you say one cell caused the move rather than gesturing at the campaign.
4. Verify the new traffic is human and countable before accepting the mix story: check is_bot_flagged coverage on the new channel, the distribution of duration_seconds and event_count for its sessions, and whether its consent_state profile differs from the rest.
5. Deliver the conclusion as a definition change rather than a diagnosis: a pooled rate over a mix that moves is not comparable week over week, so the recurring report should carry per-channel rates plus absolute signups, with the pooled figure demoted or dropped.

Follow-up

• Paid social converts at roughly a quarter of organic but absolute signups rose. Is the campaign working, and what would you need to answer that properly?
• Would you reach the same conclusion if the campaign had moved the mix by two points instead of sixteen? Where is your threshold and why?
• How would you present this to someone who has been watching the pooled number in a weekly meeting for a year?

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.

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Tell me about a time you failed on a data project. What did you learn?

Medium · behavioural and stakeholder questions

Approach

1. Quantify the outcome, including what you would not claim credit for.
2. Pick a story where you drove the decision, not one where you observed it.
3. State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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

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?

Turn an ambiguous onboarding question into a measurable metric

Easy · scoping · metric definition · stakeholder

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

Approach

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

Follow-up

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