Interview guides · Arity

Arity Data Scientist Interview Questions & Guide 2026

Case practice for Arity data scientist candidates: scope a vague prompt into a measurable question before you analyze it.

The 60-second brief

As a Data Scientist at Arity, a transportation data analytics company founded as part of The Allstate Corporation, you sit at the intersection of massive-scale mobility data and cutting-edge predictive modeling. Your work directly empowers telematics products, advertising intelligence platforms, and risk assessment systems that shape how millions of drivers move safely. By analyzing massive streams of driving and behavioral data, you turn raw telemetry into actionable products that help businesses optimize marketing funnels, evaluate driving risk, and deploy scalable machine learning solutions.

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

Focus areas

Defend attribution windows and model choice
Separate invalid traffic from genuine performance shifts
Diagnose pacing: underdelivery, overdelivery, budget exhaustion

Overview

This role requires a rare blend of deep technical rigor and product intuition. You will build proof-of-concept business solutions, design robust experiments, and collaborate closely with cross-functional scrum teams consisting of product managers, software engineers, and ad operations specialists. Whether you are optimizing programmatic advertising platforms, forecasting driver behavior, or diagnosing complex metric drops in production pipelines, your insights drive revenue growth and technical innovation across the entire ecosystem.

Expect an environment that demands both analytical agility and autonomous execution. You will frequently encounter ambiguous business problems where standard modeling practices do not apply, requiring you to rapidly adapt, test hypotheses, and justify your methodological choices to senior leadership. Success in this role means translating complex data architectures into clear business value while championing data-driven decision-making across the organization.

The Arity interview process

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

HR Screening Call

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

What to demonstrate

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

How to prepare

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

Technical Screening

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

Final Interview Loop

Where a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.

What to demonstrate

Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered

How to prepare

Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one

Where candidates lose points

Treating a bid as if it does not affect the price paid, under a first-price auction.

In a second-price auction the winner pays the runner-up's bid, so bidding true value is close to optimal and bid and price are usefully separable. Under first-price clearing, which is now the common case in open exchanges, the winner pays exactly what it bid, so raising the bid raises both the win probability and the cost of every win that would have been won anyway. A bid-increase experiment can therefore show more impressions, more conversions and worse profit simultaneously, and reading only the volume metrics will call it a success. The related second-order problem is that your own bid increase raises the clearing prices your control cells face, which contaminates the comparison unless the cells are separated by market.

Comparing conversion rates between users who were exposed to an ad and users who were not.

Exposure is the output of a targeting and bidding system that ranks users by predicted conversion propensity, so the exposed group has a higher baseline conversion rate before the ad renders. The measured difference therefore estimates the quality of the targeting model, not the effect of the advertising, and it is biased upward by an amount that grows as the model gets better. The valid control is the set of users the system would have shown the ad to but deliberately did not, captured through a ghost-bid or public-service-announcement holdout that records auction wins without serving the advertiser's creative. Anyone who proposes matching or covariate adjustment to fix this should be asked what unobserved feature the bidder used that the matching model does not have.

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.

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

Questions & practice

How do you handle heavily skewed distributions when calculating confidence intervals for driving speed or trip distance data?

Medium · statistics and probability

Approach

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

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?

Walk me through a past ML project where your initial feature engineering strategy failed, and how you iterated to fix it.

Medium · machine learning and modelling

Approach

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.
Set a baseline first, so any model has something honest to beat.

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?

Simulate bid shading profit under first-price auction clearing

Hard · simulation · auctions · bid-shading · monte-carlo

auctions holds 200,000 backtest rows for one line_item_id: predicted_cvr (per impression), floor_price_cpm_usd (nullable when the floor is undisclosed), and best_competing_cpm_usd, the highest competing bid, observable only in this sample. Clearing is first price. The advertiser values a conversion at $40. For shading factors s from 0.50 to 1.00 in steps of 0.05, the submitted bid is s * 1000 * 40 * predicted_cvr and the line item wins when that bid clears both the floor and the best competing bid. Return win rate, spend, conversions and profit per thousand auctions, the profit-maximising s, and an uncertainty estimate for it.

Approach

Separate what is deterministic from what is random before writing any sampling code. Given the bid, winning is deterministic and the price paid under first-price clearing is exactly the submitted bid, so the only stochastic element is whether a won impression converts. Use the closed form sum of predicted_cvr over wins for the expected conversion count and reserve Monte Carlo for the profit distribution, not the mean.
Vectorise across s: build the bid vector once, then evaluate the 11 shading factors as numpy operations over the full array. A per-row Python loop over 200,000 rows by 11 factors is about two million iterations and turns a 3-second job into minutes.
Make the win condition explicit and handle nulls deliberately: treat a null floor as no floor, require bid > best_competing_cpm_usd, and require bid >= floor_price_cpm_usd. Ties at exactly the floor behave differently from ties against a competitor, and the convention should be stated rather than inherited from a comparison operator chosen by accident.
Compute profit per thousand auctions as (40 * conversions - sum of bid/1000 over wins) / n * 1000. Spend must use your own submitted bid, not the competing bid: under first-price clearing every inframarginal win, one you would have taken at a lower bid, now costs more, and that term is what creates an interior optimum.
Get uncertainty by bootstrapping auction rows, recomputing the whole curve per replicate, and reporting the distribution of the argmax s rather than only a confidence band around the profit level. The decision uses the argmax, and it is far less stable than the profit curve it comes from.
State the backtest's limit honestly: best_competing_cpm_usd was observed while you bid what you actually bid. If your change alters competitors' behaviour or triggers exchange-side floor adjustments, the counterfactual does not hold and the curve overstates the achievable gain.

Worked solution (35 min)

Precompute value_cpm = 1000 * 40 * predicted_cvr and floor_eff = floor_price_cpm_usd filled with -inf.
For each s: bid = s * value_cpm; win = (bid > best_competing_cpm_usd) & (bid >= floor_eff); spend = bid[win].sum()/1000; expected conversions = predicted_cvr[win].sum().
Profit per thousand auctions = (40 * conversions - spend) / n * 1000; assemble the 11-row curve with win rate = win.mean().
Bootstrap: resample row indices with replacement 500 times, recompute the curve on each replicate, and collect the argmax s plus a percentile band on profit at the point-estimate optimum.
Optionally draw Bernoulli(predicted_cvr) on won rows for one replicate set to show the profit distribution around the expectation, and confirm the sampled mean matches the closed form.

Expected result: An 11-row curve of (s, win_rate, spend_per_1k, conversions_per_1k, profit_per_1k) with win rate non-decreasing in s, profit single-peaked at an s strictly below 1.00 under a typical competing-bid distribution, and expected profit approaching zero at s = 1.00. The bootstrap returns a distribution over the argmax that usually spans two or three adjacent grid points rather than one.

Verify it yourself

Win rate is non-decreasing across the s grid; a dip means the floor or tie comparison is inconsistent between iterations.
At s = 1.00 expected profit is approximately zero, because the bid equals the expected revenue on every impression won. A materially positive value there means spend is being charged at the competing bid rather than at your own submitted bid. This identity holds only if predicted_cvr is calibrated on the won population, so verify that before reading it as a bug.
Total spend at any s equals the sum of submitted bids over won rows divided by 1000, reconciled against a direct recomputation from the bid vector.
Monte Carlo conversions per thousand match the closed-form sum of predicted_cvr over wins within sampling error; a systematic gap means the Bernoulli draw is using the wrong probability or is being applied to lost auctions.

Follow-up

Raising this line item's bid also raises the clearing price faced by another line item in the same account bidding on overlapping supply. How does that change the optimum, and how would you detect it in the logs?
predicted_cvr over-predicts by 15% at the head of the distribution. Which way does the optimal s move, and roughly how far?
How would you run this as a live test instead of a backtest, and what is the randomisation unit given that both arms draw on one budget?

Write a SQL query using window functions to calculate running three-month moving averages of driver mileage by user ID.

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 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 user event table, write a query to identify the top three most frequent trip routes for each driver using ranking functions.

Medium · sql and data manipulation

Approach

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.
Say which table is the grain you start from, and join outward from it.

Follow-up

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

Deduplicate conversions across sources and quantify the inflation rate

Medium · window functions · deduplication · null handling

conversion_event holds one row per report received, before deduplication: conversion_id, advertiser_id, conversion_ts, received_ts, conversion_type, conversion_value_usd, dedup_key TEXT (nullable, advertiser-supplied) and source (browser_pixel, mobile_sdk, server_api, offline_upload). The same real purchase routinely arrives from two sources. For one advertiser and one month, keep exactly one row per (advertiser_id, dedup_key), preferring server_api, then mobile_sdk, then browser_pixel, then offline_upload, breaking remaining ties on earliest conversion_ts. Return the raw count, the deduplicated count and the inflation rate, and state what you do with rows whose dedup_key is null.

Approach

Rank with ROW_NUMBER() OVER (PARTITION BY advertiser_id, dedup_key ORDER BY CASE source WHEN 'server_api' THEN 1 WHEN 'mobile_sdk' THEN 2 WHEN 'browser_pixel' THEN 3 ELSE 4 END, conversion_ts, conversion_id) and keep rn = 1. The CASE encodes the stated preference; conversion_id as the last sort key makes the result deterministic when two reports tie on everything else, which matters because a non-deterministic dedup produces a different count on every run.
Handle dedup_key IS NULL before the window runs. SQL window partitioning treats all nulls as a single group, so every unkeyed conversion but one would be discarded. Split them out, pass them through untouched, and report their share alongside the headline number.
Compute the raw and deduplicated counts over an identical filter set in the same query, so the inflation rate is not comparing two differently-scoped populations. Inflation is raw / deduped - 1.
Break the duplicate population down by how many distinct sources share each dedup_key. A key appearing under two sources is the expected pixel-plus-server pattern; a key under three or with reports days apart is an advertiser implementation problem, not a duplicate.
Cross-check value per key, not in aggregate. The preference order sorts on source, not on whether conversion_value_usd is populated, so a server_api row that reports the order identifier with a null or zero value outranks a browser_pixel row carrying the basket total. Keep the surviving value beside the maximum value seen among that key's dropped rows so the substitution is visible, and if it happens, fix it with an explicit value rule (for example, coalesce to the highest non-null sibling value) rather than by reordering the source preference, which would change which row's other attributes you publish.

Worked solution (25 min)

Scope the scan to one advertiser_id and conversion_ts inside the month, and record the raw row count.
Split into a keyed set (dedup_key IS NOT NULL) and an unkeyed set, counting each, since the unkeyed share is the number you will be challenged on.
On the keyed set apply the ROW_NUMBER with the source CASE, conversion_ts and conversion_id in the ORDER BY, and keep rn = 1.
UNION ALL the unkeyed rows back in unchanged so the output is the full deduplicated population rather than only the keyed part.
Emit raw, deduped, raw::numeric / deduped - 1, and a side table of COUNT(*) per dedup_key so any key with three or more reports is visible.
From the same ranked set, emit kept_value = conversion_value_usd at rn = 1 and dropped_max_value = MAX(conversion_value_usd) FILTER (WHERE rn > 1) per key, and count the keys where the first is null or 0 while the second is above 0.

Expected result: For example: 148,200 raw reports, 131,000 deduplicated conversions, 13.1% inflation, with 6,400 null-dedup_key rows reported separately as passed through untouched.

Verify it yourself

The deduplicated count is never above the raw count and never below COUNT(DISTINCT dedup_key) plus the null-key row count.
Every dedup_key appears exactly once in the output and every surviving keyed row has rn = 1.
No key loses revenue to the source preference: the count of keys where the rn = 1 row has conversion_value_usd null or 0 while some rn > 1 row in the same key carries a value above 0 must be zero. This has to be checked per key, because a total-level comparison cannot detect it at all: the deduplicated total falls by exactly the value carried by the dropped rows, by construction, whichever row the ranking kept.
Running the query twice returns the identical conversion_id set, which is the only proof that the ORDER BY has a unique tiebreaker.

Follow-up

The advertiser's own order table shows 10% fewer conversions than your deduplicated count. Where do you look first?
What changes if the advertiser populates dedup_key on the browser pixel but leaves it null on the server API?
received_ts lags conversion_ts by hours to days. What breaks if the dedup job runs hourly over a fixed window rather than on arrival?

How do you prioritize competing requests from multiple product teams when working in a fast-paced agile scrum environment?

Medium · product sense and metrics

Approach

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

Follow-up

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

What would you do if your primary metric shows a statistically significant lift, but secondary guardrail metrics degrade?

Medium · product sense and metrics

Approach

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

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

Define a set of key performance indicators for an in-house advertising platform to measure both publisher yield and advertiser ROI.

Medium · product sense and metrics

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.

Follow-up

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

How would you design a core engagement metric for a new telematics-based driving rewards application?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Restate the decision this analysis has to support, and who acts on the answer.
State what result would change your recommendation, so the answer is falsifiable.

Follow-up

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

Walk me through common experimentation pitfalls, such as network effects and sample ratio mismatch, and how you would mitigate them.

Medium · a/b testing and experimentation

Approach

State the primary metric and the minimum effect worth shipping, then size the test.
Say whether units interfere with each other, and switch design if they do.
Decide the analysis before seeing data, including how long it runs and when you look.

Follow-up

What would you conclude if the result is positive but the test is underpowered?
What would you do if you could not randomise at all?

Explain the mathematical intuition behind hypothesis testing and how you guard against false positives when running multiple concurrent experiments.

Medium · a/b testing and experimentation

Approach

Say whether units interfere with each other, and switch design if they do.
Name the guardrails that would stop a launch even on a positive primary result.
State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

Cut the variance of a market-level holdout with pre-period data

Medium · cuped · variance-reduction · stratification

A 28-day holdout randomises 80 geo_dma markets 40/40; treated markets run the campaign and held-out markets get no delivery. The outcome is deduplicated conversion_event rows per market over the flight plus a 7-day window. Market sizes are heavily skewed and the unadjusted design detects only a 12% relative lift at 80% power. Twelve months of pre-period market conversions are available. Specify a variance-reduction plan, state the precision you expect, and state what would disqualify a covariate.

Approach

Apply CUPED: define Y_adj = Y - theta*(X - mean(X)) where X is pre-period conversions for the same market and theta = Cov(Y,X)/Var(X), estimated on the pooled arms. Variance falls by a factor of (1 - rho^2), where rho is the correlation between pre-period and in-period outcomes.
Estimate rho from history before the test runs, by regressing a past 28-day market window on its own preceding pre-period covariate. At rho = 0.9 the variance multiplier is 0.19, the standard error multiplier is 0.436, and the 12% MDE falls to roughly 5.2%.
Disqualify any covariate measured at or after assignment. In-flight impressions, in-flight spend and in-flight clicks are all downstream of treatment; adjusting on them shifts the point estimate rather than only shrinking its variance.
Stratify as well as adjust: form matched pairs on pre-period conversion volume, randomise within pair, and then analyse with pair fixed effects or a paired difference. Pairing that is not carried into the estimator buys nothing at all.
Handle the skew at design time by analysing the ratio metric, conversions per thousand eligible bid requests, or by weighting on pre-period volume, and commit to the choice before seeing the arms.
Pre-register theta's estimation rule and the covariate window, so neither can be tuned after the outcome becomes visible.

Worked solution (30 min)

On 12 months of history, build market-level 28-day outcome windows with matching pre-period covariates and compute rho; assume it returns 0.90.
Compute the variance multiplier 1 - rho^2 = 0.19 and the standard error multiplier sqrt(0.19) = 0.436.
Scale the unadjusted MDE: 12% * 0.436 = about 5.2% relative lift at 80% power, equivalent to 1/0.19 = 5.3 times the market count.
Form 40 matched pairs on pre-period volume, randomise within pair, and write the estimator as a paired difference on CUPED-adjusted outcomes.
Replay the whole pipeline as an A/A on two historical windows and confirm the adjusted estimate is centred on zero with the predicted standard error.

Expected result: rho of about 0.9 gives an 81% variance reduction, a 56% standard error reduction, and an MDE improvement from 12% to roughly 5.2% relative. Matched-pair assignment adds further reduction only if the analysis carries the pairing.

Verify it yourself

Adjusted and unadjusted point estimates must agree well within one standard error; a larger shift means the covariate is post-treatment or the assignment is imbalanced.
theta-hat should equal the OLS slope of Y on X in the pooled data.
The realised variance ratio between adjusted and unadjusted outcomes should land near 1 - rho-hat^2; if it does not, rho was estimated on a window that does not match the flight length.
An A/A replay across historical windows should produce 95% intervals covering zero about 95% of the time.

Follow-up

CUPED moves the point estimate by 30% of its own standard error. What do you conclude?
Would you use pre-period conversions or pre-period spend as the covariate, and why?
How would the plan change if only 20 markets were available?

Bid raised fifteen percent, pooled win rate went down

Medium · win rate · simpsons paradox · auction mechanics

A line item's bid_value_usd was raised 15% at 09:00 UTC. Over the following week its pooled auction win rate — bid_request_log rows with win_flag=TRUE over rows with response_status='bid' — fell from 12.4% to 11.1%, while impressions won and spend both rose. The trading team wants to roll the change back. You have bid_request_log with exchange_id, publisher_id, ad_format, auction_type, floor_price_cpm_usd, no_bid_reason, bid_cpm_usd, clearing_price_cpm_usd. Explain the direction of the pooled rate and recommend keep or roll back.

Approach

Notice the internal contradiction before touching data: more wins and more spend with a lower win rate means the bid denominator grew faster than wins. The question is therefore which bids are new, not whether the bid worked.
Stratify on the variable the bid change directly acts on — floor_price_cpm_usd bucket, crossed with auction_type — and compute per-stratum win rate and bid share before and after. A higher bid converts no_bid_reason='below_floor' rows into bids in exactly the high-floor, low-win-rate strata.
Run a Kitagawa decomposition of the pooled change into a within-stratum component (Σ share_post × Δrate) and a mix component (Σ Δshare × rate_pre), and report both numbers. Pooled direction is not evidence about either one on its own.
Confirm the mechanism in the no-bid ledger: the fall in below_floor no-bids should approximately equal the growth in bid rows in the high-floor strata. If it does not, something else expanded eligibility, such as a pacing_throttle release.
Judge the change on economics, not on the rate. Under first_price clearing the bid increase also raises clearing_price_cpm_usd on inframarginal wins, so compare incremental spend against incremental conversions rather than counting impressions.

Follow-up

Under first-price clearing, how much of the extra spend went to impressions the line item would have won at the old bid?
What would you expect the same decomposition to look like under second_price, and why is the inframarginal cost argument weaker there?
The pooled rate is a dashboard tile. What would you replace it with so this cannot recur?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

Describe a situation where you disagreed with a cross-functional partner on project direction and how you resolved the conflict.

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Name the disagreement or constraint, and how you resolved it with evidence.
State the situation in two sentences and spend the rest on your reasoning.

Follow-up

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

Defending a null incrementality result against a headline ROAS

Hard · influence · incrementality · attribution · conflict

Your geo holdout shows a retargeting line item's incremental conversions per $1,000 of spend are indistinguishable from zero. The same line item reports last-click ROAS of 8.3 and is the headline number in a renewal deck presented in six days. The account team believes your test is wrong; the advertiser has never questioned the 8.3. Deliverable: the internal case you make, the specific evidence you bring, the limits you concede, and the smallest reversible experiment you propose if your reading is rejected.

Approach

The interviewer is probing whether you can hold an unpopular finding without turning it into a credibility fight, so open by granting that both numbers are computed correctly: last-click measures which touchpoint was nearest in time, the holdout measures what changed because of the spend. Framing attribution as 'wrong' converts a methodological point into a turf argument you will lose on relationship grounds.
State the null as a bound rather than as zero. Report the minimum detectable effect the 20-market design could resolve and say 'we can rule out incremental ROAS above this value'. A null without an MDE is not a finding and is trivially dismissed as an underpowered test.
Bring converging evidence from tables you already have, and say what each piece can and cannot show. Credit sensitivity: recompute the same conversion cohort under a second attribution_model, first-touch or even credit across in-window touchpoints, holding cohort and windows fixed, and report what share of the line item's credited volume survives; that bounds how much of the 8.3 is a property of the crediting rule rather than of the spend, and it is a sensitivity result, not a lift estimate. Harvesting signature: the share of retargeting-credited conversions whose user already had an earlier impression, click or site session inside click_window_days, together with the distribution of conversion_ts minus click_ts, where a heavy mass inside a few minutes is consistent with an ad served into a session that was already converting.
Quantify both error directions before recommending anything: the spend at risk if you are right and ignored, and the renewal revenue at risk if you are wrong and act. This is what separates a defensible position from an ideological one.
Propose the reversible version rather than the shutdown: a 20% budget reduction in half the matched markets for six weeks, powered against the MDE you just computed, with the analysis window extended past the flight by the click window plus ingestion lag.

Follow-up

The account lead says telling the advertiser their favourite channel does nothing will cost us the renewal. How do you respond without either caving or escalating?
What specific evidence would change your mind about this line item?
How do you handle it if the budget-down test also comes back underpowered?

Ranking three teams' requests against one sprint of capacity

Medium · prioritisation · stakeholder-management · delivery

You have three requests and capacity for roughly one and a half. The supply team wants eligible-RPM impact sizing for a floor-price change shipping in two weeks. An advertiser team wants a pacing diagnosis for a $400k line item delivering at 60% of budget. The invalid-traffic team wants help validating a new sophisticated-invalid-traffic classifier before it restates last quarter. Deliverable: your ranking with the reason for each, exactly what you say to the two teams you deprioritise, and the trigger that would reorder the list.

Approach

The interviewer is probing whether you rank on decision structure or on social pressure, so attach two attributes to each request before ranking: the date the decision is actually made, and how expensive it is to reverse afterwards. Seniority of the requester is not one of the inputs.
Look for the request that can be collapsed rather than sequenced. The pacing diagnosis is usually a single grouped query over bid_request_log by no_bid_reason and response_status per hour for that line_item_id; time-boxed to two hours it stops competing for the sprint at all.
Size the other two in revenue at risk per week so the ranking is arguable rather than asserted: the floor change's expected eligible-RPM delta against the spend it touches, and the invoiced spend inside the period the classifier would restate.
Rank on irreversibility where deadlines are close together. A classifier that restates already-invoiced periods is the hardest of the three to unwind once shipped, which can outrank a nearer deadline.
Deprioritise with a date and a fallback rather than an apology: name what you will not do, when you could, the smallest thing you can give them now, and who else could take it.

Follow-up

The underdelivering advertiser escalates to your director. What actually changes in your plan, and what do you refuse to change?
How do you stop the pacing question arriving fresh every sprint?