Interview guides · Akamai

Akamai Data Scientist Interview Questions & Guide 2026

Akamai data scientist prep: probability, sampling distributions, and stating the assumption behind every test you run.

The 60-second brief

A Data Scientist at Akamai operates at the intersection of massive-scale distributed systems, cybersecurity, and advanced analytics. As the operator of the world’s largest edge decision platform, Akamai routes a significant portion of all global web traffic. In this role, you will not simply build models in isolation; you will develop intelligent systems that analyze petabytes of real-time network telemetry to protect enterprises from sophisticated cyber threats, optimize content delivery, and ensure seamless digital experiences for billions of users.

The boundary with engineering varies enough to be worth asking about directly. Some seats end at the analysis and the writeup; others carry feature pipelines, scheduling and alerting, which decides whether production data plumbing belongs in your prep at all.

Focus areas

Power experiments for heavy-tailed account revenue
Analyse at the account grain, cluster errors
Strip CI, retry and synthetic traffic first

Overview

The impact of your work is immediate and highly visible. Whether you are building anomaly detection algorithms to mitigate zero-day DDoS attacks, optimizing DNS routing protocols, or deploying machine learning models to identify botnets, your solutions must be highly performant, scalable, and resilient. This requires a unique blend of deep statistical knowledge, strong software engineering discipline, and a solid understanding of network infrastructure.

Working as a Data Scientist here means tackling challenges that few other companies face. The sheer volume of data means that traditional, off-the-shelf algorithms often fail at this scale. You will be expected to innovate, design custom modeling pipelines, and collaborate closely with security researchers, network engineers, and product managers to turn raw network packets into actionable, automated security decisions.

The Akamai interview process

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

Recruiter 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

Hiring Manager Conversation

Expect a live problem with pieces of it missing, closer to a conversation than an exam. A metric moved, or somebody wants to know whether a change worked, and you are asked how you would find out. The manager is watching the first ninety seconds, specifically whether you establish what decision hangs on the answer before you start proposing methods. Candidates who open with a technique get steered back. Once the decision is clear, describe what the data would look like if the story were true, and say what you would accept as evidence that it is not.

What to demonstrate

Whether you fix the decision the analysis serves before choosing an approach
How you continue when you are told the data you just asked for does not exist
Whether you state what would change your mind, not only what would confirm the hypothesis you started with
How you size an effect before you have measured it

How to prepare

Take a metric you know well and practise explaining in under two minutes the four things that could have moved it and how you would separate them
Pick a recent launch or experiment and write the single number you would ask for first, plus what you would conclude if it came back flat
Practise being interrupted: have someone remove a data source halfway through your answer and carry on without restarting

Technical Rounds

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Take-Home Assessment

The clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.

What to demonstrate

Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived

How to prepare

Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight

Specialized Interviews

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Reading consumption metrics before the metering lag window has closed

Usage pipelines land late and correct themselves, which is exactly what is_restated and restated_at record. A dashboard queried on day T sees a partially populated tail for the last several days, so the most recent points always slope downward and always look like a regression. Analysts then explain the artefact, and sometimes ship a change to fix it. Establish the empirical settling time by measuring how much a given usage_date's total moves between first_written_at and its final value, exclude that many trailing days from every reportable figure, and never compare a fresh period against a settled one.

Reporting a mean over accounts when account revenue is heavy-tailed

When a small number of accounts hold most of the revenue, the sample mean is dominated by whichever of them happens to be in the sample, and the sample variance keeps growing as more data arrives instead of stabilising. In that regime the usual central-limit-based confidence interval understates uncertainty, and a single renewal or a single large account's batch job can flip the sign of a measured effect. The fixes are to pre-register a winsorisation or capping rule before looking at the outcome, to report account counts crossing a threshold alongside the revenue figure, or to define the estimand on a bounded transform. Choosing the cap after seeing the result is a separate and worse problem, because the cap then encodes the answer.

Crediting a treatment for regression to the mean

Selecting a group because it is extreme (lowest-engagement users, accounts having their worst month, the bottom decile of a score) moves that group's expected next-period value back toward the average even under no treatment, by exactly as much as the selecting measure is imperfectly correlated with its own later value. Compare against units that met the same selection rule and went untreated, or use two pre-periods so the bounce-back is visible before the intervention starts. A pre-post number on a group chosen for being extreme measures the selection rule, not the treatment.

Optimising accuracy on a heavily imbalanced target

State the base rate first, then choose the metric from the relative cost of a false positive against a false negative: precision and recall at the operating threshold, PR-AUC, or expected cost. At a 1 percent positive rate, predicting the majority class for everyone scores 99 percent accuracy and is worthless.

Questions & practice

Implement an algorithm to parse and extract key features from a raw server log file.

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?

What metrics would you use to evaluate a classification model where false negatives are significantly more expensive than false positives?

Medium · machine learning and modelling

Approach

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

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?

Simulate how the renewal calendar distorts monthly churn rates

Medium · simulation · churn · numpy · cohorts

Simulate 1,200 accounts on annual contracts. Draw each account's renewal month from a deliberately lumpy calendar: 30% renew in January and the remaining 70% are spread evenly over the other eleven months. At each renewal an account churns with probability 0.18, independent of month; survivors renew and come back twelve months later. Run 24 simulated months. For each month compute two rates: churned accounts over all live accounts, and churned accounts over accounts whose term ended that month. Report the mean and the month-to-month standard deviation of each series, and state which one belongs in an executive summary.

Approach

Build the panel with numpy state arrays rather than a per-account loop: a next_renewal_month vector, an alive boolean vector, and a loop over the 24 months only. Looping over 24 months is fine; looping over 1,200 accounts inside it is what makes the simulation too slow to iterate on.
Maintain the live set honestly. An account that churned in month m must leave the denominator from m+1 onward and can never be renewal-eligible again; if it stays, the naive rate drifts downward for reasons that have nothing to do with churn and the calendar effect gets buried.
Compute both series over the same months and compare dispersion, not only level. The eligible-base rate should sit near 0.18 with binomial noise scaled by that month's renewal count; the naive rate spikes in January and collapses in thin months.
Quantify the gap instead of describing it: the ratio of the two means is roughly the reciprocal of the average monthly renewal-eligible fraction, and the naive series' standard deviation is driven by the signing calendar rather than by customer behaviour.
Check against the closed form before trusting the output. With the live set maintained correctly the eligible-base rate is an unbiased estimator of 0.18 in every month, so a systematic offset means the bookkeeping is wrong, not that the simulation found something.

Worked solution (30 min)

rng = np.random.default_rng(0); p = [0.30] + [0.70/11]*11; next_renewal = rng.choice(12, size=1200, p=p); alive = np.ones(1200, bool)
For m in range(24): eligible = alive & (next_renewal == m); churn = eligible & (rng.random(1200) < 0.18); record churn.sum(), eligible.sum(), alive.sum() at month start; alive &= ~churn; next_renewal[eligible & ~churn] += 12
naive = churned / live_at_start; eligible_rate = churned / eligible, left as NaN where eligible == 0.
Report naive.mean(), naive.std(ddof=1), np.nanmean(eligible_rate), np.nanstd(eligible_rate, ddof=1) and the ratio of the two means.

Expected result: The eligible-base rate averages close to 0.18 (roughly 0.17 to 0.19 at this sample size) with no January spike. The naive rate averages near 0.015, about one twelfth of it, and its January values run several times a typical month, so its month-to-month standard deviation is a large fraction of its own mean.

Verify it yourself

The 24 monthly churn counts sum to 1200 minus the final alive count.
Switching the calendar to uniform (p = 1/12 everywhere) collapses the naive series' dispersion while leaving the eligible-base mean unchanged.
Setting the churn probability to 0 makes both series identically zero and leaves 1,200 accounts alive.
Quadrupling the account count roughly halves the standard deviation of the eligible-base rate without moving its mean.

Follow-up

Compounded over twelve months the naive rate lands close to the true annual churn. Does that rescue it?
How would you report churn in a month where only nine accounts were renewal-eligible?
Eighteen-month terms are now being sold alongside annual ones. What breaks?

Write a SQL query to identify anomalous traffic spikes from a database containing billions of CDN request logs.

Medium · sql and data manipulation

Approach

Say which table is the grain you start from, and join outward from it.
State the window function and its partition and ordering out loud before writing it.
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?

Explain the step-by-step process of how a DNS query is resolved, and how you would analyze DNS latency data to identify bottlenecks.

Medium · sql and data manipulation

Approach

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

Follow-up

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

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?

What are the primary differences between Python and R for data manipulation, and why might you choose one over the other for production-level pipelines?

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

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?

Can you explain the mathematical trade-offs between supervised and unsupervised learning when designing an anomaly detection system?

Medium · product sense and metrics

Approach

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

Follow-up

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

How would you optimize a Python or R script that is running out of memory while processing a large dataset?

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.
Name one primary metric, then the guardrail that stops it being gamed.

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?

How do network protocols like TCP/IP and HTTP impact the collection and structure of the data you use for modeling?

Medium

Approach

Clarify what is being asked and what a complete answer would contain.
Say what you would check first and why it is the highest-information step.
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?

Size an account-randomised test of a consumption feature

Easy · power · mde · heavy-tailed-outcomes

You have 2,800 non-internal paying accounts. The outcome is the monthly sum of billable_quantity for sku_code = 'compute_hours' from fct_usage_daily, with a mean of 640 and a standard deviation of 2,100 across accounts. Product wants to detect a 5% lift from a scheduling feature, randomised 50/50 at the account level, at 80% power and two-sided alpha 0.05. Give the minimum detectable effect the available sample actually supports, say whether the 5% target is reachable, and state what you would change in the design or the metric before agreeing to run it.

Approach

Write the two-sample formula and plug it before discussing anything else: MDE = (z_0.975 + z_0.80) * sd * sqrt(1/n1 + 1/n2) = 2.80 * 2100 * sqrt(2/1400) = 222 compute-hours, which is 34.7% of the 640 mean. The driver is the coefficient of variation of 3.3, not the account count, so quoting a sample size without it is not a power calculation.
Invert for the stated target. A 5% lift is 32 compute-hours, needing N = 4 * (2.80 * 2100 / 32)^2 = about 135,000 accounts, roughly 48 times the eligible population. Say the test is not powerable on this metric, and do not offer to run it longer: the account population is fixed, and extra weeks add repeated observations on the same clusters rather than new clusters.
Attack the variance instead of the sample. Pre-register winsorisation at the 99th percentile of the pre-period distribution, or move the estimand to a bounded one such as the share of accounts exceeding a usage floor. State explicitly that this changes the estimand rather than cleaning the data, because a cap chosen after seeing the result encodes the answer.
Add CUPED on the account's pre-period compute_hours. At a pre/post correlation of 0.80 the residual variance multiplier is 1 - 0.80^2 = 0.36, the standard error falls to 0.60 of its unadjusted value, and the MDE drops from 222 to 133 compute-hours (20.8% relative). That is still four times the requested 5%.
Fix the randomisation unit and the balance in the same breath: randomise account_id because colleagues share workspaces, and stratify assignment on pre-period usage decile so that a handful of very large accounts cannot land disproportionately in one arm and dominate the difference in means.
Close with an operating decision rather than a number. Run only if the team will act on a roughly 20% MDE after CUPED and winsorisation; otherwise re-specify the primary outcome as a bounded indicator and treat compute-hours as a secondary reported with intervals only.

Worked solution (25 min)

Compute the coefficient of variation, 2100 / 640 = 3.28, and check the top-decile share of total compute_hours before treating the mean as a summary of anything.
Compute MDE = 2.80 * 2100 * sqrt(1/1400 + 1/1400) = 2.80 * 2100 * 0.0378 = 222 compute-hours, 34.7% relative.
Invert to the required N for a 32-hour effect: 4 * (5883 / 32)^2 = about 135,000 accounts, 48x the population.
Recompute under CUPED at rho = 0.80: variance multiplier 0.36, SE multiplier 0.60, MDE 133 compute-hours, 20.8% relative.
Recompute on the binary variant at base rate 0.46 and write the recommendation as one decision with one operating point.

Expected result: MDE is about 222 compute-hours (34.7% relative) as specified and about 133 (20.8%) with CUPED at rho = 0.80. A 5% lift would require roughly 135,000 accounts, about 48 times the eligible population, so the test is declined on this metric and re-specified on a bounded outcome where the MDE falls to about 5.3 percentage points on a 0.46 base.

Verify it yourself

sqrt(1/1400 + 1/1400) = 0.0378, so the MDE must land within a few percent of 2.80 * 2100 * 0.0378 = 222. A materially different number means a one-sample or one-sided formula was used.
Required N scales with the inverse square of the effect size: halving the target MDE must quadruple N. If it does not, the formula is wrong.
The CUPED variance multiplier is 1 - rho^2, never 1 - rho. At rho = 0.80 the standard error falls by 40%, not by 20%.

Follow-up

The feature only applies to the 310 accounts on committed_consumption contracts. What is the MDE on that subset, and does it change the recommendation?
Suppose the primary outcome becomes a binary indicator, account exceeded 100 compute-hours in the month, at a base rate of 0.46. What is the MDE now, and why did it improve so much?
What would make you stop this test early, and how do you write that rule so it is not peeking?

Consumption per account always dips in the last four days

Easy · pipeline lag · restatement · reporting windows

Your consumption dashboard reads billable units per paying account from fct_usage_daily (account_id, sku_code, usage_date, billable_quantity, net_amount_cents, is_restated, first_written_at, restated_at, updated_at). Every refresh shows the final three to five days declining, and a review is scheduled on that shape. Establish empirically how long a usage_date takes to settle, separately per sku_code, and specify the trailing exclusion window every reported figure should use. Derive the number from the data; do not adopt a convention.

Approach

Treat settlement as a measurable curve rather than a belief: for each historical usage_date, compare the total captured as of age d days against that date's final settled total.
Do it per sku_code, because metering paths differ. A per-request SKU lands within hours, while a storage-month SKU is produced by a daily sweep and lands later, so one global lag number is wrong for at least one of them.
Pick the age at which a stated percentile of dates reaches a stated completeness threshold, for example the tenth percentile date reaching 99.5 percent of final, and take the slowest SKU's age as the dashboard's exclusion window.
Separate late arrival from restatement. Late rows raise totals and are fixed by waiting; restatements can move either way and are not, so report the share of the gap from each.
Encode the exclusion inside the query rather than in the chart, so anyone reusing the SQL inherits the rule, and never compare a fresh partial period against a settled one.

Follow-up

Month-end invoicing needs a number before settlement completes. How would you publish an early estimate with an honest uncertainty band attached?
What monitor would tell you the settling time has changed, without anyone remembering to re-run this analysis?

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.

How do you stay updated with the latest advancements in data science and cybersecurity?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Quantify the outcome, including what you would not claim credit for.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

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

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?

Scope an open-ended request to predict account churn

Medium · scoping · churn modelling · operating point

A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.

Approach

The interviewer is probing whether you convert a vague request into a decision with a capacity constraint attached. A candidate who starts talking about model families has already failed the exercise.
Pin the event and the horizon first. Churn is only possible at term_end_date, so the population is accounts renewing in the next 60 to 90 days, not the whole base. Ask explicitly whether contraction and downgrade count as churn or only full non-renewal, because the three have different base rates and different interventions.
Pin the action and the capacity. Six people times a realistic number of meaningful interventions per week gives k, and k is what the list is ranked to. Evaluate on precision at k rather than a global AUC over accounts that will never be contacted.
Audit leakage before choosing features. Every feature needs a timestamp proving it existed before the prediction date. A downgrade amendment, a churn reason code, and a ticket opened after the renewal conversation started are all leaks that will make the offline number look excellent and the live list useless.
Ask for the counterfactual now rather than later. Coverage is assigned deliberately, so without a held-out slice agreed at the start the intervention can never be evaluated, and you will be asked for its impact in nine months regardless.
Propose the smallest artefact that closes the loop: a weekly ranked list sized to capacity with two or three inspectable reasons per row, plus a stated policy for accounts below the line.

Follow-up

The director insists all accounts are in scope, not only those renewing soon. How do you answer without simply refusing?
Historical non-renewals number about 30 a year. At what point do you tell them a model is the wrong tool and a rules list is better?
Which candidate features would you drop purely because you cannot date them?