Digitas Data Scientist Interview Questions & Guide 2026

The 60-second brief

At Digitas, the Data Scientist role is a cornerstone of our DNA (Data and Analytics) department. We don’t just process numbers; we translate complex consumer behaviors into actionable marketing strategies. As a member of the DNA team, you will be responsible for bridging the gap between raw data and creative storytelling, ensuring that our global clients can deliver highly personalized and effective brand experiences.

Ask early whether the loop includes an asynchronous take-home or a timed live case, because the two are graded on different things. A take-home is read as an artifact: the question you decided to answer, what you did about missing or malformed records, and a conclusion stated plainly enough for someone to act on. A reviewer who cannot rerun your notebook discounts the result whatever score is printed in it. Hold to the stated time box and write down what you would have done with more of it, since the follow-up round is usually a live defence of the same work.

Focus areas

• Decompose a metric move by segment and mix
• Pick a randomisation unit that respects interference
• Define numerator, denominator and window precisely

Overview

Your work will directly influence multi-million dollar marketing campaigns by applying machine learning, predictive modeling, and advanced statistical analysis to diverse datasets. Whether you are optimizing media spend, building recommendation engines, or performing deep-dive churn analysis, your insights will drive the strategic direction for some of the world's most recognizable brands.

What makes this role unique is the intersection of high-level technical rigor and agency-style agility. You will work in a fast-paced, collaborative environment where your ability to communicate the "why" behind the data is just as important as the code you write. You aren't just building models in a vacuum; you are shaping the future of how brands and people connect.

The Digitas interview process

Digitas candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

• Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
• Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
• Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

• Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
• Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
• Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Evaluation

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

• Whether an ambiguous term becomes a specific column and filter before any computation happens
• Whether you read the schema for keys and cardinality rather than only for column names
• Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

• Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
• On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
• Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Presentation Challenge

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

• Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
• Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
• How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

• Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
• For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
• Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Culture Fit Session

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

• Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
• Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
• Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

• For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
• Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
• Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.

Where candidates lose points

Reading a pooled rate that moved because the mix moved, not because any behaviour changed

A pooled conversion rate is a weighted average, and a shift in the weights can move it in the opposite direction to every one of its parts. A paid campaign that brings low-converting traffic drops overall signup conversion even if desktop, mobile web and app conversion each rose that week, which is Simpson's paradox and it is the single most common cause of an inexplicable dashboard move. The discipline is to decompose before explaining: recompute the rate holding last period's segment weights fixed, and compare that counterfactual to the actual, so the mix effect and the rate effect are separated numerically rather than argued about. Segment on the dimensions that actually reweight, which in this domain are almost always device_type, referrer_channel, country and new versus returning.

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.

Ignoring interference between units in a marketplace experiment

Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.

Reporting a mean for a heavy-tailed metric without saying what it hides

For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.

Questions & practice

How would you validate a time-series model used for forecasting sales?

Medium · machine learning and modelling

Approach

1. Set a baseline first, so any model has something honest to beat.
2. Check what information would not exist at prediction time, and exclude it.
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?

What are the assumptions of linear regression, and what happens if they are violated?

Medium · machine learning and modelling

Approach

1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
2. Pick an evaluation metric that matches the cost of each error type, not a default.
3. Check what information would not exist at prediction time, and exclude it.

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?

Cluster bootstrap for a per-session rate randomised on users

Hard · cluster bootstrap · randomisation unit · variance

An experiment randomised on user_id reports a per-session conversion rate, so sessions inside a user are correlated. Input: one row per session with user_id, variant in {control, treatment} and converted in {0,1}. Write a cluster bootstrap from scratch: resample users with replacement within each arm, keep every session of a drawn user, recompute each arm's ratio of converted sessions to sessions, and take the difference. Return the point estimate, a 95 percent percentile interval from at least 2,000 resamples, the naive session-level interval that ignores clustering, and the ratio of their widths.

Approach

1. Name the estimand precisely: it is a ratio of sums, sum(converted) over sum(sessions) within an arm, not the mean of per-user rates. Those differ whenever session counts vary across users, and the ratio is what the reported metric is.
2. Resample the cluster, not the row. Draw n_users user ids with replacement inside each arm and take every session belonging to each draw, including duplicate draws of the same user. Keeping the user count fixed per arm rather than the session count is what preserves the sampling design.
3. Precompute per-user (converted_sum, session_count) once, so each resample is two vector lookups and a division rather than a repeated filter over the session frame. That turns 2,000 resamples from minutes into under a second.
4. Take the 2.5th and 97.5th percentiles of the 2,000 differences for the interval, and report the point estimate from the full data rather than from the bootstrap mean, since the bootstrap mean carries the resampling bias.
5. Compute the naive interval from the session-level binomial standard error and compare widths. The expected inflation is roughly sqrt(1 + (m-1)*rho), with m the mean sessions per user and rho the intraclass correlation of converted within users, so a computed ratio far from that value points at a bug in one of the two intervals.

Worked solution (35 min)

1. per_user = df.groupby(['variant','user_id'])['converted'].agg(['sum','size']); split into two arrays per arm.
2. point = (t_sum.sum() / t_n.sum()) - (c_sum.sum() / c_n.sum()).
3. For b in range(B): idx = rng.integers(0, len(t_sum), len(t_sum)); ratio_t = t_sum[idx].sum() / t_n[idx].sum(); same for control; store the difference. Vectorise by drawing a (B, n) index matrix if memory allows.
4. ci = np.percentile(diffs, [2.5, 97.5]); naive_se = sqrt(p_t*(1-p_t)/n_sessions_t + p_c*(1-p_c)/n_sessions_c); naive_ci = point +/- 1.96*naive_se.
5. width_ratio = (ci[1]-ci[0]) / (naive_ci[1]-naive_ci[0]).

Expected result: A point estimate identical to the full-data ratio difference, a percentile interval containing that point estimate, and a width ratio greater than 1 whose value approximates sqrt(1 + (m-1)*rho) for the data's mean cluster size m and intraclass correlation rho. A ratio of approximately 1.0 means sessions were resampled instead of users.

Verify it yourself

• Construct data where every user has exactly one session: the cluster and naive intervals then agree to within Monte Carlo error, since m = 1 makes the design effect 1.
• Construct data where converted is constant within each user: rho is 1, and the width ratio should approach sqrt(m).
• The bootstrap distribution's mean sits close to the point estimate; a large offset means the resampled arms have drifted in size.
• Doubling B narrows the Monte Carlo noise in the percentile endpoints without shifting the interval's centre.

Follow-up

• Users average 3.4 sessions and the intraclass correlation is 0.12. What width ratio do you predict before running it, and does your bootstrap land there?
• Give the delta-method standard error for this ratio and say when you would prefer it to the bootstrap.
• Half the users in the treatment arm have exactly one session. What does that do to the cluster bootstrap's coverage, and how would you check it?

What is the difference between a WHERE clause and a HAVING clause?

Medium · sql and data manipulation

Approach

1. Say which table is the grain you start from, and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. 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?
• What breaks if events arrive late or out of order?

How do you perform a self-join, and when would you use one?

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. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

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

Explain the use of window functions like RANK and ROW_NUMBER.

Medium · sql and data manipulation

Approach

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

Follow-up

• How would you verify this result without re-running the same query?
• How does the query change if the join becomes one-to-many?

Write a query to find the second-highest transaction amount for each user.

Medium · sql and data manipulation

Approach

1. State the window function and its partition and ordering out loud before writing it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

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 weekly signup cohort

Easy · cohorts · activation · date-bounds

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

Approach

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

Worked solution (20 min)

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

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

Verify it yourself

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

Follow-up

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

Metric tree for a seat-expansion push on seat-based plans

Medium · revenue · seats · leading indicators

Sales wants a push to add seats to existing accounts. Tables: dim_account (account_id, seats_licensed, seats_assigned, lifecycle_status, current_plan_tier), fct_subscription_period (subscription_id, account_id, mrr_cents_constant_fx, seats_billed, period_start_utc, period_end_utc, change_reason), and fct_event (is_core_action, user_id, account_id, occurred_at_utc). Build the metric tree from net revenue retention down to the metric the push team is graded on, name the primary, and give the guardrail that fires before the damage shows up in NRR. Deliverable: the tree, the primary metric, and the lead-time argument for the guardrail.

Approach

1. Grade the team at the grain it controls. NRR is a trailing twelve-month revenue-weighted ratio that the push cannot move inside a quarter and that also moves for reasons the push does not touch, so it belongs at the top of the tree and nowhere near the target.
2. Define the primary as a delta rather than a count: the sum of period-over-period mrr_cents_constant_fx changes on rows with change_reason = 'seat_change' and a positive seats_billed delta. A COUNT of seat_change rows rewards churn of seats as much as growth.
3. Name the failure the target creates: seats sold and never assigned book revenue now and reappear as a seat reduction at the next renewal, six to twelve months later, comfortably outside the window the team is measured in.
4. Pick licensed-seat utilisation as the guardrail precisely because of that lag — distinct user_id with an is_core_action event in the trailing 28 days over seats_licensed, MRR-weighted across lifecycle_status = 'active' accounts — and read it against each account's renewal date rather than the calendar.
5. Close the horizon gap with a rule instead of a hope: the push books expansion MRR immediately but retains credit only for accounts still above the utilisation threshold at the following renewal.

Follow-up

• How do you separate seat expansion from price and tier expansion inside NRR without double-counting either?
• An account buys 40 seats and assigns 6 during a phased rollout. Is that expansion, and what would change your answer?
• What does the utilisation threshold do to a genuinely growing customer three weeks into a rollout?

A test that won on day four and lost by day twelve

Medium · peeking · sequential testing · multiple comparisons · winner's curse

A two-week test was checked every morning. On day four the primary metric crossed p = 0.03 and the team shipped. By day twelve, with logging still running, the estimate had fallen to a 0.2% lift at p = 0.61. The day-four deck also reported a significant 9% lift among paid_search visitors on mobile_web, one of twenty segments inspected. Explain both patterns quantitatively, say what the day-four evidence was actually worth, and specify the monitoring rule that prevents a repeat.

Approach

1. Price the peeking. A fixed-sample test controls type I error at one pre-declared look. Ten daily looks at a nominal 5% raise the false positive rate to roughly 0.15 to 0.20, because the running test statistic takes a random walk that will eventually cross the boundary under the null.
2. Price the winner's curse separately, because it is a different error. Conditioning an estimate on having crossed the threshold biases it away from zero, by a factor of roughly 3 when true power is 20% and only about 1.1 at 80% power. Day four sits at roughly 29% of planned information, which is exactly the low-power case.
3. Read day twelve as the better estimate rather than as a contradiction. Nothing regressed; the day-four value was an extreme draw that was selected precisely for being extreme.
4. Price the segment claim. Across twenty segments at a nominal 5%, at least one crosses about 64% of the time under a global null, since 1 - 0.95^20 = 0.64, and the slice that surfaces is by construction the one with the most favourable noise.
5. Specify the rule instead of scolding. Either fix the horizon and look once, or use group-sequential boundaries such as O'Brien-Fleming that spend almost no alpha early, or use always-valid confidence sequences that stay correct under continuous monitoring. Pre-register the small set of segments, correct across the ones tested, and treat anything found afterwards as a hypothesis needing its own powered test.

Worked solution (25 min)

1. Count the looks actually taken, twelve daily, and state the inflated alpha band of 0.15 to 0.20 for ten looks, rising with more.
2. Place day four on the information fraction, about 4/14 = 29% of planned sample, and apply the exaggeration factor for low power.
3. Compute 1 - 0.95^k for the number of segments actually inspected; with k = 20 this is 0.64.
4. Report the day-twelve interval as the estimate and say plainly that the experiment supports no effect at any size the team would ship for.
5. Write the rule into the experiment template: horizon and segments declared at launch, sequential boundaries if interim looks are needed at all.

Expected result: The day-four result is consistent with pure noise: ten looks inflate the false positive rate to roughly 0.15 to 0.20, and an estimate conditioned on crossing at about 29% information is exaggerated by roughly a factor of three at low power. The segment finding appears about 64% of the time across twenty slices under a global null. The supportable conclusion is a 0.2% lift whose interval covers zero.

Verify it yourself

• 1 - 0.95^20 = 0.6415. If your family-wise number lands near 0.05 you have computed the per-test rate instead.
• The day-twelve standard error should be roughly sqrt(12/4) = 1.7 times smaller than day four's. If the interval did not narrow, sample was not accumulating and there is a second problem.
• A Bonferroni threshold for twenty segments is 0.0025; check whether the mobile_web result would have survived it.

Follow-up

• Under O'Brien-Fleming with five planned looks, roughly what nominal p-value would the first look have needed to stop?
• The feature already shipped. What analysis can you still run, and what can it not tell you?
• If mobile_web paid_search were genuinely the only affected segment, how would you design the confirmatory test and how large would it have to be?

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

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.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Why Digitas, and why do you want to work in the agency space?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Describe a data project where you failed. What did you learn?

Medium · behavioural and stakeholder questions

Approach

1. Close with what you would do differently, concretely.
2. Name the disagreement or constraint, and how you resolved it with evidence.
3. Quantify the outcome, including what you would not claim credit for.

Follow-up

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

Choose between three teams' requests with one analyst-week

Medium · prioritisation · stakeholder · judgement

You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.

Approach

1. Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
2. Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
3. Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
4. Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
5. Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.

Follow-up

• The dashboard team escalates to your manager. What do you say in that conversation?
• Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
• A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?