A Data Scientist at Taboola plays a pivotal role in driving insights and innovations that enhance user experiences and optimize advertising strategies. This position is integral to understanding user behavior and preferences, leveraging vast datasets to inform product development and marketing initiatives. By analyzing data patterns and trends, Data Scientists contribute significantly to the effectiveness of Taboola's content recommendation algorithms, directly impacting user engagement and satisfaction.
The complexity and scale of the data handled by Data Scientists at Taboola make this role both challenging and rewarding. You will work with cross-functional teams, including engineers and product managers, to develop models and algorithms that inform business decisions and improve content delivery. The insights generated will not only support internal stakeholders but also shape the overall strategy of the company, making this position critical to achieving Taboola's mission of connecting users with relevant content.
HR Call
reportedAn 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.
Technical Interviews
reportedMuch 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 Assignment
reportedBefore any modelling, the dataset is itself the first test. Take-home data usually carries something broken: rows duplicated at an unexpected grain, a join that silently drops part of the population, timestamps stored in more than one timezone, or missingness correlated with the outcome. An hour spent profiling row counts, key uniqueness and date ranges is not overhead, because it decides whether every number after it is real. What separates submissions is whether you report the defects you found and adapt the analysis to them, rather than modelling over them quietly and hoping the aggregate absorbs it.
What to demonstrate
- Whether you established the grain of each table and checked row counts after every join, and said so in the writeup
- Whether data defects you found are surfaced with their effect on the conclusion, instead of being dropped without comment
- Whether filters and exclusions are reproducible from the submitted code, with the size of the excluded population quantified
How to prepare
- Write a short profiling script you can point at any unfamiliar table: row count, distinct key count, null rate per column, and the min and max of every date field, then run it before anything else
- Write the funnel or the join chain as one query and check the row count at each grain, so a silent fan-out shows up as a number rather than as a wrong answer later
- On a past dataset, list every exclusion you applied and how many rows each one removed, then draft the single sentence about it you would put in a report
Discussion of Assignment
reportedYour submission is read asynchronously by someone who cannot ask you a clarifying question, and who will usually skim it before reading it properly. That changes what good looks like. The conclusion belongs near the top with the supporting analysis beneath it, and the code should run start to finish on a clean machine without a manual step you forgot to document. A reviewer forced to reconstruct your reasoning from the order of notebook cells is already discounting the work. The submissions that land are the ones where a busy reader gets the answer immediately and can verify it if they want to.
What to demonstrate
- Whether the answer arrives before the methodology, so a reader who stops after the first page still has the recommendation
- Whether the code runs end to end from the submitted files, with dependencies and data paths declared rather than assumed
- Whether each chart is legible on its own, carrying axis labels and units, and exists to support a claim made in the text
How to prepare
- Restructure a past analysis so the opening paragraph holds the recommendation and the number behind it, then check that nothing later in the document quietly contradicts it
- Copy your own submission into an empty directory, run it in a clean environment, and fix everything that breaks; hidden local state is caught here or by the reviewer
- For every chart, write the one sentence it is meant to prove, and delete the chart if you cannot write that sentence
Team Engagement
reportedBecause the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.
What to demonstrate
- Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
- Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
- Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs
How to prepare
- For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
- Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
- For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.
PracHub editorial advice for the preparation topics above.
Counting conversions without deduplicating across reporting sources and identity spaces.
The same purchase routinely arrives twice, once from a browser pixel and once from a server-side API, and the two reports carry different identifiers, so a naive count inflates conversions and deflates cost per action by the duplication rate. Deduplication depends on the advertiser populating a shared dedup_key correctly, which many do not, and the failure is silent because both rows look individually valid. The mirror-image error is under-counting: when a user clicks on a mobile app and converts in a desktop browser, the identity join fails and a real conversion is attributed to nothing, which makes the channel look worse than it is. Any conversion count reported without stating the dedup rule and the identity-match rate behind it should be treated as unverified.
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.
Answering a product-sense question with a list of features
Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.
Sizing estimates built on unnamed, unrevisable assumptions
Write each assumption as a named number you can change, then show the arithmetic so the interviewer can challenge one input instead of the whole answer. Finish by saying which assumption the result is most sensitive to, which matters more than the point estimate.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function that implements a specific machine learning algorithm…
Write a function that implements a specific machine learning algorithm.
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?
- Where could label leakage enter this setup?
Given a dataset, how would you implement a sorting algorithm to organi…
Given a dataset, how would you implement a sorting algorithm to organize it efficiently?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
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?
Build a conversion maturation curve from ingestion lag
You are given conversions (conversion_event rows with conversion_ts, received_ts, advertiser_id, conversion_type, is_duplicate) and a snapshot timestamp snapshot_ts at which the warehouse was read. Using only non-duplicate rows, build a maturation curve: for each conversion_ts UTC date, the cumulative share of that date's conversions that had been received within d days, for d from 0 through 14. Return the date-by-lag matrix, one pooled curve, and the implied completeness multiplier for each of the seven most recent conversion dates.
Approach
- Compute lag_days as the floor of (received_ts - conversion_ts) in days, both in UTC. Count and report negative lags separately instead of clipping them: a nonzero negative share means advertiser-side conversion_ts values are not trustworthy, which changes what the curve can be used for.
- Estimate the curve only from conversion dates at least 15 days older than snapshot_ts. A fresher date has not finished receiving its own tail, so including it biases the curve toward fast arrival.
- Build the per-date lag histogram with groupby([date, lag]).size().unstack(fill_value=0), then cumsum along the lag axis and divide each row by that date's mature total, so every row is a CDF ending at 1.0.
- Pool with a count-weighted average across dates, not a mean of the per-date rates, otherwise a low-volume date moves the pooled curve as much as a high-volume one.
- For a recent date of age a days, completeness is curve[a] and the gross-up multiplier is 1/curve[a]. State the assumption this carries: the lag distribution is stationary, so a change in source mix or a batch upload schedule invalidates the multiplier before the curve visibly moves.
Worked solution 20 min
- Filter to is_duplicate == False, derive conv_date and lag_days, and record the negative-lag count before dropping those rows.
- Split into mature dates (conv_date <= snapshot_ts.date() - 15 days) for estimation, and the seven most recent dates for the gross-up.
- Pivot the mature rows to a date-by-lag count matrix, cumsum across lag, divide by each row's total to get per-date CDFs.
- Pool the mature counts column-wise, cumsum and divide by the pooled total for the single curve, then for each recent date compute age, completeness = pooled[age], observed count and observed/completeness.
Follow-up
- offline_upload rows arrive in weekly batches while browser_pixel rows arrive in minutes. Do you keep one pooled curve or one per source, and what does the pooled curve look like if the source mix shifts?
- How would you turn this curve into the freeze rule for a CPA dashboard, and what lag threshold would you pick?
- The multiplier for a 1-day-old date is large and unstable. Would you publish that date at all, and what would you publish instead?
Solve a LeetCode-style problem involving data manipulation.
Solve a LeetCode-style problem involving data manipulation.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Clicks with no conversion inside the attribution window
ad_click has click_id, click_ts, line_item_id, click_tracking_id TEXT (nullable), ivt_status and is_billable. conversion_event has conversion_id, conversion_ts, click_tracking_id (nullable, populated only on click-through reports) and is_duplicate. For one week of valid billable clicks, return per line_item_id the number of clicks with no non-duplicate conversion whose conversion_ts falls within 7 days after click_ts, plus that count as a share. A colleague wrote the filter as click_tracking_id NOT IN (SELECT click_tracking_id FROM conversion_event). Say exactly what that returns, why, and rewrite it correctly.
Approach
- Name the NOT IN failure precisely. If the subquery yields a single NULL, then x NOT IN (...) evaluates to NULL for every x rather than TRUE, the WHERE clause admits nothing, and the query returns zero rows. It raises no error, so the output reads as 'every click converted'. Here conversion_event.click_tracking_id is nullable by design because view-through conversions carry no click, so the subquery is guaranteed to contain nulls.
- Rewrite as NOT EXISTS with the correlated predicate inside: NOT EXISTS (SELECT 1 FROM conversion_event c WHERE c.click_tracking_id = k.click_tracking_id AND c.is_duplicate = FALSE AND c.conversion_ts >= k.click_ts AND c.conversion_ts < k.click_ts + interval '7 days'). NOT EXISTS tests row existence rather than value membership, so nulls on either side simply fail to match and the semantics stay correct.
- Keep the window and the is_duplicate predicate inside the subquery. Moving either to an outer WHERE after a LEFT JOIN converts the anti-join back into an inner join and silently drops exactly the unmatched rows you were counting.
- If you prefer the LEFT JOIN form, every conversion-side predicate must sit in the ON clause for the same reason, and the surviving filter must be c.conversion_id IS NULL.
- Treat clicks with a null click_tracking_id as a third bucket. They can never match, so they are unattributable rather than unconverted. Report their share separately, because a rising null rate is an instrumentation regression, not a performance one, and folding them into the unconverted count hides that.
- Note that one click can carry several conversions. The anti-join form avoids the fan-out by construction; if you instead join and subtract, you need COUNT(DISTINCT click_id) or the converted count will exceed the click count.
Worked solution 25 min
- Run the colleague's NOT IN query, observe zero rows, then run SELECT count(*) FROM conversion_event WHERE click_tracking_id IS NULL to demonstrate the cause rather than assert it.
- Restrict the click side to ivt_status = 'valid' AND is_billable AND click_ts inside the week.
- Classify each click in one pass into three buckets: click_tracking_id IS NULL (unattributable), satisfies the NOT EXISTS predicate (unconverted), otherwise converted.
- Write the NOT EXISTS with is_duplicate = FALSE and both window bounds inside the correlated subquery, using a half-open interval so a conversion exactly 7 days later is excluded consistently.
- Aggregate per line_item_id and take the unconverted share over clicks that carried a tracking id, not over all clicks.
Follow-up
- The unconverted share jumped 6 points on one exchange only. What do you check, in order?
- Extending the window from 7 to 28 days moves this number. Which direction, and how does the size of that move differ between a considered purchase and an impulse one?
- What share of conversions arrive with no click_tracking_id at all, and what does that do to every click-based denominator you report?
If you were tasked with improving click-through rates for a specific c…
If you were tasked with improving click-through rates for a specific campaign, what steps would you take?
Approach
- 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.
- Fix the population and the time window before naming any metric.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you prioritize tasks when working on multiple projects?
How do you prioritize tasks when working on multiple projects?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- 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
- 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?
Given a dataset with user interactions, how would you identify pattern…
Given a dataset with user interactions, how would you identify patterns that could inform marketing strategies?
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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
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?
Describe the architecture you would choose for handling large-scale da…
Describe the architecture you would choose for handling large-scale data ingestion.
Approach
- State your assumptions explicitly before working the problem.
- 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
- How would you know your answer was wrong?
- What assumption would you test first?
Define creative fatigue and set a retirement threshold
Creatives rotate within a line item; creative_id appears on ad_impression and ad_click, and ad_impression.device_id is nullable with a null share that varies systematically by supply source. The team wants an automatic rule that retires a fatigued creative. Deliverable: define the fatigue metric with its exact denominator, state which population it is computed on, choose a retirement threshold, and say what historical data you would use to set that threshold rather than picking a round number.
Approach
- Separate the quantity from its usual proxy. A creative's click-through rate declines over calendar time for at least three reasons that are not fatigue: the supply mix it wins into drifts, a bid or pacing change moves it to cheaper placements, and audience composition changes. Fatigue is specifically the decay of response as the same person sees the same creative repeatedly.
- Measure at that grain. For impressions carrying a non-null
device_id(oruser_id_hashon logged-in supply), assign an exposure sequence number per (creative_id, identity), then compute the valid-click rate — numeratorad_clickwithivt_status = 'valid', denominator billable valid impressions, both bucketed on the impression'sserved_ts— at exposure n against exposure 1. The fatigue index is the ratio of the two. - State the population and its bias in the definition, not in a footnote: the index is computed only on identified supply, and identity coverage differs systematically between app, web and connected-TV inventory, so the index under-observes fatigue wherever coverage is thin. Publish the identified-impression share next to it so a coverage shift is not read as a fatigue shift.
- Set the threshold from outcome data rather than from the index alone. Take eight to twelve weeks of creatives, plot value per $1,000 spend against the fatigue index, and choose the index value at which the creative stops beating the line item's next-best available creative. The threshold is a comparison against an alternative, not an absolute decay number.
- Guardrail the automation itself: rotation churn (creatives retired per line item per week) and budget delivery rate. An aggressive rule can retire the only creative matching a required
ad_formatand push the line item straight into underdelivery, which is a worse outcome than a tired banner.
Worked solution 30 min
- Write the exposure-sequence query: partition by creative_id and identity, order by served_ts, row_number as exposure index.
- Compute valid-click rate by exposure bucket (1, 2-3, 4-6, 7+) with invalid-traffic and billability filters applied to both numerator and denominator.
- Compute the identified-impression share by device_type to document where the index is blind.
- Join historical retirements to value per $1,000 spend and locate the index value where the creative stops beating its next-best alternative.
- Write the rule, the minimum-volume gate, and the two automation guardrails.
Follow-up
- Click rate at exposure five is half of exposure one, but post-click conversion rate per click is flat. Fatigued or not?
- How do you treat a creative whose line item had
frequency_cap_per_daychanged mid-flight? - What is the rule for a creative with fewer than 5,000 identified impressions?
Bid raised fifteen percent, pooled win rate went down
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?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Diagnostic, 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Largest 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.
Practice prompt ↗Practice prompt ↗03Largest 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.
Practice prompt ↗Practice prompt ↗04Second 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗05The 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.
Practice prompt ↗Practice prompt ↗06Retest 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.
Practice prompt ↗Practice prompt ↗07Full 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
What approach do you take to communicate complex data insights to non-…
What approach do you take to communicate complex data insights to non-technical stakeholders?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- 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 did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Describe a time when you faced a significant challenge in a team proje…
Describe a time when you faced a significant challenge in a team project. How did you handle it?
Approach
- 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.
- Close with what you would do differently, concretely.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Disagreeing with a proposal to widen the view window
A product manager proposes changing the default view window on attribution_credit from 1 day to 7 days, to bring reported performance in line with competitors. Their mock shows reported CPA falling 22%. You believe nothing real changes. You have read access to attribution_credit, conversion_event and ad_impression, and two days before the decision review. Deliverable: the analysis you run and the one-page argument you take into the review, including what you concede and the condition under which you would support the change.
Approach
- The interviewer is probing whether you can disagree from inside the other person's numbers rather than from principle, so reproduce their 22% first under the same attribution_model and conversion cohort with view_window_days set to 7, and say out loud that it is correct. The disagreement is now about interpretation, which is a debate you can win.
- Partition the newly credited volume, which is the whole argument. Group by conversion_id under both windows and split newly credited conversions into those that already carried credit on another touchpoint, which is reshuffling between channels, and those that carried no credited touchpoint at all, which is genuinely new claiming. Report the split as a percentage.
- Test the substantive claim on the newly claimed share: look for holdout evidence that view-through exposure at 1 to 7 day latency produces measurable lift. If no such evidence exists, say so plainly and label that share unvalidated rather than incremental.
- Price the cost the proposal does not mention: changing the default changes model_version, which restates history, breaks every published time series, and moves advertiser-facing numbers that some advertisers reconcile against their own order tables.
- Offer a conditional yes with a mechanism: support a window set from the observed conversion-latency distribution and corroborated by holdout lift, shipped as a parallel model_version alongside the existing one rather than as a replacement.
Follow-up
- The product manager says competitors already report this way and we look worse by comparison. Does that change your answer?
- How would you choose a defensible view window from data rather than from convention?
- 01
What approach do you take to communicate complex data insights to non-technical stakeholders?
- 02
Describe a time when you faced a significant challenge in a team project. How did you handle it?
- 03
A product manager proposes changing the default view window on attribution_credit from 1 day to 7 days, to bring reported performance in line with competitors. Their mock shows reported CPA falling 22%. You believe nothing real changes. You have read access to attribution_credit, conversion_event and ad_impression, and two days before the decision review. Deliverable: the analysis you run and the one-page argument you take into the review, including what you concede and the condition under which you would support the change.
Is this an official Taboola interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Taboola. Rounds and questions reflect what candidates have reported, not a process Taboola has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical interview difficulty level for this position?
The interview process is known to be rigorous, with a mix of technical and behavioral assessments. Candidates should expect to prepare extensively for both coding and analytical questions.
PracHub interview research ↗How long does the interview process usually take?
The timeline can vary, but candidates often report a process lasting several weeks, from the initial HR call to final interviews.
PracHub interview research ↗What differentiates successful candidates at Taboola?
Successful candidates demonstrate a strong technical foundation, effective communication skills, and the ability to work collaboratively within teams. They also align well with Taboola's values of innovation and user-centric thinking.
PracHub interview research ↗What is the company culture like?
Taboola fosters a collaborative and innovative environment where diverse ideas are valued. The company emphasizes openness and agility in its approach to problem-solving.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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