At Samba TV, a Data Scientist contributes to the company's mission of transforming the global television viewing experience. Using Samba TV's proprietary data, derived from millions of connected TVs, you will help bridge the gap between linear broadcast and digital streaming. Your work directly influences how media companies reach their audiences and how advertisers measure the incrementality and effectiveness of their campaigns across fragmented platforms.
You will operate at the intersection of large-scale data engineering, advanced statistical modeling, and business strategy. Whether you are building attribution frameworks, optimizing audience targeting, or developing causal inference models, your insights drive high-stakes decision-making for Samba TV's clients. This role is designed for those who thrive on complexity and want their technical work to have a tangible impact on the media landscape.
The Samba TV data environment is unique; successful candidates often demonstrate a deep appreciation for the nuances of TV viewership data, including set-top box and panel-based sources.
Initial Screening
reportedData Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.
What to demonstrate
- Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
- Whether you name what you have not done instead of stretching to cover every line of the posting
- Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage
How to prepare
- Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
- Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
- Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
Technical Assessments
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Final Round Interviews
reportedWhere a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.
What to demonstrate
- Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
- Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
- Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
- Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered
How to prepare
- Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
- Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
- Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one
PracHub editorial advice for the preparation topics above.
Treating a bid as if it does not affect the price paid, under a first-price auction.
In a second-price auction the winner pays the runner-up's bid, so bidding true value is close to optimal and bid and price are usefully separable. Under first-price clearing, which is now the common case in open exchanges, the winner pays exactly what it bid, so raising the bid raises both the win probability and the cost of every win that would have been won anyway. A bid-increase experiment can therefore show more impressions, more conversions and worse profit simultaneously, and reading only the volume metrics will call it a success. The related second-order problem is that your own bid increase raises the clearing prices your control cells face, which contaminates the comparison unless the cells are separated by market.
Randomising users into treatment and control while both arms draw from the same campaign budget.
The arms compete in the same auctions and against the same budget, so treatment winning more impressions directly starves control, and the measured gap includes that cannibalisation rather than only the ad effect. The test looks methodologically clean, the randomisation is genuinely valid, and the lift is still partly manufactured. This is an interference violation, not a randomisation failure, so checking balance on covariates will not catch it. The fixes are to randomise at a unit that contains the budget, such as a geographic market, or to give each arm its own budget and its own pacing, and then be explicit that you are now comparing two separately-budgeted campaigns.
Reading an observational correlation as a causal effect
Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.
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.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the difference between correlation and causation in the contex…
Explain the difference between correlation and causation in the context of ad attribution.
Approach
- Write down the assumption the method needs before you use the method.
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Check predicted conversion-rate calibration with a reliability table
scored has 4 million rows at bid_request_log grain for one campaign: predicted_cvr, win_flag, and converted, which is observed only where win_flag is true and a conversion landed within 7 days. The win rate is about 2% and the conversion rate among won impressions about 0.4%. Without using a library calibration helper, build a 20-bin equal-frequency reliability table with bin count, mean predicted_cvr, observed rate and an interval around it, then compute count-weighted expected calibration error. Say whether the model is miscalibrated, in which direction, and over which population your answer holds.
Approach
- Restrict to win_flag == True and state the precondition plainly: converted is only defined where the ad served, so the table measures calibration on the won slice. That is the population the advertiser pays for, but it is selected by the bid and the auction, so the result cannot be extended to no-bid or lost opportunities where the prediction is never tested.
- Bin with equal frequency (pd.qcut with duplicates='drop'), not equal width. Prediction distributions in this setting are heavily right-skewed with point masses, so equal-width bins put most of the mass in one bin and leave the tail bins with too few conversions to say anything. Report how many bins survived if edges collapsed.
- Per bin compute n, mean predicted_cvr, observed rate, and a Wilson score interval. At a 0.4% base rate the normal approximation produces negative lower bounds and understates uncertainty in the low bins, so the interval choice is not cosmetic here.
- Compute ECE as the count-weighted mean of |observed - predicted| across bins, and report it next to the base rate: 0.002 of absolute error is small against a 5% base rate and enormous against 0.4%.
- Read the direction from the sign pattern rather than the scalar. Observed below predicted across the top bins is head over-prediction, typically from training on a differently-selected population or from scoring against a conversion window that has not matured; a flat observed rate across rising predictions means the score has little ranking power in this range, which is a different problem from miscalibration.
Follow-up
- The pooled curve looks calibrated but ctv supply is badly off. How do you test for segment-level miscalibration without fishing across dozens of cuts?
- The model retrains daily while conversions take up to 7 days to land. What does that do to the head of the reliability curve, and in which direction?
- How would you turn this diagnostic into a bid correction, and what would you verify before shipping it?
Permutation-test a market-level holdout for incremental conversions
geo_daily has one row per (geo_dma, date): arm ('treatment' or 'control'), conversions, conversion_value_usd, spend_usd. Forty DMAs assigned in twenty matched pairs on pre-period conversions, with fifty-six pre-period dates and twenty-eight flight dates. Control DMAs received no delivery for this advertiser and each arm carried its own budget. Without using a library hypothesis test, estimate incremental conversions per $1,000 of treatment spend with a p-value and a 95% interval. State the permutation scheme, why it matches the assignment mechanism, and the minimum effect this design can detect.
Approach
- Fix the estimator before looking at the outcome. Per DMA take y as flight-period conversions and x as conversions over a matched-length pre-period window, form the adjusted outcome y - theta * x, and estimate theta from control DMAs only so it cannot absorb any part of the treatment effect. The statistic is then the mean of the paired differences of the adjusted outcome.
- Permute within matched pairs, not across all forty DMAs. Assignment was pairwise, so the exact null distribution is the 2^20 sign flips of the pair differences; sample 20,000 of them. Permuting freely across DMAs tests a weaker null, reintroduces between-pair variance the matching already removed, and makes a real effect harder to detect.
- Compute the p-value in the plus-one form, (1 + count of |T_perm| >= |T_obs|) / (1 + B). With B replicates a raw count of zero would report p = 0, which is not an attainable statement about a sampled reference distribution.
- Build the interval by inverting the test: find the set of shifts delta for which the same permutation test applied to y minus delta times the treatment indicator is not rejected at 0.05. That interval is consistent with the p-value you just reported, whereas a bootstrap over twenty pairs is thin and can disagree with its own test.
- Convert to the per-$1,000 scale by dividing the incremental-conversion estimate by treatment spend over 1,000. State the precondition that makes this a causal quantity: each arm carried its own budget, so treatment wins did not starve control. Had they shared one budget, the arms would compete in the same auctions and the difference would include cannibalisation rather than the ad effect alone.
- Report power before the p-value. Compute the minimum detectable effect from the observed standard deviation of the pair differences at twenty pairs; if the MDE is larger than any effect the advertiser would act on, say the design cannot answer the question rather than presenting a non-significant result as evidence of no effect.
Worked solution 40 min
- Collapse geo_daily to one row per DMA with pre-period conversions x, flight conversions y, flight spend, and the pair id and arm; confirm every pair has exactly one treatment and one control DMA.
- Estimate theta by regressing y on x within control DMAs only, form adj = y - theta * x, and compute the twenty pair differences d = adj_treatment - adj_control and the observed statistic T_obs = mean(d).
- Draw 20,000 sign-flip vectors in {-1, +1}^20, compute mean(sign * d) for each, and evaluate the plus-one two-sided p-value against |T_obs|.
- Invert the test over a grid of delta to get the 95% interval, then divide the point estimate and both endpoints by treatment spend over 1,000.
- Compute the MDE at 80% power from sd(d) and twenty pairs, and run a pre-period placebo by splitting the fifty-six pre-dates into two 28-day halves and testing the second against the first.
Follow-up
- One treatment DMA holds 30% of all conversions. What does that do to the permutation distribution, and would you weight, trim, or re-pair?
- The flight ends on day 28 but the click window is 7 days. What is your analysis window, and what does cutting the measurement on day 28 cost you?
- How would you detect spillover between adjacent DMAs, and what would you do to the estimate if you found it?
Describe your process for refactoring code to improve readability and …
Describe your process for refactoring code to improve readability and maintainability.
Approach
- 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.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Explain the time and space complexity of your solution for a given dat…
Explain the time and space complexity of your solution for a given data manipulation task.
Approach
- 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.
- 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?
- How does the query change if the join becomes one-to-many?
Write a Python function to filter specific patterns from a large datas…
Write a Python function to filter specific patterns from a large dataset.
Approach
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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?
Time-weighted daily budget from slowly changing line item versions
line_item is SCD type 2: one row per version with line_item_id, daily_budget_usd, pacing_mode, status, valid_from_ts, valid_to_ts (null on the current row) and is_current. ad_impression has line_item_id, served_ts, billed_price_micros_usd and is_billable. For one UTC date, compute budget delivery rate per line item: spend divided by a time-weighted daily budget, where the day's budget is each overlapping version's daily_budget_usd weighted by the fraction of the 24 hours that version was in force. Return spend, weighted budget, the weight coverage, and attainment.
Approach
- Select versions that overlap the day with valid_from_ts < day_end AND COALESCE(valid_to_ts, 'infinity'::timestamptz) > day_start. Use strict inequality on both sides so a version that ends exactly at midnight does not also claim the following day, which would make the weights sum above 1.
- Clip each version to the day before weighting: start at GREATEST(valid_from_ts, day_start) and end at LEAST(COALESCE(valid_to_ts, 'infinity'::timestamptz), day_end). Weighting on the unclipped interval gives a month-long version a weight of 30 rather than 1.
- Derive the weight as EXTRACT(EPOCH FROM (clipped_end - clipped_start)) / 86400.0 and sum daily_budget_usd * weight per line item. Keep SUM(weight) in the output as coverage: it should be 1.0 for a line item live all day and below 1 for one created or ended mid-day, and anything above 1 means the interval predicate is wrong.
- If valid_to_ts is unreliable on superseded rows, derive the interval end with LEAD(valid_from_ts) OVER (PARTITION BY line_item_id ORDER BY valid_from_ts) instead, and say which source you trust. Mixing the two produces overlapping intervals that double-count budget.
- Aggregate spend in its own CTE at impression grain, filtered to is_billable and bucketed on served_ts in UTC, then join the two pre-aggregated results on line_item_id. Joining the SCD table to impressions before aggregating multiplies every impression by the number of versions.
- Compute attainment as spend / NULLIF(weighted_budget, 0), and decide explicitly how to treat versions whose status was paused or ended for part of the day, since including a paused stretch in the denominator reads as underdelivery that never had a chance to happen.
Worked solution 40 min
- Declare day_start and day_end as explicit TIMESTAMPTZ bounds, then select line_item rows where valid_from_ts < day_end AND COALESCE(valid_to_ts, 'infinity'::timestamptz) > day_start.
- Add w := EXTRACT(EPOCH FROM (LEAST(COALESCE(valid_to_ts, 'infinity'::timestamptz), day_end) - GREATEST(valid_from_ts, day_start))) / 86400.0 to each surviving row.
- Build budget AS (SELECT line_item_id, SUM(daily_budget_usd * w) AS weighted_budget, SUM(w) AS coverage FROM clipped GROUP BY 1).
- Build spend AS (SELECT line_item_id, SUM(billed_price_micros_usd) / 1e6 AS spend FROM ad_impression WHERE is_billable AND served_ts >= day_start AND served_ts < day_end GROUP BY 1), entirely independent of the dimension.
- FULL OUTER JOIN budget and spend on line_item_id, compute spend / NULLIF(weighted_budget, 0), and inspect the rows where coverage differs from 1.0 as a separate list.
Follow-up
- A line item shows 180% attainment. Give three explanations that are not overspend.
- How does the same calculation change for lifetime_budget_usd across a multi-week flight rather than a daily budget?
- pacing_mode is 'even' but hourly spend is front-loaded and stops at 14:00. Which query distinguishes budget exhaustion from a pacing throttle, and which column settles it?
What are the trade-offs between different causal inference techniques …
What are the trade-offs between different causal inference techniques like propensity score matching and synthetic controls?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
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 would you design an A/B test to measure the incrementality of a Co…
How would you design an A/B test to measure the incrementality of a Connected TV (CTV) ad campaign?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Describe how you would approach a multi-touch attribution problem acro…
Describe how you would approach a multi-touch attribution problem across linear and OTT channels.
Approach
- Work from the decision backwards to the evidence you would need.
- Clarify what is being asked and what a complete answer would contain.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Identify retargeting lift when the advertiser refuses a holdout
An advertiser will not fund a holdout during peak season, so no group may be denied delivery. You may randomise the bid multiplier on a line item by geo_dma, plus or minus 20%, across 50 markets. Retargeting eligibility is a hard rule: a device enters the audience only if its last site visit was within 30 days. Propose an identification strategy, state exactly what quantity it estimates, and say what would invalidate it.
Approach
- Recognise that exposure cannot be randomised because the auction decides it, but the bid can be. Randomising the multiplier by market is an encouragement design: the multiplier is the instrument, impressions per device is the endogenous treatment, and conversions per device is the outcome.
- Run two-stage least squares at the market level with the multiplier as instrument and report the first-stage F. With 50 markets a weak instrument is a live risk; below F of about 10 the 2SLS estimate is biased toward the confounded OLS and its interval undercovers, so report Anderson-Rubin intervals, which stay valid under weak instruments.
- Interrogate the exclusion restriction instead of asserting it. A 20% higher bid does not only buy more impressions, it buys different impressions, winning inventory that previously cleared above the bid and may convert at a different rate. That is a direct exclusion violation, so the honest statement is that this estimates a local effect on marginal impressions won by a bid increase, not the average effect of an impression.
- Report the intention-to-treat number alongside the 2SLS number. The reduced form, conversions per device regressed on the randomised multiplier, requires no exclusion restriction and is the result you can defend without qualification.
- Offer the discontinuity as an independent cross-check: a sharp regression discontinuity at the 30-day recency cutoff comparing devices at 29 and 31 days since last visit. State plainly that this identifies the effect only at the boundary, on the least recent and least valuable segment, which is not the parameter the advertiser wants, and run a density test on the running variable to check for manipulation.
- Say what would make you decline. If the first-stage F is weak and the recency variable is coarse or manipulable, the defensible deliverable is the reduced form with an honest interval, not a headline incrementality number.
Worked solution 45 min
- Randomise 50 markets, stratified on pre-period spend, to a multiplier of 0.8 or 1.2, holding flight dates and budgets otherwise fixed.
- Estimate the first stage: market-level impressions per device on the multiplier indicator with stratum fixed effects, and report F.
- Estimate the reduced form: market-level conversions per device on the same indicator. This is the number defensible without further assumptions.
- Divide to get the Wald ratio, which equals 2SLS for a single binary instrument, and put an Anderson-Rubin interval around it.
- Test exclusion empirically by comparing supply composition won under each multiplier across exchange_id, placement_id, ad_format and the floor_price distribution, and quantify how much of the reduced form a mix shift could account for.
- Run the recency discontinuity separately on observational data as a directional cross-check, with a density test at the cutoff and a bandwidth sensitivity sweep.
Follow-up
- The first-stage F comes back at 6. What do you report?
- How would you test whether the higher multiplier bought systematically different supply?
- The recency variable is stored in whole days. What does that discreteness do to the discontinuity estimate and its inference?
Post-click conversion rate dropped on mobile app supply only
Post-click conversion rate for one advertiser fell from 3.1% to 0.9% on device_type='mobile_app', with desktop and mobile_web flat. The metric is deduplicated conversion_event rows joined to ad_click on click_tracking_id within click_window_days, over valid billable ad_click rows, cohorted on click_ts. ad_click also has impression_id, device_id, landing_arrival_ts, ivt_status; conversion_event has source, device_id, click_tracking_id, dedup_key. The advertiser shipped a mobile SDK update that week. Decide whether conversions stopped happening or stopped being joinable, and recommend an action.
Approach
- Cohort correctly before anything else: restrict to click_ts dates older than click_window_days plus ingestion lag, so an immature window is not mistaken for a segment-specific drop.
- Split the single ratio into two consecutive steps — clicks to non-null landing_arrival_ts, and arrivals to joined conversions. A drop at the first step means users are not arriving, which is a real problem on the ad or landing side; a drop only at the second means they arrive and the join fails, which is instrumentation.
- If the break is at the join step, measure click_tracking_id null rate in conversion_event split by source and advertiser, by day. A step change on the mobile_sdk source against a flat browser_pixel source localises it to the advertiser's SDK release, and the break will be a single timestamp rather than a ramp.
- Rule out the denominator explanation explicitly, because it moves the metric the other way: rising sivt reclassification on mobile_app clicks removes rows from the denominator and would raise the rate, so an unchanged ivt_status distribution is a necessary check, not a formality.
- Establish that the conversions still exist by counting total conversion_event rows for the advertiser across the break, then recover the joinable population on device_id within the window and report both the recovered rate and the residual recovery gap from null device_id.
Follow-up
- What fraction of mobile_app clicks carry a null device_id, and what does that cap your recovery at?
- Would you restate the advertiser's reported CPA for the affected week, or footnote it, and what changes that decision?
- How would you monitor the joinable share as a first-class metric so this is caught the day it starts?
Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Breadth pass: query fluency
- Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
- For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
- Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.
Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Breadth pass: statistics and inference
- Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
- Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
- Rewrite the two weakest answers the following morning from memory in full sentences.
Deliverable: Ten graded answers with an honest count of exact hits.
Practice prompt ↗Practice prompt ↗03Breadth pass: modelling
- Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
- Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
- Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.
Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.
Practice prompt ↗Practice prompt ↗04Breadth pass: product judgement
- Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
- For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
- Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.
Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Depth, first area
- Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
- Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
- Re-solve the two you failed the same evening with notes closed.
Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.
Practice prompt ↗Practice prompt ↗06Depth, second area, and the seam between them
- Repeat the depth protocol on the second-ranked area with the same six-problem structure.
- Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
- Solve your own combined problem end to end and note where the handoff between the two areas cost you time.
Deliverable: One combined problem, solved end to end, with the handoff failure written down.
Practice prompt ↗Practice prompt ↗07Integration and re-measurement
- Re-run the six prompts from day one under the same clock and compare both correctness and time.
- Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
- Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.
Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.
What draws you to the media measurement space specifically?
What draws you to the media measurement space specifically?
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
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
How do you handle disagreement within your team regarding a modeling a…
How do you handle disagreement within your team regarding a modeling approach?
Approach
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Explaining an incrementality interval to a non-technical budget owner
A 20-market geo holdout has closed. Incremental return on ad spend is estimated at 1.4 with a 90% interval of 0.6 to 2.2. The same account's last-click attributed ROAS is 4.1. A vice president with no statistics background has ten minutes and must decide whether to renew a $3M annual budget. Deliverable: the explanation you give verbally plus one slide. You may not use the words confidence interval, p-value, or significance, and you must give a recommendation rather than only a range.
Approach
- The interviewer is probing whether you can carry uncertainty into a decision instead of either hiding it or hiding behind it, so convert the interval into money at the real budget first: $3M at 0.6 to 2.2 is $1.8M to $6.6M of incremental revenue, with $4.2M as the central estimate.
- Get the break-even ROAS from the advertiser's gross margin and mark where it falls inside the range. That single number turns a statistical interval into two decision regions, and it is the only quantity the executive needs to reason about.
- Explain the 4.1 without calling it wrong: it counts every conversion that followed an ad, including the ones that would have happened anyway, while the test counts only the ones that would not have. Both are computed correctly and they answer different questions.
- State what would narrow the range and what it costs, in the same units: roughly four times the markets or four times the flight length to halve the width, against the cost of running longer at current spend.
- Pre-commit to the action at each end of the range so the recommendation is falsifiable: what you do if the true value is near 0.6, and what you do if it is near 2.2.
Follow-up
- The vice president asks whether it is working, yes or no. What is your one-sentence answer?
- Break-even is 1.0 and the range is 0.6 to 2.2. Do you renew at full budget, and what would you change about the flight?
- 01
What draws you to the media measurement space specifically?
- 02
How do you handle disagreement within your team regarding a modeling approach?
- 03
A 20-market geo holdout has closed. Incremental return on ad spend is estimated at 1.4 with a 90% interval of 0.6 to 2.2. The same account's last-click attributed ROAS is 4.1. A vice president with no statistics background has ten minutes and must decide whether to renew a $3M annual budget. Deliverable: the explanation you give verbally plus one slide. You may not use the words confidence interval, p-value, or significance, and you must give a recommendation rather than only a range.
Is this an official Samba TV interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Samba TV. Rounds and questions reflect what candidates have reported, not a process Samba TV has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long should I prepare for the technical assessment?
Treat the take-home challenge as a serious project. While it may be time-boxed (e.g., 4-6 hours), the quality of your documentation and the clarity of your presentation are just as important as the code itself.
PracHub interview research ↗Is the interview process always the same?
The process follows a structured approach, but it can be adjusted based on the specific team's needs. Expect variations in the number of technical rounds depending on whether you are applying for a specialized role like Knowledge Graph & Identity or Measurement.
PracHub interview research ↗What differentiates a successful candidate?
Successful candidates show a mix of high-level strategic thinking and low-level technical precision. Being able to explain the business impact of your model is what often separates the top performers.
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
Explore related engineering environments and compare the preparation focus.
tvScientific Data Scientist Interview Questions & Guide 2026
Timed practice for tvScientific data scientist candidates who freeze under a clock: short SQL and probability sets run end to end, then reviewed against a written rubric.
Truliant Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for Truliant data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
StackAdapt Data Scientist Interview Questions & Guide 2026
Experiment-design practice for StackAdapt data scientist candidates, covering randomisation units, power, minimum detectable effect and the traps that invalidate a result.
Taboola Data Scientist Interview Questions & Guide 2026
Take-home style practice for data scientists preparing for Taboola: a messy dataset, a fixed time budget, and a written analysis, reviewed for whether it states its assumptions and whether a reader could reproduce your numbers.