As a Data Scientist at Meta Power Solutions, you sit at the intersection of product strategy, engineering, and statistical rigor. You are responsible for transforming raw data into actionable insights that directly influence how Meta Power Solutions products function and scale. Whether you are working on ranking algorithms for AI systems or analyzing user behavior within the company's product ecosystem, your work informs high-stakes decisions that affect millions of users.
This role is critical to the mission of Meta Power Solutions because the company relies on data to eliminate ambiguity. You will not just be reporting numbers; you will be designing the metrics that define the company's success, diagnosing sudden drops in performance, and conducting experiments that push its technology forward. You will collaborate closely with cross-functional partners to ensure that every product iteration is backed by sound evidence and a deep understanding of user intent.
Initial Technical Screening
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
Deep-Dive Sessions
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Cross-Functional Meetings
reportedAn extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.
What to demonstrate
- Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
- Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
- How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one
How to prepare
- Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
- Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
- Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.
PracHub editorial advice for the preparation topics above.
Modelling win rate on proposals with a recorded outcome, using fields written after the decision
Two failures compound here. First, stage IN ('withdrawn','no_decision') is not missing at random: those are disproportionately deals that were going to be lost, so training on won-plus-lost only inflates apparent win rate and distorts the coefficients. Second, fields like engagement_id, final scope and revised pricing are populated after the outcome is known, so including them leaks the label and produces a model with excellent backtest accuracy and no forward value. Restrict features to values knowable at submitted_at.
Pooling margin, realisation or overrun across pricing models
Fixed-fee margin falls with hours worked; uncapped time-and-materials margin rises with hours worked; retainer margin depends on neither. A quarter in which the firm sells more fixed-fee work will show a margin change caused entirely by mix, not by delivery performance, and the aggregate can move in the opposite direction to every individual pricing model. Always stratify by fct_engagement.pricing_model before comparing periods, and report the mix shift alongside the within-stratum change.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Extrapolating a first-week lift inflated by novelty effects
Plot the treatment effect by days since first exposure instead of quoting one pooled average. A lift that decays toward zero across the test window is behaviour that will not persist, and annualising it produces a forecast that misses by an order of magnitude.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the difference between p-values and confidence intervals in th…
Explain the difference between p-values and confidence intervals in the context of a live product experiment.
Approach
- Write down the assumption the method needs before you use the method.
- Translate the result into the decision it informs, in one plain sentence.
- Say what the estimate is of, and over what population it generalises.
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?
Cluster bootstrap a margin change across few, unequal accounts
engagements has engagement_id, client_id, pricing_model, fees_usd, cost_usd and period in {pre, post} around a rate-card change. There are about 35 client_ids across roughly 400 engagements, and the top five clients carry a large share of fees. Estimate the pre-to-post change in value-weighted gross margin within pricing_model, and give a 95% interval by resampling whole client_ids with replacement, B = 2000. Write the bootstrap yourself; no resampling library. Report the account-weighted estimate alongside and explain any divergence.
Approach
- Write the statistic as a pure function of a dataframe first. Within each pricing_model, margin = (sum(fees) - sum(cost)) / sum(fees) per period; the headline is the fee-weighted average of the within-model differences using fixed pre-period weights, so a shift in the mix of work sold cannot masquerade as a change in delivery.
- Resample clusters, not rows: draw 35 client_ids with replacement and concatenate all engagements of each drawn client. A client drawn twice contributes its rows twice under distinct pseudo-ids, which is what preserves within-account correlation instead of averaging it away.
- Recompute the statistic on each replicate and take a percentile interval from the 2.5th and 97.5th quantiles. With concentrated fees the replicate distribution is skewed, so a symmetric point plus or minus 1.96 times a standard error is wrong in the tail that matters.
- Compute the account-weighted version (mean across clients of each client's margin change) next to the value-weighted one, and attribute the divergence to concentration rather than to noise; if they disagree in sign, that fact is the finding.
- Name the regime honestly: at roughly 35 clusters both cluster-robust standard errors and the pairs cluster bootstrap under-cover, so state the fix you would run next, which is a wild cluster bootstrap with Rademacher weights or CR2 with t(G-1) critical values.
Worked solution 40 min
- stat(df): pivot fees and cost by (pricing_model, period), compute per-model margins, difference post minus pre, and weight the differences by each model's pre-period fee share; return one scalar.
- Pre-split the frame into a dict of client_id to its rows, so each replicate is a concat of 35 preselected blocks rather than a boolean filter over 400 rows.
- rng = np.random.default_rng(11); for b in range(2000): draw client ids with replacement, concat their blocks, call stat, store; assert the replicate's nunique cluster draw count is 35 every time.
- ci = np.quantile(reps, [0.025, 0.975]); compute the account-weighted estimate as the unweighted mean of per-client margin changes over clients present in both periods.
- Re-run a row-level bootstrap on the same data for contrast and record both interval widths, then leave-one-client-out to see whether one account drives the point estimate.
Follow-up
- Implement the wild cluster bootstrap and show at what cluster count its p-value separates from the naive one.
- One client is 30% of fees. Show the interval with and without that account and say which one you would present, and to whom.
- What pre-period check would make you willing to call this a causal effect of the rate card rather than a correlation?
Collapse time entries into contiguous staffing spells
From approved delivery time entries (consultant_id, engagement_id, work_date, hours, charge_code, status, with engagement_id not null), build staffing spells. Per consultant and engagement, collapse weeks containing any logged hours into contiguous runs, where three or more consecutive zero-hour weeks end a spell. Output one row per spell with consultant_id, engagement_id, start_week, end_week, active_weeks, gap_weeks and total_hours. Consultants sit on several engagements at once, so spells from different engagements may overlap in time and must not be merged. No Python loop over rows.
Approach
- Aggregate to (consultant_id, engagement_id, week_start) with summed hours and keep only weeks with positive hours. The absent weeks are the signal, so materialising zeros here would destroy the thing you are detecting.
- Convert week_start into an integer week index, ((week_start - epoch_monday).dt.days // 7), so gap detection is integer subtraction rather than calendar arithmetic that breaks over month and year boundaries.
- Sort by [consultant_id, engagement_id, week_index], diff the week index inside each pair, and mark a spell start where the diff is null (first row of the pair) or greater than 3. A diff of 1 is adjacent weeks and a diff of 3 is two empty weeks, which the tolerance permits.
- Take spell_id = the cumulative sum of that boolean over the whole frame so ids are globally unique, then a single groupby on [consultant_id, engagement_id, spell_id] yields min and max week, active week count and summed hours; gap_weeks = (end - start + 1) - active_weeks.
- Do not deduplicate overlapping spells across engagements. A consultant on two engagements in the same week is the normal case, and that overlap is the fact any capacity or context-switching question needs.
Follow-up
- Re-run with a one-week and a four-week tolerance. What happens to the spell count, and which tolerance would you defend to a staffing lead?
- Using these spells, how would you measure how many engagements a consultant is split across in a given week, and why is that not just a count of rows?
Explain how you would handle missing data or null values when calculat…
Explain how you would handle missing data or null values when calculating click-through rates.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from it.
- 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?
- How would you verify this result without re-running the same query?
Write a query using SQL window functions to calculate the running tota…
Write a query using SQL window functions to calculate the running total of user sessions over the last 30 days.
Approach
- State the window function and its partition and ordering out loud before writing it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
How would you join these two tables to identify users who churned afte…
How would you join these two tables to identify users who churned after their first interaction?
Approach
- Say which table is the grain you start from, and join outward from 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?
Find signed engagements with no approved billable hour
fct_engagement holds engagement_id, start_date and status. fct_time_entry holds time_entry_id, consultant_id, engagement_id, charge_code, work_date, hours, is_billable and status, and engagement_id is NULL on bench, leave, training and internal rows. Return engagements with start_date in the trailing quarter and status IN ('active','closed_delivered','closed_early') that have no approved billable time entry with work_date within 45 days of start_date, giving engagement_id, start_date and days elapsed. Then explain why NOT IN over a subquery selecting fct_time_entry.engagement_id is the wrong instrument here.
Approach
- Express the exclusion as NOT EXISTS with a correlated predicate covering all four conditions at once: te.engagement_id = e.engagement_id AND te.is_billable AND te.status = 'approved' AND te.work_date < e.start_date + INTERVAL '45 days'. NOT IN can only carry a single column, so a multi-condition anti-join forces a contorted subquery even before the NULL problem.
- State the NULL semantics precisely: NOT IN (SELECT engagement_id FROM fct_time_entry) evaluates to NULL rather than TRUE for every candidate as soon as one NULL is present in the list, because x <> NULL is unknown. The WHERE clause keeps only TRUE, so the query returns zero rows and reads as a clean bill of health. This table guarantees NULLs, since bench and leave rows carry no engagement.
- Note the equivalent LEFT JOIN ... WHERE te.engagement_id IS NULL form. It is correct, but it materialises a join only to discard most of it; NOT EXISTS states the intent and most planners execute both as the same anti-join.
- Guard the window against truncation: an engagement that started 10 days ago cannot yet have failed a 45-day test, so require start_date <= current_date - 45 or report the too-early set separately rather than counting them as failures.
- Decide on the late-timesheet risk explicitly. A missing entry may mean unstaffed work or an unsubmitted timesheet, so either hold the cutoff back past the measured submission lag or label the recent tail as provisional.
Worked solution 20 min
- Count engagements matching the status and start_date predicates; this is the denominator.
- Write the NOT EXISTS version and record its row count.
- Write the NOT IN version on the same data and observe zero rows, then confirm with SELECT COUNT(*) FROM fct_time_entry WHERE engagement_id IS NULL that NULLs are present.
- Cross-check by counting engagements that DO have a qualifying entry and verifying the two counts sum to the denominator.
- Open one engagement from the result and look at its raw time entries to see whether the cause is no staffing or unapproved timesheets.
Follow-up
- Turn this into a time-to-first-billable-hour distribution. What do you do with engagements that never recorded one?
- Should client_pursuit hours count as the engagement being staffed, and how does that choice change the list?
- How would you prove the empty result from the NOT IN version is a bug rather than good news?
If a product manager asks you to prioritize a feature, how do you use …
If a product manager asks you to prioritize a feature, how do you use data to provide a recommendation?
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.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you design the success metrics for a new feature launch in o…
How would you design the success metrics for a new feature launch in our AI ranking system?
Approach
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How do you decide between a long-term retention metric and a short-ter…
How do you decide between a long-term retention metric and a short-term engagement metric for a new tool?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
What are the most common experimentation pitfalls you encounter when r…
What are the most common experimentation pitfalls you encounter when running A/B tests?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Name the guardrails that would stop a launch even on a positive primary result.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
How do you determine the required sample size to reach statistical sig…
How do you determine the required sample size to reach statistical significance for a new feature?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
- 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?
We noticed a 5% drop in daily active users on our core platform. How w…
We noticed a 5% drop in daily active users on our core platform. How would you investigate the root cause?
Approach
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer would contain.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Choose a randomisation unit when consultants are a shared pool
A staffing optimiser proposes consultant allocations to engagements. The proposal on the table is to randomise at the engagement: half of new engagements get optimiser-suggested staffing, half go through the current manual process. The claimed benefit is lower time to first billable hour, measured from fct_engagement.start_date to the earliest fct_time_entry work_date with is_billable = TRUE. Every engagement in a practice area draws from the same capacity-constrained consultant pool. Explain what the engagement-level estimate would actually measure, propose a design that measures what leadership wants, and state the price your design pays.
Approach
- Name the violation. SUTVA requires one engagement's outcome to be unaffected by another's assignment, and a fixed consultant pool makes that false by construction: a consultant the optimiser frees is staffed onto a control engagement, so treatment improves and control degrades from the same act.
- Say what the biased estimate is. The engagement-level contrast measures displacement, treatment against a control that treatment has actively worsened, so it overstates the firm-level effect. In the limiting case where the optimiser only reorders a fixed queue of consultant-weeks, the firm-level effect is zero while the engagement-level contrast is large and significant.
- Move the randomisation unit up to the level that contains the contention, the staffing pool, which here is practice_area crossed with home_region. Units then share no consultants and the interference is internalised. The price is that five practice areas by four regions yields at most 20 clusters, which is small-cluster inference territory.
- Or run a switchback inside each pool: randomise each weekly staffing cycle to optimiser or manual so every pool is its own control, turning 20 clusters into 20 pools times many cycles. The price is carryover, since allocations made in a treated week constrain availability in the following control week, so you discard a burn-in window at each switch and you cannot measure any effect whose horizon exceeds the switch interval.
- Analyse at the level of randomisation: pool-week means, pool and calendar-week fixed effects, standard errors clustered at pool, and a wild cluster bootstrap rather than the plain cluster-robust sandwich at 20 clusters.
Worked solution 30 min
- Write potential outcomes against the full assignment vector, Y_i(z) rather than Y_i(z_i), and show the dependence on z_j for any j sharing the pool.
- Work the fixed-pool case: if the optimiser reorders a conserved queue of consultant-weeks, the sum of days-to-staff across engagements is unchanged, so the pool-level effect is zero while treated engagements are faster by exactly the amount control engagements are slower.
- Enumerate candidate units, engagement, account, partner, pool, pool-week, and for each state whether it contains the contention and how many independent units it yields.
- Select a pool-week switchback with the cycle length set to the staffing-meeting cadence, a one-week burn-in discarded at every switch, and a target of at least 26 usable pool-weeks per pool.
- Pre-specify the analysis: pool-week mean days to first billable hour on a treatment indicator with pool and calendar-week fixed effects, clustered at pool, wild bootstrap with Webb six-point weights.
Follow-up
- How long a burn-in would you discard after each switch, and how would you estimate that length from data rather than assert it?
- Time to first billable hour is right-censored for engagements not yet staffed at the analysis date. How does that interact with a switchback?
- If the optimiser's benefit is pure reallocation within a fixed pool, what outcome would show a genuine firm-level gain?
One region's delivery cost fell eighteen percent for two weeks
Cost per delivered engagement-week fell about 18% in one region for two consecutive weeks while fees were flat, so reported margin jumped. Source is fct_time_entry (consultant_id, engagement_id, work_date, entered_at, hours, cost_rate_usd, charge_code, status), loaded nightly from a regional timesheet system, joined to dim_consultant for home_region. Determine whether delivery genuinely got cheaper or rows are missing, name the evidence that distinguishes the two, and specify the automated check you would add.
Approach
- Check row counts and coverage before any ratio: entries per workday and distinct consultant_id with at least one entry per workday, for the affected region against its own trailing four weeks and against other regions in the same weeks.
- Use the shape of the loss to separate causes. A load failure removes whole days or whole consultants, so distinct-consultant coverage collapses while hours per reporting consultant stays normal. A real change lowers hours per consultant with coverage intact.
- Split the cost identity: cost = hours x cost_rate_usd. Compute mean cost_rate_usd by level. If rates went NULL, zero or defaulted to a single value, the fault is in the dimension join, not the source extract.
- Look for the loader's fingerprint: max(entered_at) per region per day, gaps aligned to a calendar boundary, and whether the missing dates fall on a particular day of week, which points at a scheduler that skipped a run rather than at delivery behaviour.
- Confirm by waiting one cycle: a failed load backfills on re-run and the anomaly disappears from the restated weeks; a genuine change persists. Do not publish until that test has run.
- Automate the check that would have caught it: per-region, per-workday assertions on row count and on distinct billable consultants covered, with thresholds derived from trailing medians rather than fixed constants, plus a freshness assertion on max(entered_at).
Follow-up
- The same load also feeds utilisation. Why did utilisation not show an equally obvious break?
- How do you choose the alert threshold so it survives holidays and genuine low-activity weeks?
- If the rows never arrive, what do you do with the two weeks already reported to leadership?
Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and set a baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
- Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
- Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.
Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02One query pattern, written three times
- Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
- On the third attempt, write the grain of every CTE as a comment before writing its body.
- Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.
Deliverable: Three independent versions of the same query plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Only the statistics you will be asked to defend
- Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
- Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
- Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.
Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04One case, and the assumptions holding it up
- Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
- Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
- Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.
Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
- Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in a longer weekend block
- Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
- Re-read only your own notes from this week, and open no new material.
- Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.
Share an example of a time you identified a critical issue that others…
Share an example of a time you identified a critical issue that others had missed.
Approach
- Quantify the outcome, including what you would not claim credit for.
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
Defending a fixed-fee overrun finding against the selling partner
Over the trailing four quarters, fixed_fee engagements in one practice area show a median scope overrun ratio of 1.34, computed as approved client_delivery hours in fct_time_entry divided by fct_engagement.contracted_hours, and inception-to-date gross margin nine points below the time_and_materials book. The partner who sold most of that work says the denominator is wrong because change orders were signed, and each change order is a separate row linked by prior_engagement_id. You have ten minutes with the practice lead. Present and defend the finding.
Approach
- Say out loud what is being probed: whether a specific methodological objection makes you recompute or makes you repeat yourself. Restate the metric as numerator, denominator and window before defending anything.
- Test the objection empirically instead of debating it. Rebuild the denominator by walking the prior_engagement_id chain with a recursive CTE, summing contracted_hours across the original SOW and every extension, and summing approved delivery hours over the same chain.
- Report both numbers and say what each one answers: the original-SOW denominator answers whether the team held the scope that was signed, the chained denominator answers whether the firm estimated the total work correctly. Neither is a trick; they are different questions.
- Keep the margin claim separate from the overrun claim, and keep it stratified. Fixed_fee margin falls with hours worked while uncapped time_and_materials margin does not, so the nine-point gap is only meaningful within pricing_model, and a shift in the fixed_fee share of fees is reported alongside it.
- Close with the decision and a falsifier: if the chained overrun is near 1.0, the problem is scope control on the original SOW, and the check is whether change orders repriced at the standard rate card or at the original blended rate.
Follow-up
- The chained denominator narrows the overrun to 1.11. Does your recommendation change, and which number goes in the practice review?
- How would you distinguish an estimation problem at sale time from a delivery problem during execution, using only these tables?
Answering a request to redefine the utilisation denominator
Before a quarterly review, a practice lead asks for two changes to reported billable utilisation: drop consultants allocated to engagements with status = 'paused' out of the denominator, and count approved travel hours booked to client_delivery as billable. Together the changes move reported utilisation from 71 to 79 percent. You must answer in writing today. Give your answer, the principle you apply to each change separately, and what you do if you are overruled.
Approach
- Name the probe: whether you can hold a measurement line by adjudicating each proposal on its merits, rather than treating the request as bad faith or accepting it to avoid a fight.
- Adjudicate the paused-engagement change against what the denominator means. Billable utilisation measures the share of available consultant hours that were sold. A consultant whose engagement is paused is still available and still costs salary, which is precisely the situation the metric exists to reveal. Removing them defines away the signal, so this one is refused on definitional grounds, not on suspicion.
- Adjudicate the travel change as an empirical question, because it is one. If those hours are billed to the client, is_billable and bill_rate_usd should already reflect it; if they are charged and then written off or never invoiced, counting them raises utilisation while realisation falls. Check the invoice lines for the same engagement and period rather than arguing about principle.
- Quantify each change separately, and the interaction. An eight-point move made of one point and seven points is a different conversation from two four-point moves, and the lead cannot make a good decision without that split.
- If a change is accepted, restate history on the new definition and publish the old series alongside it with the date of the change. A definition applied forward only makes every period-over-period comparison meaningless. If overruled, comply with the restatement and the footnote, and escalate only the part that is a misstatement of fact rather than a defensible choice.
Follow-up
- Which of the two changes would you accept, and what evidence flips your answer on the other one?
- The lead wants the new definition applied only from this quarter forward. Why is that worse than applying it to history, and how do you explain that in one sentence?
- 01
Share an example of a time you identified a critical issue that others had missed.
- 02
Over the trailing four quarters, fixed_fee engagements in one practice area show a median scope overrun ratio of 1.34, computed as approved client_delivery hours in fct_time_entry divided by fct_engagement.contracted_hours, and inception-to-date gross margin nine points below the time_and_materials book. The partner who sold most of that work says the denominator is wrong because change orders were signed, and each change order is a separate row linked by prior_engagement_id. You have ten minutes with the practice lead. Present and defend the finding.
- 03
Before a quarterly review, a practice lead asks for two changes to reported billable utilisation: drop consultants allocated to engagements with status = 'paused' out of the denominator, and count approved travel hours booked to client_delivery as billable. Together the changes move reported utilisation from 71 to 79 percent. You must answer in writing today. Give your answer, the principle you apply to each change separately, and what you do if you are overruled.
Is this an official Meta Power Solutions interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Meta Power Solutions. Rounds and questions reflect what candidates have reported, not a process Meta Power Solutions has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How much time should I spend preparing for the coding portion?
Dedicate roughly 25-30% of your prep time to coding and SQL. Technical accuracy matters, but interviewers are more interested in your ability to write readable, efficient queries than in memorized obscure syntax.
PracHub interview research ↗What is the most common reason candidates fail the product-sense round?
Candidates often jump to solutions without clarifying the goal of the product or the specific user segment. Always start by defining the objective and the key metrics before suggesting a specific experiment or feature.
PracHub interview research ↗Is the process the same for all Data Scientist roles?
While the core technical rounds are standard, teams focused on AI/Ranking may place more weight on statistical modeling and machine learning fundamentals compared to general Product Analytics roles.
PracHub interview research ↗How do I handle ambiguity in the case studies?
Ambiguity is intentional. Use it as an opportunity to ask clarifying questions. An interviewer will be impressed if you can narrow the scope of a problem by asking the right questions about the product goal.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
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