As a Data Scientist at Faire, you will play a pivotal role in leveraging data to drive business decisions and enhance user experiences. Your work will directly influence product development, marketing strategies, and operational efficiencies. Data scientists at Faire are expected to analyze complex datasets, build predictive models, and derive actionable insights that align with the company's mission of supporting small businesses and independent brands.
In this role, you will collaborate closely with cross-functional teams, including product managers, engineers, and marketing specialists, to identify data-driven opportunities that can enhance the platform's functionality and user engagement. The complexity and scale of the data you will be working with present both exciting challenges and significant opportunities to impact Faire's growth and evolution in the marketplace.
Candidates can expect to work on innovative projects that utilize cutting-edge machine learning techniques, statistical analysis, and data visualization, all aimed at solving real-world problems for the small business community. Your contributions will not only support the company's objectives but also have a lasting impact on the ecosystem of independent retailers.
Phone Screen
reportedMost candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.
What to demonstrate
- Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
- Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
- The substance of the questions you ask back, which an experienced screener reads as a level signal
How to prepare
- Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
- Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
- Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered
Technical Assessment
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
Team Interviews
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.
Behavioral Interview
reportedMost of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.
What to demonstrate
- Whether you can state the other side's argument accurately before you explain why you disagreed
- What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
- Whether you distinguish being overruled from being wrong, and can give an example of each
How to prepare
- Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
- For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
- Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.
PracHub editorial advice for the preparation topics above.
Collapsing cancellation and payment failure into one churn number.
Involuntary churn from expired or declined payment instruments is a large and volatile share of gross churn, and it responds to retry schedules, card-updater coverage and billing provider, not to anything in the product. It also resolves late, so a period that looks involuntary today can be a successful retry next week, and reading the split before the dunning horizon closes overstates it. Compounding this, cancel-at-period-end means the cancellation request and the entitlement end are different timestamps on different rows, so a churn curve keyed on cancel_requested_ts and one keyed on churn_ts disagree by a full billing period.
Testing hours, revenue or completion with a difference in means on a heavy-tailed distribution.
Listening and viewing hours per account are strongly right-skewed and content popularity is close to power-law, so the variance of a sample mean is dominated by a few accounts and the central limit approximation converges slowly at realistic sample sizes. A t-test on mean hours can flip sign when one heavy account's week changes, and an experiment can appear significant because a single title released into one arm's window. Capping at a pre-registered percentile, or decomposing into a rate (did they stream at all) and a conditional intensity, controls the variance, at the stated cost that capping biases toward zero exactly when the true effect lives in the tail.
Reading experiment results before checking the arm split
Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.
Accepting a metric definition without asking about the denominator
Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given a dataset, how would you approach building a predictive model?
Given a dataset, how would you approach building a predictive model?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Set a baseline first, so any model has something honest to beat.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- What would you monitor after launch to know the model is still valid?
- How would you choose the decision threshold, and who owns that choice?
Provide a Jupyter Notebook example of a machine learning model you hav…
Provide a Jupyter Notebook example of a machine learning model you have built.
Approach
- Set a baseline first, so any model has something honest to beat.
- 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.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- What would you monitor after launch to know the model is still valid?
Attribute streams to prior impressions without merge_asof
You have impressions (impression_id, profile_id, content_version_id, rendered_at, surface, slate_position, viewport_visible_ms) and streams (stream_id, profile_id, content_version_id, started_at). Attribute each stream to the most recent impression of the same profile and content version with rendered_at at or before started_at, a gap of at most 30 minutes, and viewport_visible_ms above zero; break ties on rendered_at by the smaller slate_position. pd.merge_asof and any groupby-apply over individual rows are off limits. Return streams with impression_id added, plus the attributed share by surface.
Approach
- Filter impressions to viewport_visible_ms above zero first. A row that never scrolled into view is not an exposure, and leaving it in lets an unseen impression win the as-of match over a seen one.
- Stack both frames into one long frame with a ts column and an is_stream flag, then sort by (profile_id, content_version_id, ts, is_stream, negative slate_position) so that at an identical timestamp impressions sort before the stream, and among tied impressions the smallest slate_position sorts last.
- Forward-fill the candidate impression_id and rendered_at with groupby(['profile_id','content_version_id']).ffill(). The grouping is what stops the fill leaking across profiles; a global ffill on a sorted frame is the usual wrong answer.
- Apply the 30-minute window as a post-filter on the filled candidate, and let a stream that fails it go unattributed rather than falling back to an older row. Every candidate precedes the stream, so the gap grows monotonically as you walk backwards: if the nearest in-view impression is more than 1800 seconds old, no earlier one is inside the window either, and null is the correct answer. The window cannot be pushed onto the impressions frame beforehand in any case, because "within 30 minutes" is defined against a particular stream's started_at and one impression is a candidate for many streams. Dropping impressions rendered more than 30 minutes before the earliest stream is a safe prune, but it is an optimisation, not the rule.
- Slice the stream rows back out and compute the attributed share by surface, stating that unattributed streams (resume, direct link, radio seed) are not a surface and belong in their own bucket.
Worked solution 30 min
- imp = impressions[impressions.viewport_visible_ms > 0]; build an impression side with ts = rendered_at, is_stream = 0 and a sort helper neg_pos = -slate_position, and a stream side with ts = started_at, is_stream = 1, neg_pos = 0.
- Concatenate both, then sort_values(['profile_id','content_version_id','ts','is_stream','neg_pos']).
- Add cand_id and cand_ts columns carrying impression_id and rendered_at on impression rows and NaN on stream rows, then groupby(['profile_id','content_version_id'])[['cand_id','cand_ts']].ffill().
- Slice to is_stream == 1, compute gap = (ts - cand_ts) in seconds, and set cand_id to NA where the gap is null or above 1800.
- Merge cand_id back onto streams by stream_id, then group the attributed rows by the winning impression's surface for the share.
Follow-up
- The same item was rendered in two slates inside the window. Your tie-break picks one. What does that do to per-surface credit, and what would a fractional rule change?
- How would you validate this against the impression_id already carried on fct_stream, and what would a systematic disagreement tell you?
- At 2 million impressions and 500 thousand streams this fits in memory. What changes at 200 million?
Demonstrate how to merge two datasets in SQL.
Demonstrate how to merge two datasets in SQL.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Solve a problem using Python that requires data manipulation and analy…
Solve a problem using Python that requires data manipulation and analysis.
Approach
- 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.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Sessionise playback into listening sessions with an idle gap
fct_stream has profile_id, stream_id, content_version_id, started_at, ended_at (nullable when the client stopped reporting) and played_seconds. Collapse consecutive streams by the same profile into listening sessions: a new session begins when a stream starts more than 30 minutes after the latest end reached by any of that profile's earlier streams. One profile can play on two devices at once, so streams overlap and 'the previous stream' is not well defined — the idle gap is measured against the running maximum end, not against the row immediately before. Treat a NULL ended_at as started_at plus played_seconds. Return one row per session with profile_id, session_start, session_end, stream_count, distinct content_version_id count and total played_seconds. Use window functions; a self-join on fct_stream is not an acceptable answer.
Approach
- In a first CTE compute effective_end = COALESCE(ended_at, started_at + played_seconds * INTERVAL '1 second'). This both fills the abandoned-client case and bounds it, since played_seconds is advancing playback only and cannot run past the true end.
- Take prev_max_end = MAX(effective_end) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING), and flag is_new_session = (prev_max_end IS NULL OR started_at > prev_max_end + INTERVAL '30 minutes'). LAG(effective_end) is the reflex here and it is wrong under concurrent devices: with A 10:00-12:00, B 10:05-10:10 and C starting 10:45, LAG hands C the end of B at 10:10, so C opens a new session 35 minutes into a session still running until 12:00. The NULL branch is not defensive noise either: the first row of every profile has no preceding frame and would otherwise fall outside any session.
- Number the sessions with SUM(is_new_session::int) OVER (PARTITION BY profile_id ORDER BY started_at, stream_id ROWS UNBOUNDED PRECEDING). A running count of boundaries is the gaps-and-islands device. Specify ROWS rather than letting the default RANGE frame apply, because RANGE lumps peer rows that share a started_at into one frame position — the same reason the ORDER BY carries stream_id as a tie-break.
- Group by (profile_id, session_number) and aggregate: MIN(started_at), MAX(effective_end), COUNT(*), COUNT(DISTINCT content_version_id), SUM(played_seconds).
- The running maximum makes the sessions disjoint in wall-clock time, but it does not make SUM(played_seconds) an elapsed-time figure: two devices playing at once contribute overlapping seconds, so the total can exceed session_end minus session_start. Report it as consumption and say which of the two the requester asked for.
Worked solution 30 min
- Materialise the effective_end CTE and check for rows where effective_end < started_at, which means negative played_seconds or a clock-skewed ended_at and must be quarantined before anything else.
- Add the running MAX and the boundary flag; verify that exactly one row per profile has prev_max_end IS NULL and that it is flagged as a session start.
- Add the running SUM to number sessions; confirm the numbers are dense and begin at 1 within each profile.
- Aggregate and reconcile: SUM(stream_count) across all sessions must equal the input row count.
- Run the overlap fixture — one profile with A 10:00-12:00, B 10:05-10:10 and C at 10:45 — and confirm it collapses to one session. With LAG(effective_end) in place of the running MAX it splits into two, and the second session's start, 10:45, sits inside the first session's span.
Follow-up
- Two devices on one profile play simultaneously. Is that one session or two, and what changes in the query for each answer?
- Re-run at a 10-minute and a 60-minute idle gap. How far does session count move, and which number do you publish and defend?
- Can SUM(played_seconds) for a session ever exceed session_end minus session_start, and what does it mean when it does?
Walk us through your thought process for a recent analysis you conduct…
Walk us through your thought process for a recent analysis you conducted.
Approach
- 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.
- 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?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you prioritize tasks when faced with multiple deadlines?
How do you prioritize tasks when faced with multiple deadlines?
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
- 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?
Describe a situation where you had to make a decision based on incompl…
Describe a situation where you had to make a decision based on incomplete data.
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.
- 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 would you design an A/B test for a new feature on the platform?
How would you design an A/B test for a new feature on the platform?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
- Name the guardrails that would stop a launch even on a positive primary result.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
Explain a complex data project you have worked on, including the metho…
Explain a complex data project you have worked on, including the methodologies used.
Approach
- Clarify what is being asked and what a complete answer would contain.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Cut variance with CUPED and pre-period stratification
A 14-day account-randomised test on qualified hours per account-week has a 0.90 percent MDE, and the team believes the true effect is near 0.40 percent. You hold eight weeks of prior fct_stream data. Using each account's played_seconds over the 28 days before assignment as covariate X and the in-test metric as Y, with corr(X, Y) = 0.62 measured in the control arm, apply CUPED and pre-assignment stratification. Give theta, the variance reduction, the resulting MDE, your handling of accounts with no pre-period, and the check that the estimate is still unbiased.
Approach
- Define the adjusted outcome Y_cuped = Y - theta * (X - Xbar), with Xbar pooled across arms and theta = Cov(X, Y) / Var(X). The estimator is unbiased for any theta; this theta is the one that minimises variance.
- Translate correlation into reduction: Var(Y_cuped) = Var(Y) * (1 - rho^2). The MDE scales with the standard deviation, so it falls by a factor of sqrt(1 - rho^2), not by 1 - rho^2.
- Insist the covariate is strictly pre-assignment. Any window overlapping the ramp, and any in-test behaviour, is treatment-affected: the variance still falls, the estimate becomes biased, and the scorecard looks cleaner while being wrong.
- Handle accounts with no pre-period by stratum, not by deletion. Put them in their own stratum where (X - Xbar) is zero so they receive no adjustment, and estimate theta separately there. Dropping them changes the estimand to returning accounts only.
- Stratify assignment itself on pre-period hours decile crossed with signup_platform. That guarantees balance rather than correcting for it afterwards, and its gains are not additive with CUPED because both draw on the same covariate information.
- Report adjusted and unadjusted estimates side by side. They must agree within noise; a large gap diagnoses a broken covariate or broken randomisation, not successful variance reduction.
Worked solution 30 min
- Build one row per account: X = sum(played_seconds) over [assignment_ts - 28d, assignment_ts), Y = qualified hours over the 14-day test. Keep every assigned account, covariate or not.
- Estimate theta by regressing Y on X within the control arm: theta = Cov(X, Y) / Var(X), which with corr = 0.62 equals 0.62 * sd(Y) / sd(X).
- Variance reduction = 1 - 0.62^2 = 1 - 0.3844 = 0.6156, a 38.4 percent cut in variance.
- The MDE scales by sqrt(0.6156) = 0.7846, so 0.90 percent becomes 0.706 percent, an improvement of about 22 percent.
- Compare against the target: 0.706 / 0.40 = 1.77 remaining in MDE, which is 1.77^2 = 3.1 times the accounts or the duration.
Follow-up
- CUPED gets you to 0.71 percent and you need 0.40 percent. What is the cheapest way to close the rest, and what does running longer actually buy given within-account correlation across weeks?
- Someone proposes using the first three days of in-test hours as the covariate because the correlation is 0.81. What do you say?
- Which accounts gain nothing from CUPED, and what does that imply for the variance reduction available in a new-user test?
Qualified streams drop the week a client shipped
Qualified streams in fct_stream (is_qualified, played_seconds, max_position_seconds, app_version, device_type, started_at, ended_at, end_reason) fell 11% on phone in the seven days after version 8.4.0 reached staged rollout. Smart TV and desktop are flat. The mobile team says nothing in the release touched playback. Using fct_stream and dim_profile only, decide within one day whether engagement fell or whether the client stopped reporting playback correctly, and say what evidence settles it. Deliverable: a one-page finding with the decomposition and a recommendation on whether to halt the rollout.
Approach
- Split the 11% into stream count and per-stream played_seconds. Cut phone streams by app_version into 8.3.x and 8.4.0 on the same dates and compare starts per active profile-day against played_seconds per start. A real engagement fall moves starts; a telemetry fall moves played_seconds per start while starts hold.
- Check the accrual mechanics that change silently: share of rows with ended_at null, share with end_reason in ('unknown','app_kill'), and the played_seconds distribution in 5-second buckets from 0 to 60. A heartbeat regression piles rows up just under 30 seconds and raises the null-ended_at share, because is_qualified is a threshold on a counter rather than on the fact of playback.
- Use the staged rollout as the control, but use it correctly. Within phone and within the same dates, prefer profiles that have streams on both versions so each profile is its own before-and-after. Rollout order is not random — early upgraders skew toward frequent users — so report the within-profile change and treat the between-version gap as an upper bound.
- Compare max_position_seconds per start against played_seconds per start. max_position_seconds is the furthest playhead reached and does not depend on how time is accrued, so if the playhead still travels the same distance while played_seconds falls, playback happened and the counter is wrong. That comparison is decisive; the others are only suggestive.
- Recommend on the basis of which number moved. Halt the rollout for an engagement regression only if starts fell. If accrual broke, the halt is a data-integrity call, the affected days need a caveat on every series built on played_seconds, and the music payout path that reads is_qualified has to be told the same day.
Follow-up
- If played_seconds under-reports on 8.4.0, how would you restate the qualified-stream series across the rollout window rather than blanking it?
- Staged rollout is ordered by store and device, not randomised. What does that do to a between-version estimate, and how would you bound the bias?
- The per-stream payout boundary is the same 30-second threshold. Who needs to know, and before what deadline?
For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
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 had to persuade stakeholders to adopt y…
Share an example of a time you had to persuade stakeholders to adopt your recommendations.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
- 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?
Announce a stream-definition change that shifts payouts
You find that the 60-second idle gap used to sessionise heartbeats into fct_stream rows splits one continuous listen into two streams whenever a phone backgrounds briefly on cellular. Correcting the gap lowers qualified stream counts on phones by an estimated four percent; total played_seconds is unchanged. Per-stream counts drive rights-holder payout shares. Deliverable: what you verify before telling anyone, the order in which you take it to the engineering owner, finance and content partnerships, and your recommendation on restating history.
Approach
- The probe is whether you can tell a technical correction from a commercial decision and keep them apart in the room. Verify the split streams are genuinely one listen before anything else: same profile_id, same content_version_id, contiguous max_position_seconds across the boundary, and a gap distribution with a spike at the background-timeout duration rather than a smooth tail.
- Compute the distributional effect, not the average. The four percent aggregate is not what anyone will argue about; recompute under the corrected gap and report which rights_holder_id groups gain and lose share, because short-form catalogue on mobile is where the splits concentrate and that is not spread evenly across counterparties.
- Defend the new rule on its own terms rather than on the direction of the number. The idle gap is a choice, so the argument is evidence that the two rows describe one continuous listen — never that the corrected count is lower and therefore more conservative, which invites the symmetric accusation next time the fix goes the other way.
- Name the ownership boundary out loud: engineering owns the sessionisation rule, finance and partnerships own whether payouts are restated. Conflating them is how a correct fix gets blocked by a commercial objection it should never have been exposed to.
- Sequence the conversations so the number stops moving before it leaves the building: engineering owner first to confirm the rule and land the fix, finance second to size the restatement, partnerships last. Recommend restating history for internal metrics so trends stay comparable, and recommend against retroactive payout adjustment unless the contracts require it — naming who must answer that contractual question rather than answering it yourself.
Follow-up
- Partnerships asks you to hold the fix until after the quarter closes. What do you do, and who else needs to know you were asked?
- How do you present a change that raises some counterparties' shares and lowers others', in the same meeting, to people who will compare notes afterwards?
- The four percent estimate itself has an interval spanning roughly two to seven percent. Does that change the recommendation or only the sequencing?
Arbitrate three requests for the same analyst week
Three requests land on the same Monday for the same week of your time. Growth wants trial-to-paid conversion recut by signup_channel before a budget meeting on Thursday. The playback team wants rebuffer ratio cut by cdn_pop, device_type and network_type after a bitrate ladder change went out Friday. Catalogue wants a displacement estimate for a title that arrived on a rights window last month. You have roughly four working days. Deliverable: your ordering with the criterion behind it, what you tell the two people who do not get the week, and one thing you ship to all three by Tuesday.
Approach
- The probe is your criterion, not your ordering. Rank by how reversible the pending decision is and how fast the evidence decays, and say so; ranking by requester seniority is the default answer and reads as one.
- Put the playback request first on those grounds: a bitrate ladder change is live and degrading real playback now, and rebuffer ratio is defined on a trailing 24 hours, so the evidence is perishable and the query is small. Cheap and perishable beats expensive and durable.
- Ask each requester what they do differently at each plausible answer. The trial-to-paid recut is a defined tree metric — trailing 28 days, read at trial length plus 21 days so dunning retries have resolved — so it is largely a filter plus an hour confirming that unattributed stays its own line rather than being reallocated pro rata.
- Treat the displacement estimate as the only genuinely new analysis and scope it rather than squeezing it: it needs a matched comparison group and a pre-registered window, because consumption of the rest of the catalogue moves on the release calendar regardless. Defer it with a date and a stated shape, never silently.
- Reply to all three in one thread with the ordering and the criterion visible, so a stakeholder who disagrees argues with the criterion instead of escalating about their position in the queue.
Follow-up
- The catalogue lead escalates to your manager on Tuesday. Walk me through that conversation.
- How does the ordering change if the bitrate ladder change was already rolled back on Saturday?
- All three deadlines are genuine and none can move. What do you actually cut?
- 01
Share an example of a time you had to persuade stakeholders to adopt your recommendations.
- 02
You find that the 60-second idle gap used to sessionise heartbeats into fct_stream rows splits one continuous listen into two streams whenever a phone backgrounds briefly on cellular. Correcting the gap lowers qualified stream counts on phones by an estimated four percent; total played_seconds is unchanged. Per-stream counts drive rights-holder payout shares. Deliverable: what you verify before telling anyone, the order in which you take it to the engineering owner, finance and content partnerships, and your recommendation on restating history.
- 03
Three requests land on the same Monday for the same week of your time. Growth wants trial-to-paid conversion recut by signup_channel before a budget meeting on Thursday. The playback team wants rebuffer ratio cut by cdn_pop, device_type and network_type after a bitrate ladder change went out Friday. Catalogue wants a displacement estimate for a title that arrived on a rights window last month. You have roughly four working days. Deliverable: your ordering with the criterion behind it, what you tell the two people who do not get the week, and one thing you ship to all three by Tuesday.
Is this an official Faire interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Faire. Rounds and questions reflect what candidates have reported, not a process Faire has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the typical difficulty level of interviews for this role?
The interviews for the Data Scientist position at Faire generally range from average to difficult. Candidates should expect a mix of technical challenges and behavioral questions that assess cultural fit.
PracHub interview research ↗How should I prepare for the technical assessments?
Focus on practicing coding and data manipulation problems. Familiarize yourself with common machine learning algorithms and their applications, as well as SQL queries and data analysis techniques.
PracHub interview research ↗What qualities do successful candidates typically exhibit?
Successful candidates often demonstrate strong analytical and problem-solving skills, effective communication, and the ability to work collaboratively within teams.
PracHub interview research ↗How long does the interview process usually take?
The entire interview process can take several weeks, typically involving multiple rounds, including technical assessments and interviews with various team members.
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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