Glassdoor · Data Scientist
Updated · 2026-09-22

Glassdoor Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

This guide covers what a Data Scientist at Glassdoor is expected to do and how to prepare for the interview.

When randomisation is off the table, the skill being checked is naming an identification strategy together with the assumption it rests on: parallel trends for difference-in-differences, relevance and exclusion for an instrument, overlap and conditional ignorability for matching. Say the assumption out loud and say how you would try to break it.

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

Separate novelty effects from durable behaviour changeTurn a vague request into a measurable questionSize an experiment before anyone launches it

36 min read

Browse Data Scientist questions

See the practice prompts

17Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

This guide covers what a Data Scientist at Glassdoor is expected to do and how to prepare for the interview.

The Glassdoor interview process

01

Recruiter Screen

reported

Most 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
PracHub interview research ↗
02

Technical Screens

reported

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

  • Whether the query you write matches the plan you just described
  • What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
  • Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

  • Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
  • Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
  • Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
PracHub interview research ↗
03

Take-home or Live Coding

reported

A take-home is graded as an argument, not as a notebook. Somebody reads the submission without you in the room, so every choice has to survive on the page: why the question was framed this way, and what was deliberately left out. The gap between a strong and a weak submission is almost never model quality. It is whether the writeup names the specific question it answers and commits to a recommendation, including what evidence would overturn it. A high-accuracy model attached to no conclusion reads as effort that stopped before the decision.

What to demonstrate

  • Whether the question you answered is stated outright, and whether it is the question the prompt posed rather than an easier neighbour of it
  • Whether the recommendation is specific enough to act on, with the uncertainty attached to it instead of parked in a caveats section at the end
  • Whether analytical choices such as the metric definition, the population filter and the time window are justified in the prose, not merely visible in code

How to prepare

  • Take a dataset you have already worked with, write the one-paragraph conclusion first, then check whether the analysis you were planning actually supports it and cut whatever does not
  • Practise stating a metric in one sentence that fixes the population, the time window and the denominator, then confirm your query computes exactly that sentence and nothing adjacent to it
  • Hand a draft to someone outside the problem and ask them to tell you back what you recommended and why; anything they cannot recover is not on the page yet
PracHub interview research ↗
04

Final Panel Discussion

reported

Where a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.

What to demonstrate

  • Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
  • Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
  • Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
  • Whether you ask what decision is actually on the table before explaining anything

How to prepare

  • Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
  • For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
  • Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else
PracHub interview research ↗

Where candidates lose points

PracHub editorial advice for the preparation topics above.

01

Comparing cohort retention curves of different maturities, or building the curve from users who are still present

A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.

02

Counting on an identity key that changes underneath the metric

visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.

03

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.

04

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

Questions & practice

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

14 technical prompts3 include a worked solution

Implement seven-day activation from its written definition

medium
metric definitioncohortspandasjoins

Implement the seven-day activation rate. Inputs: dim_user with user_id, account_created_at_utc and is_internal; fct_event with user_id, occurred_at_utc and is_core_action. A user activates when core-action events carrying a non-NULL user_id fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). The denominator is every non-internal user whose account_created_at_utc lands in the cohort week, including users with no events at all. Return one row per cohort week with numerator, denominator and rate, publishing only weeks whose last signup is at least eight days old.

Approach
  1. Build the denominator first, from dim_user alone, filtered on is_internal = False. Deriving it from the join is the standard way to lose every user who never fired an event, which is exactly the population the metric is about.
  2. Join events to users on user_id with a left join from the user side, then apply the window as a half-open interval: occurred_at >= created AND occurred_at < created + 7 days. The right bound is exclusive, so an event at exactly created + 7 days does not count.
  3. Count distinct UTC dates per user, not distinct events. Floor occurred_at_utc to date before the nunique, and do it in UTC rather than local time so the threshold does not move with the user's country.
  4. Apply the >= 2 threshold, aggregate to cohort week, and compute the rate by re-summing numerator and denominator per week rather than averaging any per-user or per-day rate. Fix the week anchor explicitly: cohort_week is the Monday of the signup week in UTC, which is what Postgres DATE_TRUNC('week') returns and what any SQL version of this metric will produce. In pandas, subtract dt.weekday days from the floored timestamp. If you reach for periods instead, the anchor that matches is to_period('W') (equivalently 'W-SUN'), whose weeks end Sunday and therefore start Monday; to_period('W-MON') labels weeks that end on Monday, so it runs Tuesday through Monday and its start_time is a Tuesday. Mixing the two shifts every cohort label by one day and silently moves Mondays into the previous week.
  5. Suppress immature weeks: drop any cohort week whose maximum account_created_at_utc is within 8 days of the data cut, and return them as absent rather than as a partial number.
Follow-up
  • The threshold is 2 distinct days. What changes in the reported history if someone moves it to 3, and how would you publish that change?
  • Invited seats and SSO-provisioned users have no pre-signup session. Should they be in this denominator at all, and what does including them do to the rate for sales-assisted accounts?
  • How would you produce the same metric at account grain, and which of the two would you put on the dashboard?

Cluster bootstrap for a per-session rate randomised on users

hardWorked solution
cluster bootstraprandomisation unitvariance

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

Approach
  1. Name the estimand precisely: it is a ratio of sums, sum(converted) over sum(sessions) within an arm, not the mean of per-user rates. Those differ whenever session counts vary across users, and the ratio is what the reported metric is.
  2. Resample the cluster, not the row. Draw n_users user ids with replacement inside each arm and take every session belonging to each draw, including duplicate draws of the same user. Keeping the user count fixed per arm rather than the session count is what preserves the sampling design.
  3. Precompute per-user (converted_sum, session_count) once, so each resample is two vector lookups and a division rather than a repeated filter over the session frame. That turns 2,000 resamples from minutes into under a second.
  4. Take the 2.5th and 97.5th percentiles of the 2,000 differences for the interval, and report the point estimate from the full data rather than from the bootstrap mean, since the bootstrap mean carries the resampling bias.
  5. Compute the naive interval from the session-level binomial standard error and compare widths. The expected inflation is roughly sqrt(1 + (m-1)*rho), with m the mean sessions per user and rho the intraclass correlation of converted within users, so a computed ratio far from that value points at a bug in one of the two intervals.
Worked solution 35 min
  1. per_user = df.groupby(['variant','user_id'])['converted'].agg(['sum','size']); split into two arrays per arm.
  2. point = (t_sum.sum() / t_n.sum()) - (c_sum.sum() / c_n.sum()).
  3. For b in range(B): idx = rng.integers(0, len(t_sum), len(t_sum)); ratio_t = t_sum[idx].sum() / t_n[idx].sum(); same for control; store the difference. Vectorise by drawing a (B, n) index matrix if memory allows.
  4. ci = np.percentile(diffs, [2.5, 97.5]); naive_se = sqrt(p_t*(1-p_t)/n_sessions_t + p_c*(1-p_c)/n_sessions_c); naive_ci = point +/- 1.96*naive_se.
  5. width_ratio = (ci[1]-ci[0]) / (naive_ci[1]-naive_ci[0]).
EXPECTED RESULTA point estimate identical to the full-data ratio difference, a percentile interval containing that point estimate, and a width ratio greater than 1 whose value approximates sqrt(1 + (m-1)*rho) for the data's mean cluster size m and intraclass correlation rho. A ratio of approximately 1.0 means sessions were resampled instead of users.
Follow-up
  • Users average 3.4 sessions and the intraclass correlation is 0.12. What width ratio do you predict before running it, and does your bootstrap land there?
  • Give the delta-method standard error for this ratio and say when you would prefer it to the bootstrap.
  • Half the users in the treatment arm have exactly one session. What does that do to the cluster bootstrap's coverage, and how would you check it?

Permutation test for a difference in conversion rates

medium
permutation testresamplinginference

Write a two-sided permutation test from scratch for a difference in conversion rates, using no scipy hypothesis function. Input: a DataFrame with unit_id, variant in {control, treatment} and converted in {0,1}, one row per randomisation unit. Compute the observed difference in proportions, then build the null distribution by reshuffling the variant labels while holding each arm's size fixed. Report the p-value as (1 + the count of permuted statistics at least as extreme in absolute value) / (B + 1) with B at least 10,000, and return the permutation distribution.

Approach
  1. Name the null being tested: the sharp null that each unit's outcome is the same under either label. That is what licenses permuting labels, and it is stronger than the null of equal means, which matters when someone asks whether the test is valid under unequal variances.
  2. Extract converted to a single numpy array of 0s and 1s and record n_treatment. Every permutation is then just a reshuffle of one array, and the treatment mean is the mean of the first n_treatment entries of the shuffled array.
  3. Vectorise the B permutations with rng.permuted on a tiled 2-D array, or with argsort of a (B, n) random matrix. A Python loop calling np.random.shuffle B times is correct but roughly an order of magnitude slower and often runs past the time limit.
  4. Use the +1 correction in both numerator and denominator. Without it a p-value of exactly 0 is reportable, which is false: the observed labelling is itself one of the permutations, so the smallest attainable p-value is 1/(B+1).
  5. Compare the resulting p-value against a two-proportion z-test as a sanity check. At these sample sizes they should agree closely; a large divergence means the statistic or the shuffle is wrong, not that the permutation test found something subtle.
Follow-up
  • The arms are 200 and 20,000 units. Does the permutation test stay valid, and what happens to its resolution at B = 10,000?
  • Give a 95 percent confidence interval for the difference. Can you get it from this permutation distribution, and if not, what would you run instead?
  • The randomisation unit is user_id but the outcome is per session. What breaks, and what is the fix?

Suggested practice checklist

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.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Build 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 ↗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.

Ownership when the result is inconvenient

Sometimes the honest read is that the initiative did not work, and the person who commissioned the analysis was hoping otherwise. Interviewers want to know whether you softened it. Prepare the case where you delivered an unwelcome result, how you presented the uncertainty without hiding behind it, and what the team did next.

What are some common experimentation pitfalls, and how do you handle n…

medium
behavioural and stakeholder questions

What are some common experimentation pitfalls, and how do you handle novelty effects or network interference?

Approach
  1. Pick a story where you drove the decision, not one where you observed it.
  2. Name the disagreement or constraint, and how you resolved it with evidence.
  3. Quantify the outcome, including what you would not claim credit for.
Follow-up
  • How did you know the outcome was caused by your change?
  • What would you do differently if you ran that project again?

Handle a request for numbers supporting a decision already made

hard
integrityframingstakeholder

A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

Approach
  1. Recognise what is being probed: whether you can find the legitimate request inside an illegitimate framing instead of either complying or refusing on principle. The generic answer promises to push back; the strong one produces something genuinely useful and states its limits in the room, without ambushing anybody.
  2. Separate the decision from the justification. Sunsetting the tier may be correct for reasons the data does not hold, such as support cost, roadmap surface area or sales motion. What you decline is a one-sided document. What you produce is the case read both ways, which also happens to be more useful to the leader.
  3. Build the symmetric analysis: MRR at risk at constant FX, the share of affected accounts with a plausible migration path given seats_licensed and billing_term, the recovery rate assumed for that migration and where it came from, and the downside case in which high-utilisation accounts treat the sunset as a reason to re-evaluate the vendor entirely.
  4. Surface the inconvenient fact privately and early. The highest seat utilisation in the book is a retention signal, and the leader should hold it before the room does, so they can incorporate it rather than be caught by it.
  5. Agree the meeting sentence in advance with the leader, so that nobody is surprised. Something to the effect that the tier is 6% of MRR and its accounts are the most heavily used in the book, and that the case for sunsetting rests on cost and focus rather than on revenue. That is true, it supports the decision on its real grounds, and it stops the deck claiming the numbers endorse it.
  6. Decide your own line before you need it: what you will not put your name to, and that the route if asked anyway is your own manager rather than a confrontation in the meeting.
Follow-up
  • The deck circulates with your analysis included and the downside case removed. What do you do, and by when?
  • What changes if the honest analysis says the sunset is clearly the wrong call?
  • How do you write the same memo when the leader is your skip-level and the meeting is tomorrow?

Choose between three teams' requests with one analyst-week

medium
prioritisationstakeholderjudgement

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

Approach
  1. Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
  2. Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
  3. Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
  4. Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
  5. Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.
Follow-up
  • The dashboard team escalates to your manager. What do you say in that conversation?
  • Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
  • A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?
  • 01

    What are some common experimentation pitfalls, and how do you handle novelty effects or network interference?

  • 02

    A senior leader has already decided to sunset a plan tier and asks you for the analysis showing it is the right call. Accounts on that tier carry 6% of MRR at constant FX and have the highest licensed-seat utilisation in the book. The leader's support matters to your next review cycle, and the decision is being presented in four days. Deliver what you produce, what you decline to produce, and the exact sentence you will say in the meeting where the number appears on a slide.

  • 03

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

PracHub interview preparation framework ↗

Frequently asked questions

Is this an official Glassdoor interview guide?

No. It is PracHub's own research and practice material for the Data Scientist role at Glassdoor. Rounds and questions reflect what candidates have reported, not a process Glassdoor has published, and they change over time. Confirm the current format and scope with your recruiter.

PracHub interview research ↗
How difficult is the interview process, and how much time should I spend preparing?

Expect a rigorous, multi-stage evaluation that typically spans several weeks from initial screen to final panel. Most candidates benefit from 3 to 4 weeks of dedicated preparation, focusing heavily on SQL speed, experimentation edge cases, and product metric frameworks.

PracHub interview research ↗
What differentiates successful candidates from those who are passed on?

Successful candidates combine technical fluency with structured business thinking. Rather than jumping straight into code or formulas, they pause to clarify assumptions, propose structured frameworks, and tie their analytical choices directly back to product and user impact.

PracHub interview research ↗
How is the work culture structured for Data Scientists?

Data Scientists operate in a highly collaborative environment embedded alongside product and engineering teams. The culture values transparency, constructive intellectual debate, and a genuine commitment to improving the workplace experience for job seekers and employers alike.

PracHub interview research ↗
What is the typical timeline from initial recruiter screen to final offer?

The end-to-end timeline typically ranges from 4 to 8 weeks, depending on scheduling availability and team alignment. While some stages move quickly, take-home assignments or virtualonsite coordination can introduce scheduling pauses.

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.

Browse Data Scientist questions