Global · Data Scientist
Updated · 2026-09-24

Global Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

The Data Scientist role at Global is a high-impact position situated at the intersection of complex logistics, supply chain optimization, and large-scale product engineering. You will be responsible for transforming massive, high-velocity datasets into actionable intelligence that drives operational efficiency and improves customer delivery experiences. Whether you are working on international transportation networks or marketing intelligence platforms, your work directly influences the movement of millions of packages and the success of business-critical initiatives.

Allocate prep to your weakest link rather than your favourite topic. Of the three things that usually gate the outcome (SQL that is correct under messy joins, sound reasoning about experiments, and structured framing of an open-ended problem), candidates tend to over-invest in modelling theory and under-invest in framing.

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

Turn a vague request into a measurable questionSize an experiment before anyone launches itPick a randomisation unit that respects interference

34 min read

Browse Data Scientist questions

See the practice prompts

16Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

The Data Scientist role at Global is a high-impact position situated at the intersection of complex logistics, supply chain optimization, and large-scale product engineering. You will be responsible for transforming massive, high-velocity datasets into actionable intelligence that drives operational efficiency and improves customer delivery experiences. Whether you are working on international transportation networks or marketing intelligence platforms, your work directly influences the movement of millions of packages and the success of business-critical initiatives.

This role is not merely about building models; it is about solving systemic, real-world problems that require a deep understanding of both technical architecture and product strategy. You will collaborate closely with engineering teams to ensure your solutions are scalable, production-ready, and capable of handling complex constraints. The environment is fast-paced and data-driven, demanding a candidate who can navigate ambiguity, communicate complex technical findings to non-technical stakeholders, and deliver results that move the needle for the business.

Be prepared to discuss how your technical solutions directly impact business metrics. At Global, the most successful candidates are those who can bridge the gap between abstract algorithmic design and concrete operational outcomes.

01

Recruiter Screen

reported

Whoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.

What to demonstrate

  • Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
  • Whether your reason for wanting the role points at the work itself rather than the company's reputation
  • Whether your language signals the level being screened for: what you decided yourself versus what you were handed

How to prepare

  • Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
  • Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
  • Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
PracHub interview research ↗
02

Technical Interviews

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

Case Studies

reported

A case round is decided by whether you leave the interviewer with a recommendation, not by how much analysis you narrate on the way there. The prompt is open on purpose, so the first job is to convert it into a decision someone could act on: ask what would be done differently depending on the answer. From there name the quantity that would settle it, state the assumptions you need, and commit. Candidates who cover more ground than anyone expected and still end on "it depends" score below candidates who scoped narrowly and said what they would do.

What to demonstrate

  • Whether the version of the question you choose to answer is genuinely narrower than the prompt and still worth answering
  • Whether the recommendation arrives as an action with a number attached, rather than as a summary of what you looked at
  • Whether assumptions are stated at the moment you rely on them, instead of collected into a disclaimer at the end
  • Whether you notice when a branch you are exploring would not change the decision either way

How to prepare

  • Take six open prompts and write only the scoping move for each: the one-sentence question you would actually answer and the decision it feeds. Give yourself three minutes per prompt and stop there.
  • Put a five-minute warning into every practice case and force a closing statement that names the action, the result that would justify it, and the result that would reverse it.
  • Record one case and count how long you talked before naming a measurable quantity. Past roughly five minutes, what you are calling scoping is narration.
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

Watching an experiment daily and stopping when it crosses significance

A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.

02

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.

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

Stopping an experiment the moment it crosses significance

Fix the sample size or duration before launch, or use a method built for continuous monitoring such as a sequential test, always-valid confidence intervals, or group-sequential boundaries. Repeatedly checking a fixed-horizon p-value against 0.05 pushes the real false-positive rate well above 5 percent.

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

13 technical prompts3 include a worked solution

Explain the difference between p-values and confidence intervals in th…

medium
statistics and probability

Explain the difference between p-values and confidence intervals in the context of a live product experiment.

Approach
  1. Quantify uncertainty explicitly rather than reporting a point estimate alone.
  2. Translate the result into the decision it informs, in one plain sentence.
  3. Say what the estimate is of, and over what population it generalises.
Follow-up
  • Which assumption here is most likely to be violated in practice?
  • How would you explain this result to someone who does not know statistics?

How would you evaluate whether a new recommendation algorithm is actua…

medium
machine learning and modelling

How would you evaluate whether a new recommendation algorithm is actually improving user engagement?

Approach
  1. Say how the offline result would be validated online before it is trusted.
  2. Set a baseline first, so any model has something honest to beat.
  3. Check what information would not exist at prediction time, and exclude it.
Follow-up
  • Where could label leakage enter this setup?
  • How would you choose the decision threshold, and who owns that choice?

Split a pooled conversion drop into rate and mix

mediumWorked solution
mix shiftdecompositionpandas

You have weekly visit-to-signup counts by segment: a DataFrame with week, device_type, referrer_channel, visitors and signups. The pooled rate fell 0.84 percentage points between two consecutive weeks while several individual segments rose. Write a function that, for a caller-supplied list of segment columns, splits the pooled change into a rate effect, a mix effect and an interaction term that sum exactly to the observed change. Return those three scalars plus a per-segment contribution table sorted by absolute contribution, so the largest single driver can be named.

Approach
  1. State the algebra before coding: the pooled rate is r = sum over segments of w_s * r_s, with w_s the segment's share of the denominator. Then r1 - r0 decomposes exactly into sum(w_s0 * (r_s1 - r_s0)) for rate, sum((w_s1 - w_s0) * r_s0) for mix, and sum((w_s1 - w_s0) * (r_s1 - r_s0)) for interaction. The identity is per-segment, so it holds for any numbers you put in the four slots.
  2. Pivot both weeks onto a common segment index with an outer join so a segment that appeared or vanished is kept rather than dropped, then decide what rate to give a segment with no visitors in one of the weeks, and document the choice. The identity stays exact either way because the missing week's weight is 0, but the attribution does not. Filling the missing rate with 0 sends an appearing segment's entire w_s1 * r_s1 into the interaction term, since w_s0 = 0 makes both the rate term and the mix term (w_s1 - w_s0) * r_s0 identically zero; a vanishing segment then splits as -w_s0 * r_s0 in rate, -w_s0 * r_s0 in mix and +w_s0 * r_s0 in interaction.
  3. The convention used below instead imputes the missing week's rate as that week's pooled rate. A vanishing segment then lands wholly in mix at -w_s0 * r_s0, with rate and interaction cancelling; an appearing segment puts w_s1 * r_pooled0 in mix (volume arriving at the average rate) and only w_s1 * (r_s1 - r_pooled0) in interaction (its rate differing from that average). Impute by which week the segment is missing from, never by argument order, or the swap identities below stop holding.
  4. Guard the division where visitors is 0 so no NaN enters the vectors, because a single NaN poisons every sum. A segment with zero visitors in both weeks contributes exactly 0 and can be dropped; a segment missing from only one week does not contribute 0, and where its contribution lands is settled by the convention above, not by the guard.
  5. Compute the three components as vectors over segments, then sum. Keep the vectors, because the per-segment contribution table is what turns the decomposition into an explanation.
  6. Assert that the three components sum to the observed pooled change within floating-point tolerance. This identity is exact, so a mismatch means an implementation bug, not a modelling judgement.
Worked solution 25 min
  1. Aggregate to one row per (week, segment tuple) with summed visitors and signups, then split into w0 and w1 frames and align with an outer join, filling missing visitors and signups with 0.
  2. Compute w_s = visitors / visitors.sum() within each week, and r_s = signups / visitors only where visitors > 0. Where a week's visitors are 0, set that week's r_s to that week's pooled rate, the stated convention; never leave it NaN.
  3. rate_effect = (w0 * (r1 - r0)).sum(); mix_effect = ((w1 - w0) * r0).sum(); interaction = ((w1 - w0) * (r1 - r0)).sum().
  4. contribution = w0*(r1-r0) + (w1-w0)r0 + (w1-w0)(r1-r0) per segment, which reduces to w1r1 - w0r0; sort by abs and return the head.
  5. assert abs(rate + mix + interaction - (pooled1 - pooled0)) < 1e-12.
EXPECTED RESULTThree scalars summing exactly to the observed pooled change, plus a segment table whose contribution column also sums to that change. In the stated case rate and mix carry opposite signs: several segments converted better while volume moved toward a weaker-converting one, which is what makes the pooled move look inexplicable until it is split.
Follow-up
  • The mix effect accounts for 0.71 of the 0.84 point drop, driven by paid_social volume. What is your recommendation, and what would change it?
  • Why is a two-way split into a counterfactual rate and a residual also exact, and when would you prefer it to the three-way version?
  • Segmenting on device and channel leaves a large interaction term. What does that tell you about the choice of segments?

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.

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
01Fix 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 ↗
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 ↗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.

Work that nobody used is a common and unflattering pattern in data careers, and interviewers probe for it. Have a story about an analysis that changed a decision, and be specific about how you got it in front of the person who could act. Also have one about work that went nowhere, with your reading of why.

Describe a project where your initial approach failed; how did you piv…

medium
behavioural and stakeholder questions

Describe a project where your initial approach failed; how did you pivot and what was the outcome?

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

Defend a flat experiment readout against a post-hoc segment

medium
experimentssegmentationpushback

A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.

Approach
  1. Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
  2. Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
  3. Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
  4. Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
  5. Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.
Follow-up
  • The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
  • Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
  • Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?

Quantify your own impact without claiming the topline you touched

hard
self-assessmentattributioncommunication

You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.

Approach
  1. Recognise what is being probed: whether you apply to your own work the causal standard you would apply to somebody else's roadmap claim. Nearly everyone who would reject 'accounts that do Y retain better' will write 'I drove a 14% increase' without noticing it is the same error with a friendlier subject.
  2. Sort the work by the kind of evidence it can carry. The shipped experiment is the only item with a randomised estimate, so it is the only one where an effect size is defensible, and you claim the interval rather than the point estimate.
  3. Claim the three flat experiments as decisions prevented and price them. Features not built, or built differently, on evidence, with the engineering weeks reallocated as the number somebody else can verify. A defensible null is a delivered decision and should be written as one.
  4. Claim the definition fix as correctness, not as improvement. The old figure was overstated by a specific percentage and appeared in a specific set of recurring documents; the impact is the change it produced in the forecast built on top of it, not a change in churn itself.
  5. Claim the dashboard on usage and displacement: distinct weekly users of it, and the ad-hoc request count for six months before against six months after. If the request log does not exist, record the claim as unverified rather than estimating it upward.
  6. Disclaim the 14% explicitly and once. State that it cannot be separated from seasonality, other teams' launches and a pricing change, and bound your own contribution from above using the shipped experiment's interval converted into headline units.
Follow-up
  • Your shipped experiment's interval was +0.2pp to +1.4pp on activation. How much of the 14% can that account for, and how do you say so without undercutting yourself?
  • A peer in the same cycle claims the full 14%. What, if anything, do you do about it?
  • If you could only keep two of your three claims, which do you drop, and why that one?
  • 01

    Describe a project where your initial approach failed; how did you pivot and what was the outcome?

  • 02

    A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.

  • 03

    You are writing the impact section of your own review. Over the year you ran four experiments, one of which shipped and three of which were flat; you corrected the definition of gross monthly revenue churn so that cancellation is recognised at period_end_utc; and you built a self-serve funnel dashboard. Weekly active accounts rose 14% over the same period. Your reviewer knows the data well. Write the three impact claims you would defend, stating for each what you contributed, what evidence supports it, and what portion of the outcome you are not claiming.

PracHub interview preparation framework ↗
Is this an official Global interview guide?

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

PracHub interview research ↗
How difficult are the coding rounds?

The coding rounds are designed to test your practical problem-solving skills rather than obscure algorithm trivia. If you are comfortable with Python data manipulation and complex SQL queries, you will be well-positioned for success.

PracHub interview research ↗
What is the best way to prepare for the case study?

Structure your approach by clearly defining the business problem, identifying the necessary data, proposing a solution, and outlining how you would measure success. Communication is as important as the technical solution.

PracHub interview research ↗
How long does the entire process typically take?

While timelines vary by candidate and team, most processes move through the stages within a few weeks. Consistency and clear communication with your recruiter are key to a smooth experience.

PracHub interview research ↗
Is this role primarily focused on research or production?

This role is heavily biased toward production. The interviews favor candidates who can build, deploy, and maintain models that function reliably at scale.

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.