A Data Scientist at Walmart Global Tech operates at the intersection of massive-scale data and real-world retail impact. You are not just building models; you are solving complex challenges that directly influence the supply chain, e-commerce experience, and in-store operations for the world's largest retailer. Your work directly affects millions of customers and associates, requiring a blend of technical rigor and a deep understanding of business KPIs.
In this role, you will tackle high-stakes problems ranging from demand forecasting and inventory optimization to personalization and logistics efficiency. Because of the sheer volume of data at Walmart, the ability to design scalable, production-ready machine learning solutions is just as critical as your theoretical knowledge. You will collaborate with cross-functional teams of engineers, product managers, and business stakeholders to turn raw data into actionable strategies that drive the company’s competitive edge.
Candidates often report that interviewers look for a strong connection between your technical methodology and the resulting business impact. Always frame your past projects by identifying the specific KPI you aimed to improve.
Recruiter Screen
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Technical Assessments
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Team-Match Rounds
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.
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Walmart Global Tech Data Scientist interview experience
The process was smooth and professional from the beginning. After the recruiter conversation, the questions stayed focused on what they said they wanted, without drifting into unrelated areas. I appreciated how clear and intentional that made the conversation feel. I had a good back-and-forth and left the call feeling heard. Overall it was a pleasant experience rather than a stressful or chaotic…
Read full experiencePracHub editorial advice for the preparation topics above.
Comparing accounts that received a sales or customer-success touch against those that did not
Assignment of coverage is deliberate and pulls in both directions at once: the largest accounts get a named owner because they are valuable, and the accounts showing distress get one because they are at risk. The comparison therefore mixes a strong positive selection with a strong negative one, and the naive estimate can come out with either sign depending on which assignment rule dominated during the period examined. Nothing about matching on observed size fixes this, because the risk signal that triggered coverage is usually the same signal that predicts the outcome. It needs either an actual randomised or staggered rollout of coverage, or a design built on a capacity constraint or territory boundary that assigns coverage for reasons unrelated to account health.
Computing monthly churn against the entire customer base when contracts are annual
An annual contract has no opportunity to churn except at its renewal date, so an account that is eleven months from renewal is in the denominator while being incapable of appearing in the numerator. The resulting rate is smaller than the real one by roughly the ratio of the base to the renewal-eligible base, and it oscillates with the seasonality of when deals were originally signed rather than with anything about the customers. The corresponding trap on the other side is counting a churn on the date the record was updated rather than on term_end_date, which shifts losses into whichever month the operations team did its paperwork.
Never asking what decision the analysis will inform
Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.
Over-explaining the method and under-explaining the implication
Lead with the answer and what you would do about it, then give the approach when asked. Roughly one sentence of method per three of implication is the right ratio for a stakeholder-facing answer; the interviewer already knows what a regression is.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given a discrete probability distribution, how would you obtain sample…
Given a discrete probability distribution, how would you obtain samples from it?
Approach
- Say what the estimate is of, and over what population it generalises.
- Write down the assumption the method needs before you use the method.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
Follow-up
- Which assumption here is most likely to be violated in practice?
- What sample size would you need to detect an effect half this size?
Describe how you would approach a binary classification problem from s…
Describe how you would approach a binary classification problem from scratch.
Approach
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
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?
Explain the assumptions of linear regression and what happens when the…
Explain the assumptions of linear regression and what happens when they are not met.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Join daily usage to the contract version live that day
fct_usage_daily has account_id, usage_date and net_amount_cents. fct_subscription_period has account_id, subscription_period_id, plan_tier, term_start_date, term_end_date, arr_cents and is_current, with one row per contract term version and every amendment inserting a new row. Attach to each usage row the subscription_period_id whose term brackets usage_date (term_start_date <= usage_date <= term_end_date). pd.merge_asof and an interval-condition merge are unavailable; use sorting and numpy.searchsorted. Then report monthly net revenue by plan_tier. Terms for one account do not overlap, and usage may fall outside every term.
Approach
- Say out loud why the cheap version is wrong: joining on is_current stamps today's plan tier onto last year's usage, so every account that upgraded has its history reclassified and revenue-by-tier becomes a function of when the query ran.
- Sort each account's terms by term_start_date and use np.searchsorted(term_start, usage_date, side='right') - 1 to get the last term that started on or before the usage date. Do it per account group, or globally after encoding (account_id, date) into one monotone key.
- searchsorted only enforces the left edge. Validate the right edge afterwards — usage_date <= the candidate's term_end_date — and set the match to NA where it fails. That NA is usage in a gap between contracts and must stay visible instead of being folded into the expired term.
- Assert non-overlap before trusting the lookup, and write the assertion so it is capable of passing. prev_end = terms.groupby('account_id').term_end_date.shift() is NaT on each account's first row, and NaT < Timestamp evaluates to False rather than NA, so a comparison followed by .fillna(True) has nothing left to fill and the assertion fires on every account's first term whatever the data looks like. Guard the null yourself: assert (prev_end.isna() | (prev_end < terms.term_start_date)).all(). The failure mode of getting this wrong is not a false alarm you notice once — it is an assertion someone deletes because it never passes, after which overlapping terms make searchsorted return one of them with no trace in the output.
- Aggregate after the join, grouping by (usage_date month, plan_tier) with dropna=False so the unmatched bucket appears as its own row and the total still ties to the ungrouped sum of net_amount_cents.
Worked solution 35 min
- terms = terms.sort_values(['account_id','term_start_date']); prev_end = terms.groupby('account_id').term_end_date.shift(); assert (prev_end.isna() | (prev_end < terms.term_start_date)).all()
- Per account group: idx = np.searchsorted(g.term_start_date.values, u.usage_date.values, side='right') - 1; rows with idx < 0 are unmatched.
- Gather subscription_period_id, plan_tier and term_end_date by positional index, then null the match wherever usage_date > the gathered term_end_date.
- monthly = joined.assign(month=joined.usage_date.dt.to_period('M')).groupby(['month','plan_tier'], dropna=False).net_amount_cents.sum()
Follow-up
- An amendment takes effect on the 17th of a month. How do you report that month's revenue by tier?
- What changes if terms can overlap because of a co-term amendment?
- How would you verify this against a SQL implementation using a BETWEEN condition?
How do you efficiently join and aggregate large datasets using SQL?
How do you efficiently join and aggregate large datasets using SQL?
Approach
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Solve a medium-level dynamic programming or array-based problem.
Solve a medium-level dynamic programming or array-based problem.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Net revenue retention on a cohort frozen twelve months back
fct_subscription_period carries subscription_period_id, account_id, arr_cents, plan_code, term_start_date, term_end_date, booked_at, amendment_type, superseded_by_id (the subscription_period_id of the version that replaced this one, null on the live version of a lineage) and is_current. Compute net revenue retention for month M: the summed arr_cents at M for the set of accounts holding arr_cents > 0 at M-12, divided by that same set's arr_cents at M-12. An account can hold more than one live subscription, a churned account contributes zero rather than dropping out, and nothing signed after M-12 may enter either side. Return the ratio plus the expansion, contraction and churn components in cents.
Approach
- Write one reusable as-of ARR snapshot parameterised by a date: rows whose term brackets the date AND whose booked_at is at or before the date, then only the version of each lineage that is still live at that date, then sum arr_cents per account. The booked_at guard matters because an amendment signed in advance otherwise co-exists with the term it replaces and double counts the account.
- Collapse the lineage on superseded_by_id, not on any attribute of the contract. Keep a row when superseded_by_id IS NULL, or when the successor it points at was booked after the date. Ranking with ROW_NUMBER() OVER (PARTITION BY account_id, plan_code ...) instead is wrong in both directions: an amendment that moves the account from one plan_code to another puts the old and new versions in different partitions, so both are rank 1, both bracket the date, and the account's arr_cents is counted twice; and two genuinely concurrent subscriptions that happen to share a plan_code land in one partition, so one of them is deleted.
- Resolve the successor with a LEFT JOIN back to fct_subscription_period on subscription_period_id, and treat a missing successor as not superseded. An inner join would silently delete an account's ARR on a dangling pointer, which is a data-quality bug in the source, not a retention movement.
- Never use is_current for the M-12 side. is_current describes today; using it at the historical snapshot backdates the present contract onto last year's cohort and makes retention look like 100 percent by construction.
- Freeze the cohort from the M-12 snapshot where arr_cents > 0, then LEFT JOIN the M snapshot onto it and COALESCE the missing side to zero. An inner join deletes exactly the churned accounts, which is the single largest way this number gets overstated.
- Return a ratio of sums, not a mean of per-account ratios. The two are different estimands: contraction is floored at zero while expansion is unbounded, so the mean of ratios is both biased relative to the aggregate and far noisier on a skewed revenue base.
- Decompose per account on the delta: positive delta is expansion, negative delta with a non-zero M value is contraction, a zero M value is churn. The three components must reconcile to numerator minus denominator.
- Prove no leakage: any account whose first contract began after M-12 must be absent from both sides, and the cohort row count must be identical in the numerator and denominator.
Worked solution 45 min
- Write arr_asof(d) as a CTE or lateral: from fct_subscription_period s take rows with term_start_date <= d AND term_end_date >= d AND booked_at <= d, LEFT JOIN fct_subscription_period succ ON succ.subscription_period_id = s.superseded_by_id, keep the row when s.superseded_by_id IS NULL OR succ.subscription_period_id IS NULL OR succ.booked_at > d, then sum arr_cents per account_id across every surviving version.
- Sanity-check the lineage rule on one amended account before going further: at a date after the amendment, the account must contribute exactly one version per lineage even when the amendment changed plan_code, term dates or both.
- Materialise base = arr_asof(M-12) filtered to arr_cents > 0, and curr = arr_asof(M).
- LEFT JOIN curr onto base on account_id and COALESCE(curr.arr_cents, 0) AS arr_now.
- Compute nrr = sum(arr_now)::numeric / NULLIF(sum(base.arr_cents), 0), and the three components with SUM(...) FILTER on the sign of arr_now - base.arr_cents and on arr_now = 0.
- Reconcile: assert sum(arr_now) - sum(base.arr_cents) = expansion - contraction - churn, and assert the cohort account count is identical on both sides.
Follow-up
- Net revenue retention can rise while the business shrinks. Show one mechanism and name the guardrail that catches it.
- How do you handle an account that co-terms two subscriptions into one mid-window, so the subscription count changes but the money does not?
- Finance computes this from invoiced amounts and gets a different number. Which is right for which question?
How do you prioritize tasks when working on multiple projects with tig…
How do you prioritize tasks when working on multiple projects with tight 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.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Optimize a provided code snippet for time and space complexity.
Optimize a provided code snippet for time and space complexity.
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.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Triage a sample ratio mismatch before reading the result
An account-randomised test assigned arms 50/50 by hashing account_id. Exposure logging recorded 4,200 accounts: 1,987 in treatment and 2,213 in control. A chi-square goodness-of-fit test against the intended 2,100/2,100 split gives chi-square(1) = 12.2, p about 0.0005. The analyst reports the primary metric up 6% in treatment and asks to ship. Explain what the imbalance implies about that 6%, rank the causes you would investigate first given this domain's data, and name for each the single query against fct_api_request or dim_account that confirms or eliminates it.
Approach
- State the rule before diagnosing anything: a sample ratio mismatch below a 0.001 alarm threshold invalidates the readout. Whatever mechanism removed 113 accounts from one arm almost certainly removed them non-randomly, which makes it a selection effect on the outcome. The 6% is not to be adjusted, caveated or shipped; it is unusable until the mechanism is named.
- Verify the test is testing the right thing. chi-square = (1987 - 2100)^2 / 2100 + (2213 - 2100)^2 / 2100 = 6.08 + 6.08 = 12.16 on one degree of freedom. Confirm the denominator is the intended assignment count rather than the observed total, and that you are testing assignment rather than an analysis population that has already been filtered.
- Rank causes by how this domain actually breaks rather than by textbook order: is_internal accounts filtered after assignment instead of before; exposure logged on a code path that returns 5xx more often in one arm, silently dropping those accounts; assignment recorded at first request but exposure at a later event, so accounts that churned in between appear in only one arm; dim_account type-2 versioning giving one account_id two surrogate keys and two hash inputs; deployment_model = 'self_hosted' accounts that never emit exposure telemetry at all.
- Attach one discriminating query to each: count arms before the is_internal filter; compare the http_status >= 500 rate by arm on the exposure endpoint in fct_api_request; compare the distribution of created_at and churned_at by arm in dim_account; count distinct account_sk per account_id inside the experiment window; cross-tabulate arm against deployment_model.
- Compare counts at three fixed stages, assignment, first exposure, and analysis population, and localise the divergence to one of them. The stage where the arms first separate names the subsystem, and everything downstream of it is a symptom.
- Report the outcome as abort, fix, rerun, and be explicit about what survives: the variance estimate for re-powering, the instrumentation fix, and nothing whatsoever about the effect size.
Worked solution 20 min
- Recompute the statistic: two terms of 113^2 / 2100 = 6.08, total 12.16, p about 0.0005 on one degree of freedom, below the 0.001 alarm line.
- Pull arm counts at assignment, at first exposure, and in the analysis population, and find the first stage at which they diverge.
- At that stage, cross-tabulate arm against is_internal, deployment_model, and the 5xx rate on the exposure endpoint.
- Write the abort note naming the mechanism, the corrected code path, and the rerun date, with the effect estimate explicitly withheld.
Follow-up
- The imbalance disappears once you restrict to accounts with at least one successful request. Does that fix the experiment or confirm the bug?
- What alarm threshold would you set for this check, and why is 0.05 the wrong one for a diagnostic you run on every experiment every day?
- How would you detect a mismatch confined to one segment while the overall split looks clean?
Weekly active organisations fell nine percent over one week
A dashboard reports the weekly active organisation ratio on a trailing seven-day window ending each Wednesday. This week it reads nine percent below last week. You have fct_api_request (account_id, environment, traffic_class, http_status, request_at) and dim_account (account_id, billing_country, account_status, is_internal, is_current). Nothing was released. Decide whether usage actually fell, and hand back a corrected series plus a one-paragraph explanation that a non-analyst can repeat without you in the room.
Approach
- Count the holiday-free business days inside each window before comparing them. A trailing seven-day window spans exactly five weekdays wherever it ends, so its business-day count can only fall to four or fewer when a public holiday lands inside it and can never reach six, while usage in this domain follows a hard five-to-two weekday cycle. Two windows holding different numbers of business days are not comparable whatever the product did.
- Count distinct active accounts per calendar day for the last ten weeks and overlay the two windows. A calendar problem shows as a small number of weekdays sitting at weekend level, not as every day being uniformly lower.
- Cut the daily series by billing_country and index each country-day to that country's trailing same-weekday median, which isolates a regional public holiday from a product change.
- Check the denominator on its own: the metric divides by accounts whose account_status was in trial, free or active_paid for the whole week, so a batch suspension or status backfill moves the ratio with no change in the numerator at all.
- Report the series with each window's business-day count and the holiday dates annotated beside it, and state the residual week-over-week change that survives once the calendar effect is removed. Compare against earlier windows holding the same number of business days rather than dividing by business days, since distinct-account counts are sublinear in window length and dividing would over-correct.
Follow-up
- Distinct account counts are sublinear in the number of days in the window. Why does losing one of five business days reduce the count by noticeably less than twenty percent?
- How would you make this metric comparable across countries with different holiday calendars without hand-maintaining a holiday table forever?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.
Describe a project where you failed; what did you learn and how did yo…
Describe a project where you failed; what did you learn and how did you pivot?
Approach
- Quantify the outcome, including what you would not claim credit for.
- Pick a story where you drove the decision, not one where you observed it.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Scope an open-ended request to predict account churn
A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.
Approach
- The interviewer is probing whether you convert a vague request into a decision with a capacity constraint attached. A candidate who starts talking about model families has already failed the exercise.
- Pin the event and the horizon first. Churn is only possible at term_end_date, so the population is accounts renewing in the next 60 to 90 days, not the whole base. Ask explicitly whether contraction and downgrade count as churn or only full non-renewal, because the three have different base rates and different interventions.
- Pin the action and the capacity. Six people times a realistic number of meaningful interventions per week gives k, and k is what the list is ranked to. Evaluate on precision at k rather than a global AUC over accounts that will never be contacted.
- Audit leakage before choosing features. Every feature needs a timestamp proving it existed before the prediction date. A downgrade amendment, a churn reason code, and a ticket opened after the renewal conversation started are all leaks that will make the offline number look excellent and the live list useless.
- Ask for the counterfactual now rather than later. Coverage is assigned deliberately, so without a held-out slice agreed at the start the intervention can never be evaluated, and you will be asked for its impact in nine months regardless.
- Propose the smallest artefact that closes the loop: a weekly ranked list sized to capacity with two or three inspectable reasons per row, plus a stated policy for accounts below the line.
Follow-up
- The director insists all accounts are in scope, not only those renewing soon. How do you answer without simply refusing?
- Historical non-renewals number about 30 a year. At what point do you tell them a model is the wrong tool and a rules list is better?
- Which candidate features would you drop purely because you cannot date them?
State the measured impact of your own work honestly
You are asked for the business impact of a renewal-risk worklist you shipped nine months ago. Customer success used it, and renewals in the covered segment came in 6 points above the prior year. Coverage was assigned by the team itself: they worked the top of your list and also the accounts they were already worried about. Produce the impact claim you are willing to defend, the number you refuse to claim, and the design you would have asked for at the start.
Approach
- The interviewer is probing whether you can separate what you shipped from what you caused, and whether you would have built the measurement in rather than reconstructing it afterwards. Both halves are being scored.
- Name the confound precisely. Coverage assignment is doubly selected: the largest accounts get an owner because they are valuable, and distressed accounts get one because they are at risk. The naive covered versus uncovered comparison mixes a strong positive selection with a strong negative one and can come out with either sign depending on which rule dominated. Matching on account size does not fix it, because the risk signal that triggered coverage is the same signal that predicts the outcome.
- Split the claims by what each needs to be true. Ranking quality is defensible from precision at k on out-of-time renewals. Adoption is defensible from timestamps showing what share of listed accounts were contacted. The outcome claim is not defensible without a design, and saying so is the point of the exercise.
- Look for identification before giving up on it. A capacity cut-off, a territory boundary, or a period in which the list existed but was unstaffed can assign coverage for reasons unrelated to account health, and any of those supports a bounded estimate.
- State the design you would ask for now and its price: a randomly withheld slice of the list, held for two renewal quarters, with the expected cost in renewals stated openly. That cost is what it takes to be able to answer this question at all.
- Give a bounded number rather than none. Six points with an explicit statement of how much of it you can attribute is more useful than either claiming the whole figure or declining to quantify anything.
Follow-up
- Your manager wants the 6 points in a promotion packet. What wording do you accept, and what do you strike?
- What would have had to be true for the naive covered versus uncovered comparison to be valid?
- If the holdout costs the team real renewals, how do you justify asking for it, and to whom?
- 01
Describe a project where you failed; what did you learn and how did you pivot?
- 02
A customer success director asks for a list of accounts about to churn. You know only that the team has six people and that contracts are annual. Available data is fct_subscription_period, fct_usage_daily, fct_api_request, fct_support_ticket and dim_account. Before writing any code, produce the questions you need answered, a proposed definition of about to churn, and the shape of the artefact you would hand back, including the operating point that turns a score into a decision.
- 03
You are asked for the business impact of a renewal-risk worklist you shipped nine months ago. Customer success used it, and renewals in the covered segment came in 6 points above the prior year. Coverage was assigned by the team itself: they worked the top of your list and also the accounts they were already worried about. Produce the impact claim you are willing to defend, the number you refuse to claim, and the design you would have asked for at the start.
Is this an official Walmart Global Tech interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Walmart Global Tech. Rounds and questions reflect what candidates have reported, not a process Walmart Global Tech has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long should I prepare for the coding rounds?
Prioritize Leetcode Easy and Medium problems. Focus on consistency rather than just volume; ensure you can explain your logic as you code.
PracHub interview research ↗Is there a specific culture I should be aware of?
Walmart Global Tech values humility, collaboration, and a focus on the customer. During interviews, emphasize how your work helped a team or improved a user's experience.
PracHub interview research ↗What is the best way to handle the "take-home" challenge?
Focus on clean, modular code and clear documentation. Your presentation of the results is just as important as the model accuracy, so ensure your business insights are highlighted.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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