Sherwin-Williams · Data Scientist
Updated · 2026-09-22

Sherwin-Williams Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

The role of a Data Scientist at Sherwin-Williams is pivotal in leveraging data to drive strategic decisions and enhance product offerings. By employing advanced analytical techniques, you will help the company better understand customer preferences, optimize operational efficiency, and innovate product formulations. This position not only impacts the business by improving customer satisfaction and retention but also contributes to the overall growth and sustainability of the company, which is a leader in the paint and coatings industry.

The boundary with engineering varies enough to be worth asking about directly. Some seats end at the analysis and the writeup; others carry feature pipelines, scheduling and alerting, which decides whether production data plumbing belongs in your prep at all.

Sherwin-Williams candidates report 2 rounds · ≈ 2-4 weeks. The stages below are what candidates describe, not a published process.

Read vintage curves, not blended portfolio averagesReconcile amounts in minor units and currencyReport only matured cohorts for loss metrics

29 min read

Browse Data Scientist questions

See the practice prompts

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

The role of a Data Scientist at Sherwin-Williams is pivotal in leveraging data to drive strategic decisions and enhance product offerings. By employing advanced analytical techniques, you will help the company better understand customer preferences, optimize operational efficiency, and innovate product formulations. This position not only impacts the business by improving customer satisfaction and retention but also contributes to the overall growth and sustainability of the company, which is a leader in the paint and coatings industry.

In this role, you will work closely with cross-functional teams, including marketing, product development, and supply chain management, to analyze trends and derive actionable insights. The complexity of the data you will handle—ranging from sales figures to customer feedback—presents both challenges and exciting opportunities. You will be at the forefront of shaping data-driven strategies, which makes this role both critical and engaging for those passionate about data science and its application in real-world settings.

01

Screening Call

reported

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

  • Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
  • Whether you name what you have not done instead of stretching to cover every line of the posting
  • Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

  • Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
  • Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
  • Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
PracHub interview research ↗
02

Panel Interview

reported

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

  • Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
  • Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
  • Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

  • Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
  • Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
  • Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
  • Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

02

Recalibrating an underwriting cutoff on approved and funded applicants only

Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.

03

Explaining an aggregate move without decomposing the mix shift

Split the change in the aggregate into within-segment movement and movement in segment weights before you explain it. Every segment's rate can fall while the overall rate rises, purely because volume shifted toward segments that already had higher rates.

04

Reading a dozen metrics with no multiplicity control

Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.

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

What algorithms would you choose for a regression problem, and why?

medium
machine learning and modelling

What algorithms would you choose for a regression problem, and why?

Approach
  1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  2. Check what information would not exist at prediction time, and exclude it.
  3. Say how the offline result would be validated online before it is trusted.
Follow-up
  • What would you monitor after launch to know the model is still valid?
  • Where could label leakage enter this setup?

Discuss the importance of feature selection in model training.

medium
machine learning and modelling

Discuss the importance of feature selection in model training.

Approach
  1. Check what information would not exist at prediction time, and exclude it.
  2. Set a baseline first, so any model has something honest to beat.
  3. Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
  • Where could label leakage enter this setup?
  • What would you monitor after launch to know the model is still valid?

Implement accident-quarter loss ratio at twelve months development

mediumWorked solution
loss ratiodevelopment ageearned premium

fct_policy_period_monthly arrives as a stack of month-end snapshots: each row carries valuation_month alongside as_of_month, policy_id, product_line, written_premium_minor, earned_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor. Compute the accident-quarter loss ratio at exactly 12 months of development: incurred losses over earned premium, both taken from rows whose as_of_month falls in the accident quarter, read from the snapshot 12 months after that quarter closes. Report quarters that cannot reach that age as incomplete rather than dropping them.

Approach
  1. Derive accident_quarter from as_of_month, then define the evaluation snapshot per quarter as valuation_month equal to the quarter's final month plus twelve months. Every figure in the ratio comes from that one snapshot, not from whichever snapshot happens to be newest.
  2. Numerator is paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor over the accident quarter's rows in that snapshot. Loss adjustment expense may be included or not, but the choice applies to every quarter and is named in an output column.
  3. Denominator is earned_premium_minor over the same rows. Written premium is booked in full at inception, so in a growing book it runs ahead of earned premium and drags the ratio down, with the error reversing when the book shrinks.
  4. Left-join the full quarter list against available valuation months so a quarter with no 12-month snapshot yields status incomplete and a null ratio, instead of disappearing and shortening the series without saying so.
  5. Split by product_line, since both the loss ratio level and the speed of development differ by line, and a blended series moves with mix as much as with experience.
Worked solution 30 min
  1. Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
  2. Filter rows to those where valuation_month equals the row's target_valuation, then assert each accident_quarter has exactly one distinct valuation_month left.
  3. Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
  4. Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
  5. Recompute one quarter by hand on a five-policy subset and confirm it matches.
EXPECTED RESULTOne row per accident_quarter and product_line with incurred, earned_premium, loss_ratio, an lae_included flag and status in complete or incomplete. Complete quarters end twelve months before the latest valuation_month, so the four or five most recent quarters carry null ratios.
Follow-up
  • The most recent complete quarter came in four points better than the one before. What do you check before calling it an improvement?
  • How would you estimate the 12-month figure for a quarter that is only 6 months developed, and how would you label the estimate?
  • Why can an expense ratio legitimately use a different denominator from the loss ratio in the same presentation?

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.

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Can you discuss a challenging situation you faced in a team setting an…

medium
behavioural and stakeholder questions

Can you discuss a challenging situation you faced in a team setting and how you resolved it?

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

Retract a published number after finding a currency bug

medium
error disclosureminor unitsprocess repair

Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

Approach
  1. Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
  2. Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
  3. Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
  4. Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
  5. Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.
Follow-up
  • The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
  • What automated check would have caught this, where would it live, and what would it cost in false alarms?

State honestly what your cutoff change actually contributed

hard
impact attributionswap setcounterfactual

Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.

Approach
  1. Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
  2. Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
  3. Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
  4. Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
  5. State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.
Follow-up
  • The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
  • What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?
  • 01

    Can you discuss a challenging situation you faced in a team setting and how you resolved it?

  • 02

    Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

  • 03

    Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.

PracHub interview preparation framework ↗
Is this an official Sherwin-Williams interview guide?

No. It is PracHub's own research and practice material for the Data Scientist role at Sherwin-Williams. Rounds and questions reflect what candidates have reported, not a process Sherwin-Williams 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?

The interview process can be challenging, but with thorough preparation, you can navigate it successfully. Expect technical questions alongside behavioral assessments to gauge your fit within the team and company culture.

PracHub interview research ↗
What differentiates successful candidates?

Successful candidates demonstrate a solid balance of technical skills and the ability to communicate effectively. Showcasing your past experiences and how they align with the company's values will set you apart.

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

The process generally spans 2-4 weeks, depending on scheduling and availability. You will likely have a screening call followed by one or more interviews.

PracHub interview research ↗
Is remote work an option for this role?

While specific policies may vary, Sherwin-Williams has adopted flexible work arrangements. It’s advisable to inquire about remote or hybrid opportunities during your interview.

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.