NatWest · Data Scientist
Updated · 2026-09-24

NatWest Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at NatWest, you are at the intersection of traditional banking and cutting-edge financial technology. You are responsible for transforming vast datasets into actionable insights that drive product innovation, enhance customer experiences, and manage complex financial risks. Your work directly influences how the bank serves millions of customers, making your analytical output a cornerstone of the organization's strategic decision-making.

Learn the economics of the product category before the loop. Marketplaces, subscription products and ad-supported products turn on different core quantities (match rate and liquidity, retention and churn, fill rate and yield) and fail in different characteristic ways.

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

Set fraud thresholds by expected costSeparate authorization, settlement and dispute outcomes cleanlyRead vintage curves, not blended portfolio averages

29 min read

Practice 13 Data Scientist prompts
2Candidate experiences ↗Read their reports
13Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

As a Data Scientist at NatWest, you are at the intersection of traditional banking and cutting-edge financial technology. You are responsible for transforming vast datasets into actionable insights that drive product innovation, enhance customer experiences, and manage complex financial risks. Your work directly influences how the bank serves millions of customers, making your analytical output a cornerstone of the organization's strategic decision-making.

This role is critical because the financial sector is increasingly driven by predictive modeling, personalization, and automated decisioning. You will likely operate within cross-functional squads, collaborating with software engineers, product managers, and business stakeholders to deploy models that operate at scale. Expect to tackle challenges involving fraud detection, customer behavior analytics, and credit risk assessment, all while navigating the regulatory landscape inherent to a major banking institution.

01

Online Assessment

reported

Before 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
PracHub interview research ↗
02

Video Interviews

reported

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

  • Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
  • Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
  • How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

  • Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
  • For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
  • Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.
PracHub interview research ↗
03

Assessment Center

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 ↗

2 candidate reports. Individual accounts describe a particular role and hiring cycle.

Software Engineer

NatWest Software Engineer interview: coding test, video interview and VAC timeline

Online Assessment → Technical ScreenOutcome: offer

I took a coding test and then moved to a video interview, which felt like the most important part of the process. The coding portion was described as fairly simple. By the time I recorded and answered the video interview, I felt better prepared because I could practice first. VAC was also mentioned and seemed straightforward if I had prepared. I did not hear much right after the test. About 1.5 m…

Read full experience
Software Engineer

NatWest Software Engineer interview experience

Technical Screen

A recruiter reached out on LinkedIn and set up an initial technical discussion of about 30 minutes with two people. One gave a short company introduction, then the questions began without much time for a personal introduction. The other interviewer asked a dense set of questions on Java, thread safety, reactive paradigms, data structures, and cloud topics. The atmosphere felt cold and detached. T…

Read full experience

PracHub editorial advice for the preparation topics above.

01

Averaging delinquency across a book that is growing

A loan three months old cannot be 90 days past due, so a portfolio with many recent originations reports a low blended 90+ rate purely from age mix. The blended rate falls fastest exactly when originations grow fastest, which is precisely when credit quality most needs watching, so the metric moves in the reassuring direction during the riskiest period. Only comparisons at equal months on book are valid, which is what a vintage or roll-rate view enforces.

02

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.

03

Naming a model class before naming the deployment constraints

Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.

04

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

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

10 technical prompts3 include a worked solution

How would you explain a complex machine learning model to a non-techni…

medium
machine learning and modelling

How would you explain a complex machine learning model to a non-technical stakeholder?

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

What are the trade-offs between different classification algorithms in…

medium
machine learning and modelling

What are the trade-offs between different classification algorithms in a credit risk context?

Approach
  1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  2. Say how the offline result would be validated online before it is trusted.
  3. 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 how you handle feature engineering when working with imbalance…

medium
machine learning and modelling

Explain how you handle feature engineering when working with imbalanced financial data.

Approach
  1. Set a baseline first, so any model has something honest to beat.
  2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  3. 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?

Estimate a delinquency roll-rate matrix and project twelve months

hardWorked solution
roll ratesmarkov chainsurvivorship

fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.

Approach
  1. Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
  2. Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
  3. Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
  4. Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
  5. Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
  6. State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.
Worked solution 45 min
  1. Sort by loan_id and as_of_month_end, shift to form (from_state, to_state) pairs, and flag pairs whose month gap is not exactly one.
  2. For each loan's final row, assign the absorbing destination from charge_off_flag or prepaid_in_full_flag, and list loans that vanish with neither as an exception count to report.
  3. Apply the restructure rule, then build the 7 by 7 count matrix with a cross-tabulation over ordered state categories and row-normalise it.
  4. Assert row sums equal one and absorbing rows are the identity, then take the current month's bucket distribution as a row vector.
  5. Multiply twelve times, report the charged_off component, and repeat from an all-current vector for comparison.
EXPECTED RESULTA 7 by 7 row-stochastic matrix with identity rows for charged_off and prepaid, a current-to-current diagonal typically above 0.95, roll rates rising with bucket depth, and a 12-month projected charge-off share of a few percentage points from an all-current start and materially higher from the actual book.
Follow-up
  • How would you validate the projection against what actually happened, and over what window?
  • The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
  • When would you prefer a vintage curve to a roll-rate projection, and why?

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

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
01Breadth pass: query fluency
  • Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
  • For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
  • Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Practice prompt ↗Practice prompt ↗Worked solution ↗
02Breadth pass: statistics and inference
  • Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
  • Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
  • Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Practice prompt ↗Practice prompt ↗
03Breadth pass: modelling
  • Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
  • Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
  • Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Practice prompt ↗Practice prompt ↗
04Breadth pass: product judgement
  • Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
  • For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
  • Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Practice prompt ↗Practice prompt ↗Worked solution ↗
05Depth, first area
  • Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
  • Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
  • Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Practice prompt ↗Practice prompt ↗
06Depth, second area, and the seam between them
  • Repeat the depth protocol on the second-ranked area with the same six-problem structure.
  • Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
  • Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Practice prompt ↗Practice prompt ↗
07Integration and re-measurement
  • Re-run the six prompts from day one under the same clock and compare both correctness and time.
  • Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
  • Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

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 describe a time you had to clean a messy dataset and the speci…

medium
behavioural and stakeholder questions

Can you describe a time you had to clean a messy dataset and the specific techniques you used?

Approach
  1. Name the disagreement or constraint, and how you resolved it with evidence.
  2. State the situation in two sentences and spend the rest on your reasoning.
  3. Quantify the outcome, including what you would not claim credit for.
Follow-up
  • What did you decide not to do, and why?
  • How did you know the outcome was caused by your change?

How do you maintain motivation when working on long-term, complex data…

medium
behavioural and stakeholder questions

How do you maintain motivation when working on long-term, complex data projects?

Approach
  1. Quantify the outcome, including what you would not claim credit for.
  2. Pick a story where you drove the decision, not one where you observed it.
  3. 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 did you decide not to do, and why?

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?
  • 01

    Can you describe a time you had to clean a messy dataset and the specific techniques you used?

  • 02

    How do you maintain motivation when working on long-term, complex data projects?

  • 03

    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.

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

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

PracHub interview research ↗
How difficult are the technical interviews?

The difficulty is generally rated as average to high. The focus is not just on coding, but on your ability to apply your knowledge to specific, practical business problems.

PracHub interview research ↗
How much time should I spend preparing for the situational judgment tests?

Take these seriously. Practice common situational judgment test patterns to understand the "corporate logic" they are looking for, which typically prioritizes collaboration, risk management, and ethical decision-making.

PracHub interview research ↗
What is the company culture like?

NatWest is a large, established institution. The culture is professional, structured, and collaborative. Successful candidates are those who respect the bank's processes while still bringing analytical innovation to the table.

PracHub interview research ↗
Will I be asked about my specific past projects?

Yes, expect deep-dive questions into the projects listed on your resume. Be prepared to explain your methodology, the challenges you faced, and the actual business impact of your work.

PracHub interview research ↗
Sources & methodology 3 sources ↗

Official role evidence, timestamped platform data and clearly labeled preparation advice.