Barclays · Data Scientist
Updated · 2026-09-24

Barclays Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at Barclays, you sit at the intersection of quantitative modeling, financial engineering, and strategic business decision-making. You will build and deploy sophisticated analytical models, predictive algorithms, and data-driven solutions that directly influence banking products, risk assessment, wealth management, fraud detection, and compliance operations. Your work touches millions of retail and institutional customers globally, requiring rigorous thinking and massive scale data handling.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Barclays candidates report 5 rounds · ≈ 4-6 weeks. The stages below are what candidates describe, not a published process.

Reconcile amounts in minor units and currencySeparate authorization, settlement and dispute outcomes cleanlyRead vintage curves, not blended portfolio averages

34 min read

Practice 18 Data Scientist prompts
1Company bank questionsSnapshot · Sep 24, 2026 PT
18Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

As a Data Scientist at Barclays, you sit at the intersection of quantitative modeling, financial engineering, and strategic business decision-making. You will build and deploy sophisticated analytical models, predictive algorithms, and data-driven solutions that directly influence banking products, risk assessment, wealth management, fraud detection, and compliance operations. Your work touches millions of retail and institutional customers globally, requiring rigorous thinking and massive scale data handling.

This role is critical for driving digital transformation and modernizing financial services within a heavily regulated global institution. You will collaborate closely with software engineering, product management, and business stakeholders to translate complex financial datasets into actionable intelligence. Whether you are optimizing algorithmic trading pathways in markets sales technology, identifying sophisticated fraud patterns, or personalizing wealth management portfolios, your insights will shape executive strategy and customer experience.

Expect a high-visibility, intellectually demanding environment where your technical acumen must be matched by strong business intuition and risk awareness. The work requires balancing rapid model deployment with rigorous governance standards typical of a major tier-one bank. You will find yourself solving complex multi-variable problems, designing robust frameworks, and communicating technical findings clearly to non-technical stakeholders across the organization.

01

Online Application

reported

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What to demonstrate

  • Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
  • Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
  • Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

  • Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
  • Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
  • If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
PracHub interview research
02

Recruiter Phone Screen

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
03

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
04

Behavioral and Technical Rounds

reported

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

  • Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
  • Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
  • Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

  • For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
  • Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
  • Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
PracHub interview research
05

Deep-Dive with Hiring Manager

reported

Much of this round runs on your own history, but the manager is not collecting a project list. They are working out what it is like when something goes wrong on your watch: how late the bad news tends to arrive, and whether a number you hand over has been checked by anyone including you. That is why the strongest material is a project where you can describe the part that did not work and what it cost. A result you cannot take full responsibility for, however clean, gives them nothing to trust you with afterwards.

What to demonstrate

  • Whether you volunteer the limits of a result you are proud of, or wait to be pushed onto them
  • How errors surfaced in your past work, and whether you or somebody else found them
  • Whether the scope you claim matches the level of detail you can still produce about it
  • What you did the first time a stakeholder acted on something of yours that turned out to be wrong

How to prepare

  • Rebuild one headline figure from memory down to the join and the filter, so a question about the denominator does not stall the conversation
  • For each project you raise, write the sentence you would say to someone who had already acted on a number that later turned out wrong
  • Mark which parts of a project were yours and which belonged to other people, and state that boundary yourself before anyone asks
PracHub interview research

PracHub editorial advice for the preparation topics above.

01

Reading the most recent months of fraud and dispute rates as final

Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.

02

Using written premium as the denominator of a loss ratio

Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.

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

Solving silently instead of narrating the reasoning

Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.

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

15 technical prompts3 include a worked solution

Walk through Bayes theorem and provide a practical example of how you …

medium
statistics and probability

Walk through Bayes theorem and provide a practical example of how you would apply it to transaction fraud detection.

Approach
  1. Say what the estimate is of, and over what population it generalises.
  2. Write down the assumption the method needs before you use the method.
  3. Translate the result into the decision it informs, in one plain sentence.
Follow-up
  • What sample size would you need to detect an effect half this size?
  • How would you explain this result to someone who does not know statistics?

How would you set up a dashboard to monitor the real-time performance …

medium
machine learning and modelling

How would you set up a dashboard to monitor the real-time performance and latency of a fraud detection model?

Approach
  1. Say how the offline result would be validated online before it is trusted.
  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?
  • How would you choose the decision threshold, and who owns that choice?

Write integrity checks for the authorization and settlement lifecycle

easyWorked solution
data qualityminor unitsfx reconciliation

You are given fct_payment_authorization as a pandas DataFrame with auth_id, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, is_reversal, parent_auth_id, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency and settlement_fx_rate. Write a function returning one row per integrity check with the check name, failing row count, failing share and up to five example auth_id values. Cover at least six checks, one of which reconciles captured_amount_minor against settlement_amount_minor through settlement_fx_rate. Partial capture, zero-amount verification and a decline with no capture are all legitimate and must not be flagged.

Approach
  1. Separate contract violations from observations before writing any code: an approved row carrying a decline_reason_code is structurally impossible, while a capture two days after requested_at is merely slow and belongs in a different severity tier.
  2. Express each check as a boolean mask over the whole frame and collect the masks in a dict, so the summary table is one comprehension over mask.sum() rather than a row loop.
  3. For the reconciliation, leave minor units before comparing: expected = captured_amount_minor / 10exponent[transaction_currency] * settlement_fx_rate * 10exponent[settlement_currency]. Build the exponent table covering zero-decimal and three-decimal currencies instead of assuming two everywhere.
  4. Guard the legitimate cases explicitly so each mask fires only on the genuine contradiction: captured_amount_minor below amount_minor is partial capture, amount_minor of zero on an approved row is account verification, a null captured_at on a declined row is correct.
  5. Sort the output by failing share times a stated severity weight, because a check firing on 0.01 percent of rows can still be the one that breaks a ledger reconciliation.
Worked solution 25 min
  1. Assert auth_id is unique, then build a currency exponent lookup that includes the zero-decimal and three-decimal currencies present in the data.
  2. Define masks for: approved with non-null decline_reason_code; declined with non-null captured_at; captured_amount_minor above amount_minor with parent_auth_id null; captured_at before requested_at; settled_at before captured_at; is_reversal true with parent_auth_id null; settlement_currency differing from transaction_currency while settlement_fx_rate is null.
  3. Add the exponent-aware reconciliation mask with a tolerance of one minor unit plus a small relative term.
  4. Assemble a frame of check_name, n_failing, pct_failing and up to five sample auth_id values, ordered by severity then share.
  5. Read five flagged rows per check by hand and confirm each is genuinely contradictory before reporting any counts.
EXPECTED RESULTA DataFrame of at least eight rows, one per check, each with n_failing, pct_failing and example auth_id values. The reconciliation check should fire on a whole currency at once rather than on scattered rows, because an exponent error is systematic while an FX error is not.
Follow-up
  • Which of these would you run as a blocking pipeline assertion and which as a monitored metric, and why?
  • The FX check fails on 3 percent of rows, all in one settlement currency. How do you decide between a data bug and a rounding convention?
  • How would you detect that a currency's minor-unit exponent is wrong in your reference table, using only the transaction data?

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 ↗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 ↗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.

Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.

Describe a situation where your data analysis contradicted the intuiti…

medium
behavioural and stakeholder questions

Describe a situation where your data analysis contradicted the intuition or opinion of a senior product manager. How did you resolve the disagreement?

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. Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
  • What would you do differently if you ran that project again?
  • What did you decide not to do, and why?

Turn a one-line fraud-number request into a scoped brief

easy
scopingmetric definitiondenominators

A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.

Approach
  1. Establish the decision behind the question first, because a risk-rule change, a board number and a merchant contract negotiation need different denominators, and asking which one is not stalling.
  2. Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
  3. Commit to a default so the reply is useful even if nobody answers, for example net fraud loss in basis points of settled volume, attributed to the requested_at month, matured months only.
  4. State the exclusions in the same breath as the default: non-fraud dispute categories, transaction months with less than 120 days of maturity, and first-party abuse that arrives coded as consumer_dispute.
  5. Give a delivery time for the default and a longer one for the fuller cut, so the choice between them carries a visible cost.
Follow-up
  • They come back wanting it by merchant for a contract negotiation. What changes in the definition and in the maturity rule?
  • How would you separate first-party abuse from third-party fraud in this data, and what would you refuse to conclude from the split?

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

    Describe a situation where your data analysis contradicted the intuition or opinion of a senior product manager. How did you resolve the disagreement?

  • 02

    A stakeholder messages: what is our fraud rate, and is it going up? You have fct_payment_authorization, fct_card_dispute and dim_customer. At least four defensible answers exist: count-weighted or value-weighted, attributed to the transaction month or to the dispute filing month, and gross or net of recoveries and successful representments. You get one reply before someone else produces an uncaveated number. Write that reply: the clarifying questions you ask, the single default you will produce if nobody answers, and what the default excludes.

  • 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 Barclays interview guide?

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

The technical rounds are moderately to highly rigorous, focusing heavily on core fundamentals like SQL window functions, Python data manipulation, and statistical theory. While you may not always encounter whiteboarding of complex algorithms, interviewers expect clean, efficient code and precise reasoning during case studies and technical screens.

PracHub interview research
How should I prepare for the business and product sense portions?

Focus heavily on mastering A/B testing methodologies, common experimentation pitfalls, product metric design, and root-cause analysis frameworks like metric drop diagnosis. Practice structuring ambiguous business problems by breaking them down into mutually exclusive, collectively exhaustive components before diving into solutions.

PracHub interview research
What is the typical interview timeline from application to offer?

The timeline can vary by region and business unit, typically spanning 3 to 6 weeks from initial online application through recruiter screening, technical assessments, take-home case studies (where applicable), and final rounds with hiring managers and senior leadership.

PracHub interview research
Are remote or hybrid work options available for Data Scientists?

Barclays generally operates on a hybrid work model for technology and data roles, requiring a mix of in-office collaboration and remote work depending on the specific team, hub location (such as London, New York, Glasgow, or Noida), and role requirements.

PracHub interview research
Sources & methodology 3 sources ↗

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