Banco Sabadell · Data Scientist
Updated · 2026-09-24

Banco Sabadell Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

As a Data Scientist at Banco Sabadell, you are at the intersection of advanced analytics and the evolving landscape of European financial services. You will be tasked with transforming raw financial data into strategic insights that drive the bank’s core operations, including credit risk management, mortgage assessment, and sophisticated product recommendation engines. Your work directly influences how Banco Sabadell interacts with its clients and manages its portfolio, making this a high-impact, visibility-heavy role.

SQL is seldom the hardest round and is often the one that eliminates people. The working bar is usually window functions, correct deduplication, and joins that do not silently fan out rows, rather than obscure syntax.

PracHub has no confirmed round sequence for Banco Sabadell. Treat the sections below as preparation areas and confirm the format with your recruiter.

Separate authorization, settlement and dispute outcomes cleanlyDecompose expected loss into PD, LGD, EADReport only matured cohorts for loss metrics

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12Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

As a Data Scientist at Banco Sabadell, you are at the intersection of advanced analytics and the evolving landscape of European financial services. You will be tasked with transforming raw financial data into strategic insights that drive the bank’s core operations, including credit risk management, mortgage assessment, and sophisticated product recommendation engines. Your work directly influences how Banco Sabadell interacts with its clients and manages its portfolio, making this a high-impact, visibility-heavy role.

The environment is one of technical rigor mixed with business pragmatism. You will not only build models but also articulate their value to stakeholders, from department directors to technical peers. Whether you are addressing complex issues like loan delinquency or optimizing customer-facing financial systems, you are expected to bridge the gap between abstract data science methodologies and concrete, bottom-line business outcomes.

The team is relatively lean and highly collaborative. Expect to interact with both leadership and fellow data scientists throughout the interview process, emphasizing the need for both technical depth and clear, business-oriented communication.

01

Preparation focus

editorial

No round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.

What to demonstrate

  • Breadth across SQL, experimentation and product reasoning
  • Ability to state assumptions before choosing a method

How to prepare

  • Drill the practice exercises below and time yourself
  • Prepare three quantified stories about decisions you drove
PracHub interview preparation framework ↗

PracHub editorial advice for the preparation topics above.

01

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.

02

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.

03

Stopping an experiment the moment it crosses significance

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

04

Generalising beyond the population the sample actually supports

State the frame the sample was drawn from and where it diverges from the population you want to talk about: time window, platform, geography, opt-in. If a group is excluded from the frame, either weight to known margins or narrow the claim rather than quietly extending it.

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

9 technical prompts3 include a worked solution

If you notice a sudden drop in model performance for a specific custom…

medium
machine learning and modelling

If you notice a sudden drop in model performance for a specific customer segment, what is your diagnostic process?

Approach
  1. Pick an evaluation metric that matches the cost of each error type, not a default.
  2. Say how the offline result would be validated online before it is trusted.
  3. Set a baseline first, so any model has something honest to beat.
Follow-up
  • Where could label leakage enter this setup?
  • How would you choose the decision threshold, and who owns that choice?

How do you balance model accuracy with regulatory transparency in cred…

medium
machine learning and modelling

How do you balance model accuracy with regulatory transparency in credit scoring?

Approach
  1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  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
  • How would you choose the decision threshold, and who owns that choice?
  • Where could label leakage enter this setup?

Explain the trade-offs between different classification algorithms in …

medium
machine learning and modelling

Explain the trade-offs between different classification algorithms in the context of fraud detection.

Approach
  1. Pick an evaluation metric that matches the cost of each error type, not a default.
  2. Check what information would not exist at prediction time, and exclude it.
  3. Set a baseline first, so any model has something honest to beat.
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?

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 ↗
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.

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

Allocate one analyst-week across three competing risk requests

medium
prioritisationdecision deadlinesstakeholder negotiation

Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

Approach
  1. Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
  2. Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
  3. Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
  4. Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
  5. Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.
Follow-up
  • The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
  • How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?

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?

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?
  • 01

    Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.

  • 02

    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.

  • 03

    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.

PracHub interview preparation framework ↗
Is this an official Banco Sabadell interview guide?

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

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

The process can range from a few weeks to over a month, depending on team availability and the specific requirements of the role.

PracHub interview research ↗
What is the most common reason candidates fail the interview?

Success often hinges on your ability to explain the "why" behind your technical choices. Candidates who focus solely on the code without considering the business context often struggle.

PracHub interview research ↗
Is the interview process mostly technical or behavioral?

It is a balanced blend. You will face rigorous technical testing and case studies, but your ability to communicate effectively with the team and leadership is equally weighted.

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.