A Data Scientist at Santander plays a pivotal role in transforming one of the world's largest financial institutions into a fully data-driven powerhouse. Operating at the intersection of advanced analytics, machine learning, and financial technology, data scientists here do not just build models—they design the decision-making engines that safeguard assets, optimize capital, and personalize the banking experience for millions of retail and corporate customers globally.
The impact of this role is felt across a vast array of critical business areas. From developing sophisticated credit risk models and real-time fraud detection algorithms to deploying cutting-edge Natural Language Processing (NLP) systems that extract insights from complex financial documents, your work directly influences Santander's bottom line and regulatory compliance. The sheer scale of the bank's data infrastructure presents an incredibly rich environment for solving high-consequence, complex analytical problems that few other industries can match.
Whether you are optimizing customer lifetime value, building conversational AI assistants, or refining predictive models for market trends, a career as a at offers the unique challenge of driving systemic innovation within a highly regulated global framework. It requires a rare blend of technical brilliance, mathematical rigor, and the commercial acumen necessary to translate complex data into clear, actionable business strategies.
Initial Screen
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Core Evaluation
reportedMuch 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
Technical Discussions
reportedA 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
Situational Interviews
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
PracHub editorial advice for the preparation topics above.
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.
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.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Ignoring interference between units in a marketplace experiment
Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you interpret a ROC-AUC curve, and why might it be preferred ov…
How do you interpret a ROC-AUC curve, and why might it be preferred over accuracy in a banking context?
Approach
- Write down the assumption the method needs before you use the method.
- Say what the estimate is of, and over what population it generalises.
- Sanity-check the answer against a simple bound or a simulated case.
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?
Solve this probability puzzle: If you roll two fair dice, what is the …
Solve this probability puzzle: If you roll two fair dice, what is the probability that the sum of the numbers is greater than eight given that the first die rolled is a five?
Approach
- Translate the result into the decision it informs, in one plain sentence.
- Sanity-check the answer against a simple bound or a simulated case.
- Write down the assumption the method needs before you use the method.
Follow-up
- Which assumption here is most likely to be violated in practice?
- How would you explain this result to someone who does not know statistics?
Explain the difference between L1 and L2 regularization and when you w…
Explain the difference between L1 and L2 regularization and when you would choose one over the other.
Approach
- Say what the estimate is of, and over what population it generalises.
- Sanity-check the answer against a simple bound or a simulated case.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
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 do you address class imbalance when training a predictive model fo…
How do you address class imbalance when training a predictive model for fraud detection?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
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?
Walk me through the mathematical formulation of a random forest algori…
Walk me through the mathematical formulation of a random forest algorithm and how it reduces variance.
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
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
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
- 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.
- 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.
- 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.
- 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.
- 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
- Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
- 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.
- Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
- Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
- Recompute one quarter by hand on a five-policy subset and confirm it matches.
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?
Attribute chargebacks to transaction month without fanning out
Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.
Approach
- Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
- Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
- Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
- Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
- Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.
Worked solution 30 min
- CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
- CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
- LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
- Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.
Follow-up
- Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
- Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
- How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?
Reconcile captured authorizations against the daily settlement total
fct_payment_authorization holds captured_amount_minor in transaction_currency, and settlement_amount_minor in settlement_currency with settlement_fx_rate applied at settlement rather than at authorization. The rate is quoted in major units of settlement_currency per major unit of transaction_currency, and dim_currency.minor_unit_exponent carries the ISO 4217 exponent for each code (0, 2 or 3 depending on the currency). Produce a daily reconciliation: for each settled_at date and settlement_currency, return settled_count, total settlement_amount_minor, and the sum of captured_amount_minor converted into settlement minor units. Flag any date and currency pair whose two totals differ by more than one minor unit per settled authorization. Do not sum amounts across currencies anywhere in the output.
Approach
- Restrict to rows that actually settled: settled_at is not null and settlement_amount_minor is not null, which is a smaller population than captured rows because a capture can still be in flight.
- Truncate settled_at to a date with an explicit time zone so the cut matches the ledger's cut, since settled_at is timestamptz and date_trunc on timestamptz silently uses the session time zone.
- Join dim_currency twice, once on transaction_currency and once on settlement_currency, so both exponents are on the row. Minor units are not a common scale: a bare captured_amount_minor * settlement_fx_rate is correct only when the two exponents are equal, and a zero-decimal currency settling into a two-decimal one is wrong by a factor of 100.
- Convert per row as ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle)) — minor units to major in the transaction currency, apply the major-per-major rate, then back to minor units in the settlement currency. The collapsed form ROUND(captured_amount_minor::numeric * settlement_fx_rate * POWER(10::numeric, exp_settle - exp_txn)) is the same expression. Round per row and then sum, not SUM(...) * an average rate, because the rate varies row by row and rounding per row is what the settlement file did.
- Group by the settlement date and settlement_currency together, never by date alone, and carry the currency into every output column name or row.
- Compare the two totals with a tolerance scaled by settled_count, since per-row rounding accumulates linearly in the number of rows rather than being a fixed constant.
Follow-up
- A partial capture means captured_amount_minor is less than amount_minor. Where does that show up in this reconciliation, and where does it not?
- On one currency pair the converted total is consistently about one hundredth of the settlement total, on every date, while the other pairs reconcile. Which two columns do you inspect first, and what single change fixes it?
- The rate is documented as major-per-major. If a feed started publishing it minor-per-minor instead, which pairs would still reconcile and which would break?
- How would you present a total across currencies to a finance partner who has asked for one number?
How would you design an experiment to test the effectiveness of a new …
How would you design an experiment to test the effectiveness of a new personalized marketing campaign on our mobile app?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- Say whether units interfere with each other, and switch design if they do.
- Name the guardrails that would stop a launch even on a positive primary result.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- What would you do if you could not randomise at all?
How would you estimate the total number of credit card transactions pr…
How would you estimate the total number of credit card transactions processed by Santander in a single day?
Approach
- Clarify what is being asked and what a complete answer would contain.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Explain the concept of word embeddings (e.g., Word2Vec, BERT) and how …
Explain the concept of word embeddings (e.g., Word2Vec, BERT) and how they capture semantic meaning.
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
- Clarify what is being asked and what a complete answer would contain.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Define an active customer for a card portfolio
Leadership wants one weekly number for how many customers are active. You have fct_payment_authorization (auth_id, customer_id, auth_result, captured_at, amount_minor, is_reversal, channel) and dim_customer (customer_id, is_current, kyc_status, onboarded_at, closed_at). Write the metric definition you would publish: numerator, denominator, window, and every exclusion with its reason. Then name two ways the definition gives a wrong read, one that inflates it and one that deflates it, and say what you would report alongside it.
Approach
- Fix the grain before writing anything. The number counts distinct customers, so the authorization table has to be reduced to one row per customer_id first; a customer with forty approvals counts once.
- Build the numerator from money that actually moved: auth_result = 'approved' AND captured_at IS NOT NULL, excluding is_reversal = true and zero-amount verification authorizations. An approval that is never captured, or is reversed, has no economic content behind it.
- Build the denominator from the population that could have transacted: dim_customer with is_current = true, kyc_status = 'verified', onboarded_at on or before the window start, and closed_at null or after the window start. Joining without is_current fans the type 2 history out and multiplies the denominator by the number of attribute versions.
- State the window and its recompute cadence, trailing 30 days recomputed daily, and say plainly that this series is not comparable to a calendar-month version of the same definition.
- Name the inflation path (recurring and subscription-only customers, and wallet top-ups, count as active with no user intent behind them) and the deflation path (a customer onboarded inside the window had less than 30 days of opportunity, and activity on products outside this table is invisible).
- Pair it with a depth metric such as settled volume per active customer, so breadth cannot be reported on its own and an acquisition push cannot pass as engagement.
Worked solution 20 min
- Write the denominator query first and record both the raw dim_customer row count and the distinct customer_id count; their ratio is the fan-out factor the is_current filter removes.
- Write the numerator as a distinct customer_id count over the trailing 30 days, applying the three exclusions one at a time and recording the count after each.
- Compute the rate, then recompute with zero-amount verifications left in, and record the difference in basis points.
- Write the two failure modes as one sentence each, naming the customer population affected by each.
Follow-up
- A product team proposes counting any successful login as activity. What breaks?
- The number jumps four percent overnight with no product change. What do you check first?
- How would you report customers onboarded inside the window, given they had a shorter opportunity to transact?
Approval rate fell but approved value did not
Over ten days the count-weighted 7-day approval rate on fct_payment_authorization fell from 91 to 86 percent, while captured value in the reporting currency is flat. Available columns: auth_id, card_token_id, merchant_id, mcc, channel, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, risk_score, is_reversal, parent_auth_id, captured_at, captured_amount_minor, issuer_country. In twenty minutes, decide whether approved value is actually at risk, and hand back a corrected rate together with its denominator and every exclusion written down.
Approach
- Restate the denominator before querying anything. The current one counts every row, so reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and zero-amount verification attempts (amount_minor = 0) are all sitting in it.
- Chart numerator and denominator separately by day. If approved counts are flat and total attempts rose, the rate moved because the denominator grew, which is a different investigation from a rule change and points at a different owner.
- Group declines by decline_reason_code and merchant_id. Retry-driven inflation concentrates in a few soft decline codes at a few merchants; a genuine policy change spreads across merchants within one code family.
- Collapse retry chains: partition by (card_token_id, merchant_id, amount_minor), keep one attempt per 15-minute window taking the best outcome, and recompute both count-weighted and dollar-weighted rates on the collapsed set.
- Convert amount_minor to one reporting currency using each currency's ISO 4217 exponent before any dollar weighting, then cut by channel and issuer_country to confirm nothing is hiding underneath a flat total.
- Report both rates side by side with the exclusion list attached, and state which definition the alert should have been built on.
Follow-up
- How would you pick the retry-collapsing window when merchants retry on different schedules?
- Flat captured value could itself be hiding a mix shift. How do you rule that out?
- What monitor would have caught denominator inflation on the day it started, rather than ten days later?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.
Defend a vintage finding that contradicts the portfolio dashboard
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
Approach
- Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
- Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
- Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
- Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
- Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.
Follow-up
- Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
- Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?
Disagree with a product manager over an approval-rate target
A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.
Approach
- Separate the goal from the metric out loud and agree with the goal first, so the disagreement stays narrow and technical rather than becoming positional.
- Demonstrate the failure rather than asserting it: compute the proposed metric and the dollar-weighted collapsed version over the same recent window, and find a period where they moved in opposite directions.
- Propose the replacement precisely: sum of approved amount_minor over sum of attempted amount_minor, after collapsing retries to one attempt per card_token_id, merchant_id and amount_minor within a 15-minute window, excluding is_reversal rows and zero-amount verifications, with everything converted to one reporting currency before summing.
- Attach the guardrail that makes the target honest: matured first-chargeback rate and net fraud loss in basis points of settled volume, read only on transaction months carrying at least 120 days of maturity.
- Give the product manager something back: the replacement metric cuts cleanly by channel and issuer_country, which makes a roadmap of merchant-specific and authentication fixes legible in a way the blended rate never was.
Follow-up
- How do you identify a retry chain when the merchant varies the amount slightly between attempts?
- The product manager wants a weekly read on the guardrail. What is the earliest defensible signal, and how do you label it?
State honestly what your cutoff change actually contributed
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
- 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.
- 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.
- 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.
- 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.
- 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
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
- 02
A product manager proposes a quarterly goal of raising card authorization approval rate by 150 basis points, measured as approved authorizations divided by all authorizations in fct_payment_authorization. You believe that metric can be hit with no customer benefit, because merchant retry chains, zero-amount verification authorizations, incremental authorizations and reversals all sit in the denominator, and declines skew toward high-value cross-border ecommerce. You support the underlying goal. In one working session, change the metric without killing the initiative, and name the guardrail you would accept.
- 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.
Is this an official Santander interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Santander. Rounds and questions reflect what candidates have reported, not a process Santander has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical is the Data Scientist interview at Santander?
It can be highly technical and detailed, particularly in hubs like New York or Madrid. You should expect in-depth discussions on statistical theory, machine learning mechanics, and NLP. However, in some locations, the technical rounds are balanced with logic puzzles and psychometric assessments rather than purely hands-on coding tests.
PracHub interview research ↗Is prior banking experience required to land a Data Scientist role at Santander?
No, prior experience in banking or finance is not strictly required, though it is considered a strong plus. The hiring teams value strong core data science skills, analytical rigor, and logical reasoning above all else, and they are fully prepared to help you learn the financial domain on the job.
PracHub interview research ↗What is the typical timeline for the interview process?
The timeline can vary quite a bit by region. While some candidates report a highly efficient process spanning just a few weeks, others have experienced longer wait times or administrative delays due to internal coordination. It is best to maintain proactive communication with your recruiter to stay updated on your status.
PracHub interview research ↗How should I prepare for the behavioral and cultural aspects of the interview?
Focus on demonstrating a collaborative, highly professional, and respectful communication style. Santander highly values teamwork, integrity, and structured thinking. Be prepared to discuss your past projects using the STAR method (Situation, Task, Action, Result) to showcase your impact.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
1st Central Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for 1st Central data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
3M Data Scientist Interview Questions & Guide 2026
Communication practice for 3M data scientist candidates: turn an analysis into a recommendation, name the uncertainty honestly, and answer the follow-up a skeptical stakeholder will raise.
ValueLabs Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for ValueLabs data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
vConstruct Data Scientist Interview Questions & Guide 2026
Communication practice for vConstruct data scientist candidates: turn an analysis into a recommendation, name the uncertainty honestly, and answer the follow-up a skeptical stakeholder will raise.