As a Data Scientist at Scotiabank, you are at the intersection of complex financial modeling and customer-centric product innovation. Your work directly impacts how Scotiabank serves millions of customers, ranging from optimizing digital banking experiences to building robust fraud detection systems and personalized financial product recommendations. This role is not merely about running models; it is about translating high-level business objectives into actionable data products that drive measurable value.
You will collaborate with cross-functional teams, including product managers, data engineers, and business stakeholders, to solve ambiguous problems at scale. Whether you are identifying the root cause of a sudden drop in a key product metric or designing an experimentation framework to test new features, your technical rigor and product intuition will be essential. Scotiabank values professionals who can navigate both the depth of statistical theory and the breadth of real-world business constraints.
Initial Screening
reportedData 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
Technical Assessments
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
Behavioral Interviews
reportedMost of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.
What to demonstrate
- Whether you can state the other side's argument accurately before you explain why you disagreed
- What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
- Whether you distinguish being overruled from being wrong, and can give an example of each
How to prepare
- Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
- For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
- Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.
Panel Interviews
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
PracHub editorial advice for the preparation topics above.
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.
Assuming a model is fair because protected attributes are not among its inputs
Postcode, device, tenure, income proxies and even transaction patterns correlate with protected characteristics, so a model can produce a disparate outcome without ever reading the attribute. Credit decisions additionally carry an explainability obligation in many jurisdictions, since a denial has to be accompanied by its principal reasons, which constrains model form and feature engineering rather than being a reporting afterthought. Treating fairness testing and reason-code generation as design constraints from the first model version is far cheaper than retrofitting them to a deployed one.
Comparing periods without accounting for seasonality or day-of-week
Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the concept of statistical significance and how it influences …
Explain the concept of statistical significance and how it influences your decision to deploy a model.
Approach
- 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.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
Bootstrap a fraud loss rate that clusters within merchant
You have a per-transaction frame with auth_id, merchant_id, settled_amount_reporting and net_loss_reporting, both already in one reporting currency. Most rows carry zero loss, a few carry large ones, and losses cluster within merchant. Using only numpy's random generator and no resampling helper from any library, write a bootstrap that returns a 95 percent interval for net fraud loss in basis points of settled volume, resampling merchants with replacement and taking all rows belonging to each drawn merchant. Also produce the naive row-level interval and state which you would report.
Approach
- State the estimator before writing it: total net loss divided by total settled volume, times 10,000. It is a ratio of sums, so each replicate recomputes both sums. Averaging per-transaction loss rates instead would weight a five-unit transaction like a five-thousand-unit one.
- Pre-aggregate loss and volume to merchant level once. For a ratio of sums, drawing merchants and taking all their rows is arithmetically identical to drawing merchant-level (loss_sum, volume_sum) pairs, so a replicate becomes one integer draw plus two vectorised sums rather than a groupby inside the loop.
- Draw B replicates of M merchant indices with replacement, where M is the observed merchant count, compute the ratio per replicate, and take the 2.5th and 97.5th percentiles. Say explicitly that this is a percentile interval and that BCa would correct the skew-induced bias if the decision is close.
- Repeat with independent row draws for the naive interval and compare widths on the same replicate count.
- Report the clustered interval. Rows within a merchant share an acceptance profile, a category code and a fraud exposure, so they are not independent, and the row-level interval understates variance by roughly the design effect.
Follow-up
- Your clustered interval is three times wider. How do you explain that to someone who wanted a tighter number?
- One merchant accounts for 40 percent of losses. What does that do to the interval, and what would you do about it?
- How does this change if the question is whether two months differ rather than what this month's rate is?
Simulate false alarms in a merchant chargeback monitoring rule
Baseline matured first-chargeback rate is 12 per 10,000 settled transactions. A monitoring rule alerts when a merchant's observed monthly rate exceeds twice baseline. For monthly settled transaction counts of 500, 2,000, 10,000 and 50,000, simulate the false-alarm probability per merchant-month under the baseline, and the power to detect a merchant whose true rate is 30 per 10,000. Then, for a portfolio of 4,000 merchants split 60, 25, 10 and 5 percent across those four counts, give the expected number of false alarms per month.
Approach
- Recognise the rule is a threshold on an integer count, not on a continuous rate. At n = 500, twice baseline is 24 per 10,000, so the first observable value above it is 2 chargebacks, or 40 per 10,000. Derive the trigger count for every n before simulating anything.
- Draw binomial counts with numpy at p = 0.0012 and take the share at or above the trigger for the false-alarm rate, then repeat at p = 0.0030 for power. Use at least 200,000 draws per cell so a probability near 0.001 has a usable standard error.
- Cross-check every simulated cell against the Poisson approximation with lambda = n*p, which is tight here because p is tiny. A mismatch almost always means the trigger count is off by one.
- Weight the per-merchant false-alarm probabilities by the portfolio mix, and report the share of expected alerts contributed by each size band rather than only the total.
- Close on the operating consequence: a fixed multiplicative threshold is not a constant false-alarm rate across merchant sizes, so either the threshold scales with n or small merchants need a minimum volume before the rule applies.
Worked solution 30 min
- For each n, compute trigger = floor(2 * 0.0012 * n) + 1 and print the four values before simulating.
- Simulate 200,000 binomial draws per n at p = 0.0012 and take the share at or above the trigger.
- Repeat at p = 0.0030 and record power for the same triggers.
- Compute the Poisson tail 1 - CDF(trigger - 1, lambda = n*p) for both p values and confirm agreement within Monte Carlo error.
- Multiply the false-alarm probabilities by 2400, 1000, 400 and 200 merchants and sum.
Follow-up
- How would you set a threshold that holds the false-alarm rate roughly constant across merchant size?
- The rule reads the transaction month, but disputes arrive for up to 120 days afterwards. What does that do to the alert and how would you fix it?
- What does a month of these false alarms cost, and how would you decide whether it is worth paying?
Describe how you would handle missing data or outliers in a large-scal…
Describe how you would handle missing data or outliers in a large-scale financial dataset.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Write a query to identify the top 5 customers by spend in each region …
Write a query to identify the top 5 customers by spend in each region using partitioned data.
Approach
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
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.
Worked solution 25 min
- Filter to settled rows and derive settlement_date from settled_at with an explicit time zone.
- Join dim_currency on transaction_currency and again on settlement_currency to pick up exp_txn and exp_settle; fail the run if either is null rather than defaulting to 2.
- Aggregate by settlement_date and settlement_currency: COUNT(*), SUM(settlement_amount_minor), and SUM(ROUND(captured_amount_minor::numeric / POWER(10::numeric, exp_txn) * settlement_fx_rate * POWER(10::numeric, exp_settle))).
- Add a derived difference column and a boolean flag where ABS(difference) > settled_count.
- Order by the flag first and then by settlement_date so the exceptions surface at the top.
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 do you balance trade-offs between short-term engagement metrics an…
How do you balance trade-offs between short-term engagement metrics and long-term customer retention?
Approach
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you measure the success of a new feature in the Scotiabank m…
How would you measure the success of a new feature in the Scotiabank mobile app?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
Design a metric to evaluate the effectiveness of our personalized offe…
Design a metric to evaluate the effectiveness of our personalized offer engine.
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
If you notice a sudden drop in a key conversion metric, how would you …
If you notice a sudden drop in a key conversion metric, how would you go about diagnosing the root cause?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you determine the required sample size for an A/B test before l…
How do you determine the required sample size for an A/B test before launching?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- 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
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
What are the most common experimentation pitfalls you have encountered…
What are the most common experimentation pitfalls you have encountered, and how do you mitigate them?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
Design an authorization approval rate a team cannot game
A payments team is given authorization approval rate as its quarterly target, defined as approved authorizations over all authorizations in fct_payment_authorization. Using auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, auth_result, is_reversal, parent_auth_id, channel, issuer_country and requested_at, list three ways the team can raise that number without a single additional customer purchase succeeding. Then write the definition you would put on the dashboard instead, with its exclusions, and name the one guardrail you would always read beside it.
Approach
- Enumerate the rows in the table that are not purchases: zero-amount account verifications, reversals (is_reversal = true), incremental authorizations (parent_auth_id not null) and merchant retries of a declined attempt. Each is a lever on the numerator or the denominator that no customer would ever notice.
- Name the mix levers. Recurring and card-present traffic approve at higher rates than ecommerce and cross-border, so routing changes or a marketing push into an easy channel move the blended rate with no change in decisioning quality at all.
- Work the retry arithmetic rather than asserting it. A declined purchase that is retried three times and succeeds on the fourth contributes one approval and four denominator rows, which is below a healthy blended rate, so suppressing retries raises the published number while some purchases that would have succeeded on a retry now simply fail.
- Replace the definition: dollar-weighted, retry chains collapsed to a single attempt per (card_token_id, merchant_id, amount_minor) within a 15-minute window, excluding reversals, zero-amount verifications and incremental authorizations, with all amounts converted into one reporting currency before summing because amount_minor is in the minor unit of transaction_currency and that unit is not always two decimals. Cut by channel and issuer_country so mix shift shows up as mix shift.
- Guardrail: net fraud loss in basis points of settled volume, read only on transaction months with at least 120 days of dispute maturity. The fastest way to approve more is to decline less risk, and that bill arrives two to four months later.
Worked solution 25 min
- Compute the naive rate for one week: approved rows over all rows.
- Apply the exclusions one at a time (zero-amount, is_reversal, parent_auth_id not null), recording the rate after each, so you can say what each row type was worth to the headline number.
- Add a retry-collapsing CTE keyed on (card_token_id, merchant_id, amount_minor) over a 15-minute window, keeping the last attempt in each chain, and recompute.
- Recompute dollar-weighted in a single reporting currency, then split by channel and issuer_country.
- Write the final one-line definition with its exclusions, plus the guardrail sentence.
Follow-up
- The blended rate is flat but ecommerce cross-border fell 200 basis points. How do you present that?
- Why weight by amount rather than by count, and when does the weighted version mislead?
- Your 15-minute retry window merges two genuine purchases of the same amount at the same merchant. How would you bound that error?
Trailing thirty day volume per customer drops week over week
The trailing 30-day settled volume per active customer is down 7 percent against the same metric seven days earlier. Nothing shipped. You have fct_payment_authorization with requested_at, channel, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, plus dim_customer with is_current, kyc_status, onboarded_at and closed_at for the active denominator. Before anyone writes a retention narrative, decide how much of the 7 percent is calendar structure, and hand back a calendar-robust version of the comparison.
Approach
- Do the window arithmetic first. Thirty days is four whole weeks plus two days, so exactly two weekdays appear five times and the other five appear four times. Sliding the window by seven days changes which two, and card-present and card-not-present volumes differ sharply by weekday.
- Rebuild on a 28-day window, which contains exactly four of every weekday, and see how much of the 7 percent survives. That single change removes the weekday composition effect with no modelling and no assumptions.
- Count the structural events inside each window: public holidays, and the billing anchor days that recurring authorizations cluster on. A window holding one fewer month boundary loses a block of recurring volume that has nothing to do with customer behaviour.
- Decompose by channel, since recurring, card_present and ecommerce have different calendar signatures. A drop concentrated in recurring points at anchor-day placement; one spread evenly across channels does not.
- Compare year over year at a 364-day lag rather than 365, which preserves weekday alignment, and only then read the residual.
- Check the denominator on its own. Active customer counts on a trailing window carry their own calendar structure, and a ratio can move because either side moved.
Follow-up
- Which window goes on the executive dashboard, and what do you give up by choosing it?
- How would you handle a holiday that moves between years, so that a 364-day lag still misaligns it?
- If a genuine 2 percent residual survives, what is the smallest cut that tells you whether it is breadth or depth?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix 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 ↗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.
A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.
Describe a situation where you encountered a significant roadblock in …
Describe a situation where you encountered a significant roadblock in a project. How did you resolve it?
Approach
- Pick a story where you drove the decision, not one where you observed it.
- Close with what you would do differently, concretely.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
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?
Turn a one-line fraud-number request into a scoped brief
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
- 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.
- Offer a short menu rather than an open question: a stakeholder can choose between two named options but cannot specify a denominator from scratch.
- 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.
- 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.
- 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
Describe a situation where you encountered a significant roadblock in a project. How did you resolve it?
- 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
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.
Is this an official Scotiabank interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Scotiabank. Rounds and questions reflect what candidates have reported, not a process Scotiabank has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long should I spend preparing for the technical rounds?
Most successful candidates spend 2–4 weeks reviewing core concepts like SQL window functions, A/B testing theory, and practicing coding problems. Focus on depth rather than breadth.
PracHub interview research ↗What is the most important trait for a successful candidate?
Beyond technical skills, Scotiabank highly values ownership and communication. Being able to explain your technical decisions in the context of business value is what differentiates top-tier candidates.
PracHub interview research ↗Does the interview process involve a take-home assignment?
You may encounter case studies or presentations. These are designed to test your real-world problem-solving skills, so treat them as a professional consultation rather than a classroom test.
PracHub interview research ↗What is the culture like at Scotiabank for Data Scientists?
It is a collaborative, professional environment that emphasizes data-driven decision-making. You will have the opportunity to work on large-scale problems that have a tangible impact on the bank's millions of customers.
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.