As a Data Scientist at DBS Bank, you are at the forefront of the bank’s digital transformation. You are not merely a builder of models; you are a strategic partner who leverages data to influence product design, mitigate financial risk, and optimize the customer experience across one of the world’s most innovative financial institutions. Your work directly impacts how millions of users interact with digital banking services, from fraud detection systems to personalized financial insights.
The role is deeply rooted in product-centric problem solving. You will operate in a complex environment where you must balance technical rigor with clear business utility. Whether you are diagnosing a sudden drop in a key product metric or designing a robust A/B test to validate a new feature rollout, your contributions will be evaluated by their ability to drive measurable business outcomes. You will work closely with cross-functional teams, including engineers, product managers, and business stakeholders, requiring you to translate technical complexity into actionable strategy.
Initial Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
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
Case Studies
reportedA case round is decided by whether you leave the interviewer with a recommendation, not by how much analysis you narrate on the way there. The prompt is open on purpose, so the first job is to convert it into a decision someone could act on: ask what would be done differently depending on the answer. From there name the quantity that would settle it, state the assumptions you need, and commit. Candidates who cover more ground than anyone expected and still end on "it depends" score below candidates who scoped narrowly and said what they would do.
What to demonstrate
- Whether the version of the question you choose to answer is genuinely narrower than the prompt and still worth answering
- Whether the recommendation arrives as an action with a number attached, rather than as a summary of what you looked at
- Whether assumptions are stated at the moment you rely on them, instead of collected into a disclaimer at the end
- Whether you notice when a branch you are exploring would not change the decision either way
How to prepare
- Take six open prompts and write only the scoping move for each: the one-sentence question you would actually answer and the decision it feeds. Give yourself three minutes per prompt and stop there.
- Put a five-minute warning into every practice case and force a closing statement that names the action, the result that would justify it, and the result that would reverse it.
- Record one case and count how long you talked before naming a measurable quantity. Past roughly five minutes, what you are calling scoping is narration.
Final Rounds
reportedWhere a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.
What to demonstrate
- Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
- Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
- Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
- Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered
How to prepare
- Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
- Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
- Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one
PracHub editorial advice for the preparation topics above.
Using written premium as the denominator of a loss ratio
Premium is written at inception and earned pro rata across the exposure period, so in a growing book written premium runs ahead of earned premium and the loss ratio comes out too low, with the error reversing when the book shrinks. The numerator has the mirror-image problem if it omits incurred-but-not-reported reserves, since recent accident periods then look profitable twice over. Both sides must refer to the same exposure period, which is what an accident-period view at a fixed development age enforces.
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.
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.
Reading an observational correlation as a causal effect
Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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.
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?
Measure calibration of a twelve-month default probability from scratch
fct_loan_application gives application_id, model_pd_12m, model_version, decision, funded_at and loan_id. fct_loan_performance_monthly gives loan_id, months_on_book, days_past_due and charge_off_flag. Define the outcome as ever 90 or more days past due, or charged off, by months_on_book = 12. Without sklearn or scipy, build an equal-count binned reliability table, the expected calibration error, the Brier score and its reliability, resolution and uncertainty components, and report the residual the binned identity leaves behind. Restrict to cohorts that have actually reached 12 months on book.
Approach
- Build the label first and name the population it covers out loud: only funded loans have outcomes, so this measures calibration on the approved population. The declined region is unmeasured, and no binning scheme repairs that.
- Restrict to applications whose loans have reached months_on_book = 12. A cohort observed at 8 months has a mechanically lower default rate and will read as systematic over-prediction that is really just immaturity.
- Bin by equal count, deciles of model_pd_12m through a rank-based cut, not equal width. The PD distribution is heavily right-skewed, so equal-width bins put most of the mass in the first bin and leave the risky bins with single-digit counts whose observed rates mean nothing.
- Per bin compute n, mean predicted, observed rate, and the binomial standard error sqrt(o(1-o)/n) so a gap can be read against noise. ECE is the count-weighted mean absolute gap between mean predicted and observed.
- Compute Brier directly as the mean squared error, then reliability = sum of n_k (pbar_k - obar_k)^2 over N, resolution = sum of n_k (obar_k - obar)^2 over N, uncertainty = obar(1 - obar). Report residual = Brier - (reliability - resolution + uncertainty). That identity is exact only for discrete forecasts, so with binned continuous scores the residual is the within-bin spread of the score; a large one means the bins are too wide to support the decomposition.
- Split by model_version. A mixed-version population can look well calibrated in aggregate while each version is biased in opposite directions.
Follow-up
- AUC is unchanged after a population shift but the reliability curve has moved. What happened, and what do you do about it?
- How would you recalibrate without retraining, and what would you check afterwards?
- The top decile shows observed default well above predicted. Is that a calibration problem or a policy problem?
Collapse retry chains and compute a dollar-weighted approval rate
fct_payment_authorization gives auth_id, card_token_id, merchant_id, amount_minor, transaction_currency, requested_at, auth_result, is_reversal, channel and issuer_country. Two reference frames give the minor-unit exponent per currency and a daily rate to one reporting currency. Collapse retry chains first: attempts sharing card_token_id, merchant_id and amount_minor whose consecutive gaps are under 15 minutes form a single attempt, whose outcome is its last row. Exclude reversals and zero-amount verifications. Return a 7-day rolling dollar-weighted approval rate by channel and issuer_country.
Approach
- Filter before grouping: drop is_reversal rows and zero-amount verifications, since neither is a purchase attempt and both would otherwise sit in the denominator.
- Sort by card_token_id, merchant_id, amount_minor and requested_at, take the gap to the previous row within that key, mark a chain start where the gap exceeds 15 minutes or the key changes, and label chains with a cumulative sum of that flag. This is a gap rule between consecutive attempts, not a fixed clock bucket, so a chain may span more than 15 minutes in total.
- Keep each chain's terminal row by requested_at. If a retry was approved, the purchase was approved; keeping the first row reports the decline that caused the retry as the outcome.
- Convert amounts exactly once: amount_minor divided by 10 to the power of the currency exponent, multiplied by the reference rate for the authorization date. Do not reach for settlement_fx_rate, which is null on precisely the declined rows the denominator needs.
- Build the rolling window as a ratio of two rolling sums, approved value over total value, per channel and issuer_country. A rolling mean of daily ratios weights a quiet Sunday the same as a busy Friday.
Worked solution 35 min
- Filter out reversals and zero-amount rows, then sort by the chain key and requested_at.
- Compute the within-key time difference, derive the chain start flag and the chain id, and take the last row per chain with groupby(chain_id).tail(1) after sorting.
- Join the exponent and daily rate tables, compute value_reporting, and assert no nulls remain after the join.
- Aggregate approved value and total value to a daily grain by channel and issuer_country, reindex to a complete date range per group so missing days are zero rather than absent.
- Take 7-day rolling sums of both columns and divide, then confirm one hand-picked group-day against a direct filter.
Follow-up
- The count-weighted rate is flat while the dollar-weighted rate falls 80 basis points. What do you look at first?
- How would you choose the 15-minute window rather than inheriting it?
- A merchant moves from two retries to five. Which of your two rates moves, and is that a real change in approval quality?
How would you handle null values in a dataset containing sensitive fin…
How would you handle null values in a dataset containing sensitive financial records?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Given two tables, one for user profiles and one for transactions, how …
Given two tables, one for user profiles and one for transactions, how would you identify high-frequency users using SQL?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- 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.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
Monthly delinquency roll rates with honest exiting-account denominators
From fct_loan_performance_monthly, compute month-over-month roll rates: for each as_of_month_end and delinquency_bucket, the share of loans in that bucket that are in a worse state at the following month end. Use this disposition convention and apply it without exception: charge-off is the terminal state one rank past dpd_90_plus, so a loan that charges off has rolled forward, not exited; loans that leave the book any other way — prepaid in full, sold, matured, or simply absent at the next month end — are exited. Bucket order is current, dpd_1_29, dpd_30_59, dpd_60_89, dpd_90_plus, then charged_off. Return from_month, from_bucket, accounts, rolled_forward, cured, stayed, exited and roll_rate, and show that the four dispositions sum to accounts on every row.
Approach
- Map the five delinquency_bucket values to ranks 0 through 4 in a small inline VALUES list, and reserve rank 5 for charged_off, because 'worse' is an ordering statement and enum text does not sort that way reliably. Read charge-off from charge_off_flag on the next month's row rather than from delinquency_bucket, which keeps carrying whatever delinquency value the loan held when it was written off.
- Use LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) OVER (PARTITION BY loan_id ORDER BY as_of_month_end), then verify the leading month end is exactly one month after the current one, since a loan missing a month would otherwise appear to cure or roll across a gap it never traversed.
- Classify each loan-month into exactly one of four labels off the adjacent next row, whose rank is 5 when its charge_off_flag is true and its mapped bucket rank otherwise. rolled_forward: the next rank is strictly greater. cured: strictly lower. stayed: equal. exited: there is no adjacent next row at all, which is where prepayment, sale, maturity and a gap in the monthly history land.
- Define rolled_forward as 'any strictly worse rank', not 'rank plus one'. Under normal monthly aging the only reachable worse state is the next bucket, so the two definitions coincide — except at charge-off, where a loan can go from dpd_60_89 straight to rank 5. A strict plus-one test leaves those loan-months matching no branch and breaks the exhaustiveness invariant.
- Be explicit about why charge-off is a roll rather than an exit: routed to exited, dpd_90_plus has no worse state left, so its roll_rate is identically zero by construction — arithmetic, not a credit finding. At rank 5 the dpd_90_plus row reports roll-to-loss, which is the number a loss forecast actually consumes.
- Aggregate by from_month and from_bucket and require that rolled_forward + cured + stayed + exited equals accounts on every row, which is the invariant that proves the classification is exhaustive and mutually exclusive.
Worked solution 45 min
- CTE ranks: an inline VALUES mapping of the five delinquency_bucket values to ranks 0 through 4, joined onto the monthly rows. Rank 5 is not in the map; it is assigned during classification.
- CTE leads: add LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) partitioned by loan_id ordered by as_of_month_end.
- CTE classified: derive next_rank as CASE WHEN next_charge_off_flag THEN 5 ELSE next_bucket_rank END, then a CASE expression producing exactly one label per loan-month — exited when the next month end is null or is not the current month end plus one month, otherwise rolled_forward / cured / stayed by comparing next_rank to bucket_rank.
- Aggregate to from_month and from_bucket with COUNT(*) and four FILTER counts, then compute roll_rate as rolled_forward over accounts with a numeric cast.
- Assert the exhaustiveness invariant in a final HAVING or a separate verification query before trusting any number.
Follow-up
- Project the next three months of dpd_90_plus inflow from these roll rates. What assumption does that projection make, and when does it break?
- A restructure resets days_past_due to zero. What does that do to the cure rate out of dpd_60_89, and how would you separate a real cure from a reset?
- Argue the other convention: prepayment in exited but charge-off also in exited, with roll rates reported only for the four non-terminal buckets. What does that series answer better, and what does it lose?
- The charge-off policy changed from 180 to 120 days. Show where that appears in this table and what it does to the dpd_90_plus roll rate specifically.
What are the key product metrics you would track for a new loan applic…
What are the key product metrics you would track for a new loan application flow?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design the success metrics for a new digital wallet feat…
How would you design the success metrics for a new digital wallet feature?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
- 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 do you balance short-term conversion gains with long-term customer…
How do you balance short-term conversion gains with long-term customer retention?
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.
- Fix the population and the time window before naming any metric.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Explain the difference between vanity metrics and actionable North Sta…
Explain the difference between vanity metrics and actionable North Star metrics.
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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
- 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?
How do you determine the required sample size for an A/B test?
How do you determine the required sample size for an A/B test?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- State the primary metric and the minimum effect worth shipping, then size the test.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
How do you ensure statistical significance when testing a feature with…
How do you ensure statistical significance when testing a feature with low conversion rates?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- 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.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
A dispute rate that does not fall at the right edge
Your chargeback chart slopes downward over the last four months every time it is refreshed. It is built from fct_card_dispute counted by opened_at, divided by settled transactions in the same month. Using auth_id, requested_at, dispute_stage, opened_at, network_reason_code and outcome, explain what the chart is actually showing, then specify the metric you would publish: attribution, denominator, maturity rule, and how immature periods appear on the chart. State the condition under which you would publish a development-factor estimate instead of a blank.
Approach
- Separate the two dates. A dispute belongs to the month of the transaction that caused it, which is requested_at on the linked authorization, not to the month the case was filed. Counting by opened_at mixes old transactions into new months and makes the series unreadable in both directions at once.
- Once attribution is fixed, name the censoring. Recent transaction months have not had time to accumulate their disputes, so the right edge falls by construction. The slope will still be there next month, one month further right, which is the diagnostic that distinguishes it from a real improvement.
- Set the maturity threshold empirically rather than by folklore. From completed transaction months, compute the share of the final dispute count filed by 30, 60, 90, 120 and 180 days, split by reason-code family. Consumer dispute rights commonly run around 120 days from the transaction or the expected delivery date, but several reason codes run materially longer, so one global cut-off under-covers the slow families.
- Specify the published metric: disputes reaching dispute_stage 'first_chargeback' or later, attributed to the transaction month, over settled transactions in that same month, with only months past the measured threshold shown as final.
- Decide how immature months appear. Either omit them, or show a chain-ladder style estimate with an interval in visibly different styling, never as a plain point on the same line as final months. Publish the estimate only when the development pattern is stable across recent completed cohorts; if the reason-code mix or the dispute process has shifted, the factors are estimated on a pattern that no longer applies.
- Note that the count series and the value series mature at different speeds, since net_loss_minor is signed and known only after the case resolves, so a loss-value chart is stale for longer than a case-count chart of the same data.
Worked solution 30 min
- Join fct_card_dispute to fct_payment_authorization on auth_id, rebuild the series by requested_at month, and plot both versions together.
- For transaction months older than twelve months, compute the cumulative share of final disputes filed within 30, 60, 90, 120 and 180 days, split into fraud and non-fraud categories.
- Choose the maturity threshold as the age at which cumulative share reaches a stated coverage level, for example 95 percent, for the slowest family you intend to report, and write that level into the definition.
- Recompute the last twelve months, marking anything below the threshold as incomplete, and write one line saying what the chart no longer claims.
Follow-up
- Your development factors were estimated over a period containing an authentication policy change. What do you do?
- How do you present a month that is 60 days mature to an audience that wants a single number?
- Would you attribute a dispute to the authorization month or the settlement month, and when does the difference matter?
Manual review rate doubles on the day two changes ship
Manual review rate on fct_loan_application, defined as decided_by = 'manual' over decisions in ('approve','decline'), stepped from 9 to 18 percent on a single day. That day a new model_version went live, and the same release changed which referral paths are recorded as manual. Columns: submitted_at, channel, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. Attribute the step across definition, model and population, with a number against each.
Approach
- Establish the shape before the cause. A definition or code change produces a step at the deploy timestamp; a population change produces a ramp. Plot at hourly granularity either side of the release and read which one this is.
- Dual-run the definition. Recompute the metric under both the old and the new recording rules across several months of history. If the historical series under the new rule sits near 18 percent throughout, that part of the step is definitional and should be restated, not investigated.
- Hold the model constant and decompose the residual by policy_rule_hits identifier. A referral rule keyed to a score threshold shows its own step, and naming the rule is what makes the finding actionable to the policy owner.
- Test the score, not the applicants. Compare model_pd_12m distributions by model_version on the same applications where shadow scores exist; otherwise compare adjacent weeks with a population stability index, treating the customary 0.1 and 0.25 marks as heuristics rather than tests, and assess calibration separately from ranking.
- Test the applicants, not the score. Compare channel mix, bureau_score distribution and null-bureau share across the boundary. If those are stable, population is not a driver and should be reported as approximately zero rather than left unquantified.
- Deliver three components that sum to the observed step, each with the team that owns it.
Follow-up
- If the new model is better calibrated, is a higher referral rate the correct outcome or a threshold nobody retuned?
- How would you have caught the recording change before it reached the dashboard?
- What is the swap set here, and which swap group would you inspect first?
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 ↗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.
Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.
Tell me about a conflict you had with a team member and how it was res…
Tell me about a conflict you had with a team member and how it was resolved.
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
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?
Allocate one analyst-week across three competing risk requests
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
- 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.
- 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.
- 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.
- 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.
- 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?
- 01
Tell me about a conflict you had with a team member and how it was resolved.
- 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
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.
Is this an official DBS Bank interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at DBS Bank. Rounds and questions reflect what candidates have reported, not a process DBS Bank has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
The technical rounds are of moderate to high difficulty, focusing on practical application rather than obscure trivia. Expect to write code on the spot, so practice coding SQL window functions and common data manipulation tasks in a live environment.
PracHub interview research ↗What is the best way to prepare for the business case studies?
Structure your approach. Start by defining the objective, identifying the relevant metrics, and then walking through your analytical plan. Interviewers are looking for your thought process, not just the final result.
PracHub interview research ↗How long is the entire process?
The process typically spans several weeks, involving multiple rounds from the initial screen to the final team lead interview. Stay proactive with your recruiter regarding your timeline.
PracHub interview research ↗Is there a focus on specific machine learning techniques?
Yes, be ready to discuss ensemble and boosting techniques, as well as how to choose and evaluate model metrics like precision and recall.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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