As a Data Scientist at Sberbank, you operate at the intersection of massive-scale enterprise data, financial services, and cutting-edge machine learning. Your role involves designing, building, and deploying predictive models and analytical frameworks that directly impact millions of retail and corporate banking customers. Whether you are optimizing credit scoring algorithms, personalizing digital banking experiences, or detecting fraudulent transactions, your work drives strategic decision-making across one of the largest financial institutions in the region.
The complexity and scale of Sberbank mean that your models must balance high statistical rigor with real-time performance and robust governance. You will collaborate closely with software engineers, product managers, and business stakeholders to translate complex business problems into tractable data problems. This requires not only strong technical execution in machine learning and statistics, but also a sharp product sense to ensure your solutions deliver measurable business value.
Expect a fast-paced yet intellectually stimulating environment where you are encouraged to propose innovative modeling approaches while respecting regulatory and risk constraints. Success in this role requires intellectual curiosity, resilience when handling ambiguous datasets, and the ability to communicate technical concepts clearly to non-technical stakeholders.
HR Screening
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
Technical Interview
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Managerial Round
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.
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.
Optimising accuracy on a heavily imbalanced target
State the base rate first, then choose the metric from the relative cost of a false positive against a false negative: precision and recall at the operating threshold, PR-AUC, or expected cost. At a 1 percent positive rate, predicting the majority class for everyone scores 99 percent accuracy and is worthless.
Naming a model class before naming the deployment constraints
Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you calculate the probability of default given a set of correla…
How do you calculate the probability of default given a set of correlated risk factors?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Sanity-check the answer against a simple bound or a simulated case.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
What regularization techniques do you use to prevent overfitting in de…
What regularization techniques do you use to prevent overfitting in deep models?
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.
- 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?
- Where could label leakage enter this setup?
Write integrity checks for the authorization and settlement lifecycle
You are given fct_payment_authorization as a pandas DataFrame with auth_id, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, is_reversal, parent_auth_id, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency and settlement_fx_rate. Write a function returning one row per integrity check with the check name, failing row count, failing share and up to five example auth_id values. Cover at least six checks, one of which reconciles captured_amount_minor against settlement_amount_minor through settlement_fx_rate. Partial capture, zero-amount verification and a decline with no capture are all legitimate and must not be flagged.
Approach
- Separate contract violations from observations before writing any code: an approved row carrying a decline_reason_code is structurally impossible, while a capture two days after requested_at is merely slow and belongs in a different severity tier.
- Express each check as a boolean mask over the whole frame and collect the masks in a dict, so the summary table is one comprehension over mask.sum() rather than a row loop.
- For the reconciliation, leave minor units before comparing: expected = captured_amount_minor / 10exponent[transaction_currency] * settlement_fx_rate * 10exponent[settlement_currency]. Build the exponent table covering zero-decimal and three-decimal currencies instead of assuming two everywhere.
- Guard the legitimate cases explicitly so each mask fires only on the genuine contradiction: captured_amount_minor below amount_minor is partial capture, amount_minor of zero on an approved row is account verification, a null captured_at on a declined row is correct.
- Sort the output by failing share times a stated severity weight, because a check firing on 0.01 percent of rows can still be the one that breaks a ledger reconciliation.
Worked solution 25 min
- Assert auth_id is unique, then build a currency exponent lookup that includes the zero-decimal and three-decimal currencies present in the data.
- Define masks for: approved with non-null decline_reason_code; declined with non-null captured_at; captured_amount_minor above amount_minor with parent_auth_id null; captured_at before requested_at; settled_at before captured_at; is_reversal true with parent_auth_id null; settlement_currency differing from transaction_currency while settlement_fx_rate is null.
- Add the exponent-aware reconciliation mask with a tolerance of one minor unit plus a small relative term.
- Assemble a frame of check_name, n_failing, pct_failing and up to five sample auth_id values, ordered by severity then share.
- Read five flagged rows per check by hand and confirm each is genuinely contradictory before reporting any counts.
Follow-up
- Which of these would you run as a blocking pipeline assertion and which as a monitored metric, and why?
- The FX check fails on 3 percent of rows, all in one settlement currency. How do you decide between a data bug and a rounding convention?
- How would you detect that a currency's minor-unit exponent is wrong in your reference table, using only the transaction data?
How do you optimize a slow-running SQL query that joins multiple finan…
How do you optimize a slow-running SQL query that joins multiple financial transaction tables?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- 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.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Extract the top three spending categories for each customer using rank…
Extract the top three spending categories for each customer using ranking window functions.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- 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 does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Customers with no credit application, avoiding the NOT IN trap
Count current customers who have never submitted a credit application, broken out by segment. dim_customer is a slowly changing dimension type 2, so restrict to is_current = true, kyc_status = 'verified' and closed_at null. In fct_loan_application, customer_id is null for applicants who were not customers when they applied. Write the anti-join, return segment and customer_count, and state in one line what NOT IN (SELECT customer_id FROM fct_loan_application) returns against this table and why.
Approach
- Pin the dimension to one row per customer first: is_current = true already guarantees that, but say so out loud, because forgetting it multiplies every count by the number of attribute versions a customer has accumulated.
- Write the anti-join as NOT EXISTS with a correlated predicate on customer_id, which evaluates per row and is unaffected by nulls anywhere in the applications table.
- Name the failure explicitly: NOT IN against a nullable column compares each candidate to a set containing NULL, the comparison yields UNKNOWN rather than TRUE, and the whole predicate is therefore never satisfied, so the query returns zero rows.
- If NOT IN is required for some reason, add WHERE customer_id IS NOT NULL inside the subquery, which restores the intended semantics, and note that a LEFT JOIN with an IS NULL filter is equally safe.
- Group by segment and sanity-check the total against the unfiltered current-customer count minus the count of distinct applying customers.
Worked solution 20 min
- SELECT segment, COUNT(*) FROM dim_customer c WHERE c.is_current AND c.kyc_status = 'verified' AND c.closed_at IS NULL.
- Add AND NOT EXISTS (SELECT 1 FROM fct_loan_application a WHERE a.customer_id = c.customer_id).
- Group by segment and order by the count descending.
- Run the NOT IN variant alongside it and record that it returns zero rows, then run it again with IS NOT NULL added to the subquery and confirm the counts match the NOT EXISTS version.
Follow-up
- Rewrite it as a LEFT JOIN with IS NULL and say when you would prefer that form to NOT EXISTS.
- How does the answer change if you want customers who never applied as of a historical date rather than today?
- The applications table has 40,000 rows with a null customer_id. What are those rows, and are they a data quality problem or a product fact?
Which metrics would you prioritize when monitoring the health of a fra…
Which metrics would you prioritize when monitoring the health of a fraud detection model?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- 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?
- What would you do if the primary metric and the guardrail moved in opposite directions?
What leading indicators do you monitor before a customer officially cl…
What leading indicators do you monitor before a customer officially closes their account?
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
- 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?
These questions test your ability to connect data science solutions to…
These questions test your ability to connect data science solutions to business objectives, define key metrics, and design user-centric features.
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
- State what result would change your recommendation, so the answer is falsifiable.
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?
Your key engagement metric has plateaued; how do you diagnose the root…
Your key engagement metric has plateaued; how do you diagnose the root cause?
Approach
- 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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
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?
Explain the concept of statistical significance and how you choose you…
Explain the concept of statistical significance and how you choose your alpha and beta levels.
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
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
What are some common experimentation pitfalls you must watch out for w…
What are some common experimentation pitfalls you must watch out for when running concurrent tests?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- 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 conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
How would you design a product recommendation engine for mobile bankin…
How would you design a product recommendation engine for mobile banking users?
Approach
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer would contain.
- 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?
Test a referral rule when reviewers are a shared queue
A proposed risk rule raises the share of ecommerce authorizations routed to manual review. Reviewers work a shared pool of queues serving both arms, so extra referrals from treatment lengthen the wait for control cases too. The current plan randomises by customer_id and reads decision latency plus matured fraud basis points. Explain why that design is biased and in which direction, propose a design that is not, and say what governs its power.
Approach
- Name the violated assumption precisely: a unit's outcome depends on other units' assignments through the shared reviewer capacity, so the stable unit treatment value assumption fails. Customer-level randomisation then estimates a contrast between a degraded treatment and a degraded control, not between treatment and the status quo.
- State the direction. Treatment pushes work into the shared queue, control absorbs part of that wait, so the measured latency difference understates the true effect of full rollout. The test can look acceptable while the rolled-out state is materially worse, which is the expensive failure mode here.
- Move randomisation up to a unit that contains the interference. Either cluster-randomise whole queues or sites, or run a switchback that flips the rule for an entire queue over time blocks. Switchback is usually the better choice here because queue count is small and each queue serves as its own control, removing between-queue heterogeneity.
- Specify the switchback concretely. Block length should be several times the queue sojourn time; discard a burn-in after each switch equal to the 95th percentile sojourn so carryover cases from the previous regime do not contaminate the new block; balance assignment within each day so the daily arrival pattern cannot correlate with arm.
- Analyse at the randomisation unit. Aggregate to queue-block means, include queue and time-block fixed effects, and use randomisation inference or a wild cluster bootstrap rather than cluster-robust standard errors, which are anti-conservative with few clusters. Check residual autocorrelation between adjacent blocks; if it is material, widen the blocks or model it.
- Separate the two readouts by maturity. Latency and referral precision at case close are available inside the test window; matured fraud basis points require at least 120 days of dispute maturity from the transaction month, so it is a deferred confirmatory read and must not be presented as a low number on immature cohorts.
Worked solution 30 min
- Write the interference channel explicitly: reviewer capacity is fixed per queue per hour, arrivals are the sum of both arms, so control's wait is a function of treatment's assignment.
- Choose a queue-by-four-hour-block switchback across 14 queues for 30 days, giving 14 * 6 * 30 = 2,520 blocks, with within-day balanced assignment and a burn-in equal to the 95th percentile sojourn discarded at each switch.
- Compute power at the block level. With a residual block-level standard deviation of decision latency of 6 minutes after removing queue and day-of-week fixed effects, and half the blocks per arm, MDE = 2.8016 * sqrt(2 * 36 / 1260).
- State the caveat that adjacent blocks are autocorrelated, so the effective block count is below 2,520 and the analytic MDE is a floor; estimate the inflation from historical block-to-block autocorrelation before committing.
- Split the readout: latency and referral precision as the in-window primary, matured fraud basis points as a deferred read at 120 days with immature months marked incomplete rather than plotted.
Follow-up
- How do you set the block length when the queue sojourn time itself changes under treatment?
- You have 14 queues and 30 days. Compare a queue-level cluster design against a queue-day switchback on power and on what each can estimate.
- The shared resource is capacity. What happens to your estimate if reviewers work faster when the queue is long?
A single day of settled volume comes in thirty percent low
Yesterday's settled volume is 30 percent below the same weekday across the previous eight weeks, while authorization counts and approved counts for the day look normal. You have fct_payment_authorization with requested_at, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency, created_at and updated_at, plus warehouse load metadata. Decide within the hour whether this is a real drop or a load problem, and say what you would post to the on-call channel in either case.
Approach
- Check the data before the business. Compare max(updated_at) and row counts per load partition for the affected day against the prior eight same weekdays; a truncated load usually shows as a count deficit confined to a specific hour range.
- Measure null rates per column by requested_at date. Authorization fields present while settled_at and settlement_amount_minor are null points at the settlement feed; a uniform deficit across all columns points at ingestion.
- Separate late arrival from loss. Settlement lands after authorization by design, so re-measure the same day 24 and 48 hours later. A deficit that closes on the next load is lag, not a drop.
- Reconcile against the independent source: tie the day's settled total to the settlement ledger or acquirer file. If the ledger has the money and the warehouse does not, the business number was never wrong.
- Only after those four steps consider a business explanation, and then only if a segment cut produces a coherent story rather than a flat haircut spread evenly across every merchant and channel.
- Close by splitting the alert: a freshness and completeness check on the settlement feed, separate from the volume alert, so the next occurrence is classified automatically.
Follow-up
- How would you make the daily volume alert immune to settlement lag without also hiding a genuine drop?
- What is the right way to restate a published number after a backfill lands?
- Which check catches a partial load that is uniform across the day rather than concentrated in one window?
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 ↗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.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
What are your primary professional strengths and weaknesses?
What are your primary professional strengths and weaknesses?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
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?
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
What are your primary professional strengths and weaknesses?
- 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
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 Sberbank interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Sberbank. Rounds and questions reflect what candidates have reported, not a process Sberbank has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews at Sberbank?
The technical interviews range from moderate to challenging, depending on the specific team and seniority level. While the questions cover advanced topics like probability, linear algebra, and machine learning, interviewers prioritize your problem-solving process and logical approach over arriving at a memorized final answer.
PracHub interview research ↗How much preparation time should I plan for?
Most candidates benefit from 4 to 6 weeks of dedicated preparation. This allows sufficient time to review core statistical theory, practice complex SQL window functions, brush up on A/B testing principles, and rehearse behavioral interview narratives.
PracHub interview research ↗What is the company culture like for data scientists?
The culture is professional, analytical, and collaborative. Interviewers are generally polite and supportive, aiming to understand your capabilities rather than trip you up. Teams value rigorous data-driven decision-making balanced with respect for regulatory and risk constraints.
PracHub interview research ↗What is the typical timeline from initial screen to offer?
The entire process can move relatively quickly, often taking anywhere from two to four weeks from the initial recruiter chat through the final technical rounds and feedback review, provided your schedule accommodates the interview stages promptly.
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
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