As a Data Scientist at PayU, you sit at the intersection of high-frequency financial transactions, complex fraud detection, and credit risk modeling. Your work directly impacts how millions of users move money safely across global markets. By leveraging vast datasets, you design and deploy AI-driven solutions that balance seamless user experiences with the rigorous security requirements of a world-class payment gateway.
This role is critical to the business because every decision—from approving a high-risk credit application to identifying a fraudulent pattern in milliseconds—requires precise, scalable, and explainable machine learning models. You will work within a high-stakes environment where the quality of your insights directly correlates to the company’s bottom line and operational stability. If you thrive on solving complex, real-world problems at scale, this position offers a unique vantage point into the future of global fintech.
Technical Screening
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Case Studies
reportedA case has a fixed clock, and a good deal of what is being scored is how you spend it. Thirty to forty-five minutes buys one pass across the whole problem or a deep read of one part of it, and choosing between those is the work rather than a compromise forced on you. Announce the shape early: the structure you are using, the branch you think carries the decision, and what you are setting aside. An answer that is thorough for the first third and silent on the recommendation reads worse than one that is rougher throughout and lands.
What to demonstrate
- Whether a visible structure appears in the opening minutes and survives the rest of the case
- Whether the depth goes to the branch that carries the decision, rather than the branch you find most comfortable
- Whether you say what you are leaving out and why, instead of quietly omitting it and hoping nobody asks
How to prepare
- After each practice case, write down the branches you chose not to open and the reason for each, then check whether you said any of them out loud while the case was running. A branch you only cut privately reads to the interviewer as one you missed.
- Redo a case you have already worked in half the time, deciding in advance which single branch you keep, then compare which version a listener would find more useful.
- Write a two-sentence opening you can reuse, holding the restated question and your plan for the available time, and deliver it within the first ninety seconds of every practice run.
Team Interaction
reportedAn added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.
What to demonstrate
- Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
- Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
- How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact
How to prepare
- Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
- For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
- Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.
PracHub editorial advice for the preparation topics above.
Recalibrating an underwriting cutoff on approved and funded applicants only
Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.
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.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Reading a dozen metrics with no multiplicity control
Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given a deck of cards, what is the probability of drawing a specific s…
Given a deck of cards, what is the probability of drawing a specific sequence?
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Say what the estimate is of, and over what population it generalises.
- 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?
- How would you explain this result to someone who does not know statistics?
Describe the central limit theorem and its practical application in yo…
Describe the central limit theorem and its practical application in your previous projects.
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
- Write down the assumption the method needs before you use the method.
Follow-up
- Which assumption here is most likely to be violated in practice?
- How would you explain this result to someone who does not know statistics?
How would you calculate the probability of a specific transaction sequ…
How would you calculate the probability of a specific transaction sequence occurring?
Approach
- Say what the estimate is of, and over what population it generalises.
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Translate the result into the decision it informs, in one plain sentence.
Follow-up
- How would you explain this result to someone who does not know statistics?
- Which assumption here is most likely to be violated in practice?
Explain the difference between Type I and Type II errors in the contex…
Explain the difference between Type I and Type II errors in the context of fraud detection.
Approach
- Quantify uncertainty explicitly rather than reporting a point estimate alone.
- Say what the estimate is of, and over what population it generalises.
- Sanity-check the answer against a simple bound or a simulated case.
Follow-up
- What sample size would you need to detect an effect half this size?
- How would you explain this result to someone who does not know statistics?
How do you address overfitting in tree-based models?
How do you address overfitting in tree-based models?
Approach
- Say how the offline result would be validated online before it is trusted.
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Explain the internal mechanics of a Random Forest versus a Gradient Bo…
Explain the internal mechanics of a Random Forest versus a Gradient Boosting machine.
Approach
- Set a baseline first, so any model has something honest to beat.
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
Implement accident-quarter loss ratio at twelve months development
fct_policy_period_monthly arrives as a stack of month-end snapshots: each row carries valuation_month alongside as_of_month, policy_id, product_line, written_premium_minor, earned_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor. Compute the accident-quarter loss ratio at exactly 12 months of development: incurred losses over earned premium, both taken from rows whose as_of_month falls in the accident quarter, read from the snapshot 12 months after that quarter closes. Report quarters that cannot reach that age as incomplete rather than dropping them.
Approach
- Derive accident_quarter from as_of_month, then define the evaluation snapshot per quarter as valuation_month equal to the quarter's final month plus twelve months. Every figure in the ratio comes from that one snapshot, not from whichever snapshot happens to be newest.
- Numerator is paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor over the accident quarter's rows in that snapshot. Loss adjustment expense may be included or not, but the choice applies to every quarter and is named in an output column.
- Denominator is earned_premium_minor over the same rows. Written premium is booked in full at inception, so in a growing book it runs ahead of earned premium and drags the ratio down, with the error reversing when the book shrinks.
- Left-join the full quarter list against available valuation months so a quarter with no 12-month snapshot yields status incomplete and a null ratio, instead of disappearing and shortening the series without saying so.
- Split by product_line, since both the loss ratio level and the speed of development differ by line, and a blended series moves with mix as much as with experience.
Worked solution 30 min
- Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
- Filter rows to those where valuation_month equals the row's target_valuation, then assert each accident_quarter has exactly one distinct valuation_month left.
- Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
- Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
- Recompute one quarter by hand on a five-policy subset and confirm it matches.
Follow-up
- The most recent complete quarter came in four points better than the one before. What do you check before calling it an improvement?
- How would you estimate the 12-month figure for a quarter that is only 6 months developed, and how would you label the estimate?
- Why can an expense ratio legitimately use a different denominator from the loss ratio in the same presentation?
Attribute chargebacks to transaction month without fanning out
Report the matured first-chargeback rate. Numerator: fct_card_dispute rows whose dispute_stage is first_chargeback, representment, pre_arbitration or arbitration, attributed to the requested_at month of the linked fct_payment_authorization row rather than to opened_at. Denominator: settled authorizations in that same transaction month, meaning settled_at is not null and is_reversal is false. One authorization can carry more than one dispute case. Report only transaction months whose last day is at least 120 days old, and mark the remainder incomplete rather than showing them as low.
Approach
- Compute the denominator from fct_payment_authorization alone, grouped by the requested_at month, before any dispute table is mentioned, so the join cannot touch it.
- Compute the numerator as a separate aggregate: join disputes to their authorization only to recover requested_at, then count dispute rows per transaction month with the stage filter.
- Join the two aggregates on the month key, which keeps the grain at one row per month and makes double counting structurally impossible rather than something you have to remember.
- Apply the maturity gate on the month's last day plus 120 days against the current date, because a transaction on the final day of the month is the least mature one in the cohort.
- Emit an explicit status column of 'final' or 'incomplete' instead of filtering immature months away silently, so a reader cannot mistake absence for zero.
Worked solution 30 min
- CTE denom: SELECT date_trunc('month', requested_at) AS txn_month, COUNT(*) AS settled_txns FROM fct_payment_authorization WHERE settled_at IS NOT NULL AND NOT is_reversal GROUP BY 1.
- CTE numer: join fct_card_dispute to fct_payment_authorization on auth_id, filter dispute_stage to the four stages at or beyond first_chargeback, group by the same txn_month expression, count dispute rows.
- LEFT JOIN numer onto denom on txn_month so months with zero disputes appear as zero rather than disappearing.
- Derive the rate with a numeric cast and a status column driven by (txn_month + interval '1 month - 1 day') + interval '120 days' <= current_date.
Follow-up
- Should the numerator count dispute cases or disputed authorizations? Which one reconciles to the loss line, and which one to the operations queue?
- Some reason codes allow filing well beyond 120 days. How would you estimate the tail on a month you have decided to call final?
- How would you present the incomplete months so that a weekly reader does not read the right-hand slope as an improvement?
Vintage ninety-plus rate at twelve months on book
From fct_loan_performance_monthly, build a vintage table keyed on origination_month: the share of each cohort that ever reached days_past_due of 90 or more, or charge_off_flag = true, at or before months_on_book = 12. Restructuring resets days_past_due, so for any loan with restructured_flag true, evaluate only the month ends strictly before its first restructured month. Return origination_month, loans_funded, bad_loans and bad_rate. Exclude any cohort that does not yet have a months_on_book = 12 observation for every loan still on book.
Approach
- Establish the cohort denominator from the first month end each loan appears at, months_on_book = 0, so a loan is counted once in its origination_month rather than once per monthly row.
- Find each loan's first restructured month with MIN(as_of_month_end) FILTER (WHERE restructured_flag) OVER (PARTITION BY loan_id), or the equivalent grouped subquery, and keep it null for loans never restructured.
- Flag a loan bad if any row with months_on_book <= 12 and as_of_month_end earlier than that first restructured month has days_past_due >= 90 or charge_off_flag = true, which is what 'pre-restructure worst state' means in practice.
- Gate maturity by requiring the cohort's newest month end to be at least 12 months after origination_month, and report immature cohorts as incomplete rather than letting them appear at a flattering low rate.
- Aggregate to one row per origination_month and read the column downward, not across calendar time, because the whole point is comparing cohorts at equal age.
Follow-up
- Should a restructure inside 12 months count as bad in its own right? Argue both sides and say what you would actually ship.
- A loan that prepaid in full at month 4 never had a chance to go 90 days past due. In or out of the denominator, and why?
- The 2025-11 cohort is two points worse at month 12 than its neighbours. What three queries do you run before you call it a credit-quality change?
You have a limited budget for a marketing campaign; how do you estimat…
You have a limited budget for a marketing campaign; how do you estimate the potential ROI?
Approach
- Work from the decision backwards to the evidence you would need.
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Define an active customer for a card portfolio
Leadership wants one weekly number for how many customers are active. You have fct_payment_authorization (auth_id, customer_id, auth_result, captured_at, amount_minor, is_reversal, channel) and dim_customer (customer_id, is_current, kyc_status, onboarded_at, closed_at). Write the metric definition you would publish: numerator, denominator, window, and every exclusion with its reason. Then name two ways the definition gives a wrong read, one that inflates it and one that deflates it, and say what you would report alongside it.
Approach
- Fix the grain before writing anything. The number counts distinct customers, so the authorization table has to be reduced to one row per customer_id first; a customer with forty approvals counts once.
- Build the numerator from money that actually moved: auth_result = 'approved' AND captured_at IS NOT NULL, excluding is_reversal = true and zero-amount verification authorizations. An approval that is never captured, or is reversed, has no economic content behind it.
- Build the denominator from the population that could have transacted: dim_customer with is_current = true, kyc_status = 'verified', onboarded_at on or before the window start, and closed_at null or after the window start. Joining without is_current fans the type 2 history out and multiplies the denominator by the number of attribute versions.
- State the window and its recompute cadence, trailing 30 days recomputed daily, and say plainly that this series is not comparable to a calendar-month version of the same definition.
- Name the inflation path (recurring and subscription-only customers, and wallet top-ups, count as active with no user intent behind them) and the deflation path (a customer onboarded inside the window had less than 30 days of opportunity, and activity on products outside this table is invisible).
- Pair it with a depth metric such as settled volume per active customer, so breadth cannot be reported on its own and an acquisition push cannot pass as engagement.
Worked solution 20 min
- Write the denominator query first and record both the raw dim_customer row count and the distinct customer_id count; their ratio is the fan-out factor the is_current filter removes.
- Write the numerator as a distinct customer_id count over the trailing 30 days, applying the three exclusions one at a time and recording the count after each.
- Compute the rate, then recompute with zero-amount verifications left in, and record the difference in basis points.
- Write the two failure modes as one sentence each, naming the customer population affected by each.
Follow-up
- A product team proposes counting any successful login as activity. What breaks?
- The number jumps four percent overnight with no product change. What do you check first?
- How would you report customers onboarded inside the window, given they had a shorter opportunity to transact?
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?
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 ↗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 ↗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.
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.
How do you handle missing data in a statistical model?
How do you handle missing data in a statistical model?
Approach
- Close with what you would do differently, concretely.
- Pick a story where you drove the decision, not one where you observed it.
- 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?
- How did you know the outcome was caused by your change?
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?
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
How do you handle missing data in a statistical model?
- 02
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.
- 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 PayU interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at PayU. Rounds and questions reflect what candidates have reported, not a process PayU 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?
Dedicate at least two to four weeks to reviewing core machine learning concepts, practicing SQL queries, and working through probability puzzles. Consistency is more effective than last-minute cramming.
PracHub interview research ↗What differentiates a good candidate from a great one at PayU?
A great candidate doesn't just solve the problem; they communicate their thought process clearly, consider edge cases, and tie their solution back to the business impact.
PracHub interview research ↗Is there a specific style of coding I should use?
Focus on writing clean, efficient, and well-documented code. Since you may be asked to discuss your code in depth, clarity is as important as correctness.
PracHub interview research ↗How should I handle the guesstimate questions?
Do not rush to a final number. The interviewer is looking for your logic, your assumptions, and your ability to structure the problem. It is perfectly acceptable to state your assumptions clearly before you begin the math.
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.