Bgb Group · Data Scientist
Updated · 2026-09-22

Bgb Group Data Scientist
Interview Questions & Guide 2026

THE 60-SECOND BRIEF

The role of a Data Scientist at Bgb Group is crucial in transforming vast amounts of data into actionable insights that drive strategic decision-making. As a key member of our data team, you will harness advanced analytical techniques and machine learning methodologies to enhance our products, optimize user experiences, and support business growth. This role involves collaborating closely with cross-functional teams, including engineering, product management, and marketing, to develop solutions that are not only innovative but also grounded in data.

A large share of questions open as "how would you measure X", where the real work is choosing the metric, fixing its denominator, and defining the population it applies to. Any computation comes last and is frequently not required at all.

Bgb Group candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Separate authorization, settlement and dispute outcomes cleanlyReconcile amounts in minor units and currencySet fraud thresholds by expected cost

32 min read

Browse Data Scientist questions

See the practice prompts

16Practice promptsAcross five skill areas
3With worked solutionsIncluded in the practice prompts

The role of a Data Scientist at Bgb Group is crucial in transforming vast amounts of data into actionable insights that drive strategic decision-making. As a key member of our data team, you will harness advanced analytical techniques and machine learning methodologies to enhance our products, optimize user experiences, and support business growth. This role involves collaborating closely with cross-functional teams, including engineering, product management, and marketing, to develop solutions that are not only innovative but also grounded in data.

You will impact various products and initiatives within Bgb Group, from consumer analytics to operational efficiency projects. The complexity and scale of the data challenges you will face are significant, giving you the opportunity to influence the direction of our services and enhance our market competitiveness. Expect to work on exciting and complex problems that require both technical expertise and creative thinking, making your contributions critical to our success.

01

Initial Screening

reported

Data Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.

What to demonstrate

  • Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
  • Whether you name what you have not done instead of stretching to cover every line of the posting
  • Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage

How to prepare

  • Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
  • Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
  • Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
PracHub interview research ↗
02

Technical Assessment

reported

Before 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
PracHub interview research ↗
03

Behavioral Interview

reported

Behavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.

What to demonstrate

  • Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
  • Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
  • Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier

How to prepare

  • For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
  • Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
  • Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
PracHub interview research ↗

PracHub editorial advice for the preparation topics above.

01

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

02

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.

03

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

04

SQL that silently fans out on a one-to-many join

State the grain of each table and the grain you want in the result before writing the join. Pre-aggregate the many side to the join key, or use EXISTS or a window function, and verify with a row count against COUNT(DISTINCT id) rather than trusting that the numbers look plausible.

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

13 technical prompts3 include a worked solution

Write a function to calculate the mean and standard deviation of a lis…

medium
statistics and probability

Write a function to calculate the mean and standard deviation of a list of numbers.

Approach
  1. Say what the estimate is of, and over what population it generalises.
  2. Write down the assumption the method needs before you use the method.
  3. Quantify uncertainty explicitly rather than reporting a point estimate alone.
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?

What are some common metrics used to evaluate classification models?

medium
machine learning and modelling

What are some common metrics used to evaluate classification models?

Approach
  1. Check what information would not exist at prediction time, and exclude it.
  2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  3. 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?

Describe the importance of feature engineering in a machine learning m…

medium
machine learning and modelling

Describe the importance of feature engineering in a machine learning model.

Approach
  1. Say how the offline result would be validated online before it is trusted.
  2. Frame the prediction: the label, the moment of prediction, and the action it triggers.
  3. Check what information would not exist at prediction time, and exclude it.
Follow-up
  • Where could label leakage enter this setup?
  • What would you monitor after launch to know the model is still valid?

Implement accident-quarter loss ratio at twelve months development

mediumWorked solution
loss ratiodevelopment ageearned premium

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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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
  1. Add accident_quarter and a target_valuation column equal to the quarter end plus twelve months.
  2. 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.
  3. Aggregate incurred and earned premium by accident_quarter and product_line and take the ratio.
  4. Reindex against the full list of accident quarters and product lines, marking rows with no matching snapshot as incomplete with a null ratio.
  5. Recompute one quarter by hand on a five-policy subset and confirm it matches.
EXPECTED RESULTOne row per accident_quarter and product_line with incurred, earned_premium, loss_ratio, an lae_included flag and status in complete or incomplete. Complete quarters end twelve months before the latest valuation_month, so the four or five most recent quarters carry null ratios.
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?

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.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Fix 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 ↗
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.

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

Tell me about a time you faced a significant obstacle while working on…

medium
behavioural and stakeholder questions

Tell me about a time you faced a significant obstacle while working on a project. How did you overcome it?

Approach
  1. Close with what you would do differently, concretely.
  2. Quantify the outcome, including what you would not claim credit for.
  3. Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
  • What would you do differently if you ran that project again?
  • What did you decide not to do, and why?

Retract a published number after finding a currency bug

medium
error disclosureminor unitsprocess repair

Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

Approach
  1. Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
  2. Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
  3. Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
  4. Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
  5. Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.
Follow-up
  • The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
  • What automated check would have caught this, where would it live, and what would it cost in false alarms?

Allocate one analyst-week across three competing risk requests

medium
prioritisationdecision deadlinesstakeholder negotiation

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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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 time you faced a significant obstacle while working on a project. How did you overcome it?

  • 02

    Two weeks ago you published an interchange and fraud analysis that summed amount_minor across fct_payment_authorization without converting currencies. Minor units are not two decimals everywhere: some currencies carry none and some carry three, so the sum has no interpretation. A pricing decision is already in flight on the back of it. You now have corrected figures. Produce the retraction: what you send, to whom, in what order, and what you change in the process so this class of error is caught next time rather than trusted next time.

  • 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.

PracHub interview preparation framework ↗
Is this an official Bgb Group interview guide?

No. It is PracHub's own research and practice material for the Data Scientist role at Bgb Group. Rounds and questions reflect what candidates have reported, not a process Bgb Group has published, and they change over time. Confirm the current format and scope with your recruiter.

PracHub interview research ↗
How difficult are the interviews at Bgb Group?

The interviews can be challenging, focusing on both technical expertise and cultural fit. Candidates typically find that preparation in both areas is crucial to success.

PracHub interview research ↗
What differentiates successful candidates?

Successful candidates often demonstrate a strong combination of technical skills, problem-solving ability, and effective communication. They also align well with the company’s values and culture.

PracHub interview research ↗
What is the typical timeline from initial screen to offer?

The timeline can vary but generally ranges from a few weeks to over a month, depending on scheduling and the number of interview stages.

PracHub interview research ↗
Is remote work an option for this role?

Yes, Bgb Group offers flexible work arrangements, including options for remote and hybrid work, depending on team requirements.

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.