A Data Scientist at Lendbuzz plays a pivotal role in disrupting the traditional auto finance industry. By leveraging alternative data, advanced machine learning, and modern underwriting models, the data science team evaluates credit risk for consumers who are underserved by traditional banks—such as immigrants or individuals with thin credit files. Your work directly impacts the core business model, determining lending decisions, interest rates, and risk management strategies in real-time.
As a Data Scientist, you will not just build models in isolation; you will deploy them to drive measurable business outcomes. The models you build and refine help Lendbuzz maintain low default rates while expanding access to credit. This requires a unique blend of predictive modeling, software engineering, and analytical intuition to solve complex, high-stakes financial problems.
Working on this team offers the fast-paced, high-impact environment of a growing fintech startup. You will collaborate closely with engineering, product, and operations teams to translate complex data into robust, automated credit underwriting pipelines. The scale of data and the immediate feedback loop of model performance make this role both highly challenging and exceptionally rewarding.
Recruiter Screen
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Rounds
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
Machine Learning Discussions
reportedAn extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.
What to demonstrate
- Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
- Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
- How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one
How to prepare
- Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
- Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
- Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.
Hiring Manager Review
reportedExpect a live problem with pieces of it missing, closer to a conversation than an exam. A metric moved, or somebody wants to know whether a change worked, and you are asked how you would find out. The manager is watching the first ninety seconds, specifically whether you establish what decision hangs on the answer before you start proposing methods. Candidates who open with a technique get steered back. Once the decision is clear, describe what the data would look like if the story were true, and say what you would accept as evidence that it is not.
What to demonstrate
- Whether you fix the decision the analysis serves before choosing an approach
- How you continue when you are told the data you just asked for does not exist
- Whether you state what would change your mind, not only what would confirm the hypothesis you started with
- How you size an effect before you have measured it
How to prepare
- Take a metric you know well and practise explaining in under two minutes the four things that could have moved it and how you would separate them
- Pick a recent launch or experiment and write the single number you would ask for first, plus what you would conclude if it came back flat
- Practise being interrupted: have someone remove a data source halfway through your answer and carry on without restarting
PracHub editorial advice for the preparation topics above.
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.
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.
Averaging per-user rates to produce a population rate
Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.
Reporting a mean for a heavy-tailed metric without saying what it hides
For spend, session length or items per order, a small fraction of units carries most of the total, so the mean has a wide standard error and one account can move it. Fix the handling before you see the result: cap or winsorise at a pre-declared percentile, and report the median or the share above a threshold next to the mean. Capping changes the estimand, so say which question the capped number answers, and check how much of any difference comes from the top 0.1 percent of units.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain Bayes' Theorem and describe how you would use it to calculate …
Explain Bayes' Theorem and describe how you would use it to calculate the probability of a borrower defaulting given specific historical indicators.
Approach
- Write down the assumption the method needs before you use the method.
- 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
- How would you explain this result to someone who does not know statistics?
- What sample size would you need to detect an effect half this size?
Explain the concept of ROC-AUC. Why is it often preferred over accurac…
Explain the concept of ROC-AUC. Why is it often preferred over accuracy for evaluating highly imbalanced datasets, such as default risk?
Approach
- Translate the result into the decision it informs, in one plain sentence.
- Write down the assumption the method needs before you use the method.
- Sanity-check the answer against a simple bound or a simulated case.
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?
Implement a basic rate-limiting algorithm using a sliding window appro…
Implement a basic rate-limiting algorithm using a sliding window approach in Python.
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.
- 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?
- How would you choose the decision threshold, and who owns that choice?
How would you design a model validation strategy when dealing with tim…
How would you design a model validation strategy when dealing with time-series or sequential loan performance data?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Set a baseline first, so any model has something honest to beat.
- 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?
Simulate false alarms in a merchant chargeback monitoring rule
Baseline matured first-chargeback rate is 12 per 10,000 settled transactions. A monitoring rule alerts when a merchant's observed monthly rate exceeds twice baseline. For monthly settled transaction counts of 500, 2,000, 10,000 and 50,000, simulate the false-alarm probability per merchant-month under the baseline, and the power to detect a merchant whose true rate is 30 per 10,000. Then, for a portfolio of 4,000 merchants split 60, 25, 10 and 5 percent across those four counts, give the expected number of false alarms per month.
Approach
- Recognise the rule is a threshold on an integer count, not on a continuous rate. At n = 500, twice baseline is 24 per 10,000, so the first observable value above it is 2 chargebacks, or 40 per 10,000. Derive the trigger count for every n before simulating anything.
- Draw binomial counts with numpy at p = 0.0012 and take the share at or above the trigger for the false-alarm rate, then repeat at p = 0.0030 for power. Use at least 200,000 draws per cell so a probability near 0.001 has a usable standard error.
- Cross-check every simulated cell against the Poisson approximation with lambda = n*p, which is tight here because p is tiny. A mismatch almost always means the trigger count is off by one.
- Weight the per-merchant false-alarm probabilities by the portfolio mix, and report the share of expected alerts contributed by each size band rather than only the total.
- Close on the operating consequence: a fixed multiplicative threshold is not a constant false-alarm rate across merchant sizes, so either the threshold scales with n or small merchants need a minimum volume before the rule applies.
Worked solution 30 min
- For each n, compute trigger = floor(2 * 0.0012 * n) + 1 and print the four values before simulating.
- Simulate 200,000 binomial draws per n at p = 0.0012 and take the share at or above the trigger.
- Repeat at p = 0.0030 and record power for the same triggers.
- Compute the Poisson tail 1 - CDF(trigger - 1, lambda = n*p) for both p values and confirm agreement within Monte Carlo error.
- Multiply the false-alarm probabilities by 2400, 1000, 400 and 200 merchants and sum.
Follow-up
- How would you set a threshold that holds the false-alarm rate roughly constant across merchant size?
- The rule reads the transaction month, but disputes arrive for up to 120 days afterwards. What does that do to the alert and how would you fix it?
- What does a month of these false alarms cost, and how would you decide whether it is worth paying?
Write a query to find the second-highest salary from an employee table…
Write a query to find the second-highest salary from an employee table without using the LIMIT clause.
Approach
- 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.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
How would you optimize a slow-running SQL query that involves multiple…
How would you optimize a slow-running SQL query that involves multiple joins on large tables?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Write a function to find the first non-repeating character in a string…
Write a function to find the first non-repeating character in a string and return its index.
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Say which table is the grain you start from, and join outward from it.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
Implement a function to merge two sorted arrays without using extra sp…
Implement a function to merge two sorted arrays without using extra space.
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 would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Collapse merchant retry chains before measuring authorization approval
Merchant retry logic turns one declined purchase into several rows in fct_payment_authorization. Collapse attempts into chains: inside a partition of (card_token_id, merchant_id, amount_minor), consecutive rows whose requested_at gap is 15 minutes or less belong to the same chain. Return one row per chain with chain_started_at, attempt_count, amount_minor, channel, and chain_outcome set to 'approved' when any attempt in the chain was approved. Then report a 7-day rolling dollar-weighted approval rate by channel over chains rather than attempts. Exclude is_reversal rows.
Approach
- In a CTE, take LAG(requested_at) OVER (PARTITION BY card_token_id, merchant_id, amount_minor ORDER BY requested_at) and set is_new_chain when the previous value is null or the gap exceeds 15 minutes.
- Turn the flag into a chain identifier with SUM(is_new_chain::int) OVER (same partition, same order, rows unbounded preceding to current row), which is the standard gaps-and-islands construction.
- Aggregate to one row per chain: MIN(requested_at), COUNT(*), and BOOL_OR(auth_result = 'approved') as the chain outcome, because the purchase succeeded if any attempt in the chain did.
- Roll the chains to a daily grain per channel, then apply the rolling window as PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW. An interval-offset RANGE frame is value-based: it admits every row in the partition whose chain_date falls in the closed window [D - 6 days, D], so a channel with no chains on some dates still gets the right seven-day sums — sparsity is exactly what this construct tolerates. ROWS BETWEEN 6 PRECEDING AND CURRENT ROW is the one that needs a dense grain, because it counts rows and on a sparse grain reaches back further than seven days.
- Left-join a dense date spine only if the output needs a row on days with no chains. That is a presentation requirement, not a correctness one, and it leaves every rolling sum unchanged. The RANGE frame's preconditions are that chain_date is a date or timestamp (an interval offset needs an ordering column it can be added to) and that the aggregation left one row per (channel, chain_date).
- Divide approved chain value by total chain value inside the rolling frame, never the average of daily rates, since averaging rates weights a quiet day the same as a heavy one.
Worked solution 40 min
- CTE 1: filter out is_reversal rows, then compute prev_requested_at with LAG and the is_new_chain flag.
- CTE 2: build chain_id with a running SUM of the flag over the same partition and order.
- CTE 3: group by partition keys plus chain_id to emit chain_started_at, attempt_count, amount_minor, channel and chain_outcome.
- CTE 4: aggregate chains to (chain_date, channel) with approved value and total value. Add the date spine here only if the report must show empty days.
- Final SELECT: two rolling SUMs over PARTITION BY channel ORDER BY chain_date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW, divided at the end.
Follow-up
- This definition chains off the previous attempt, so a chain can span an hour if attempts arrive every 14 minutes. When is that right, and when would you anchor the window to the first attempt instead?
- Two genuine purchases of the same amount at the same merchant, four minutes apart, collapse into one chain. How large is that error and how would you bound it?
- A channel goes dark for three days mid-window. Which of the two frame types would silently widen, and what would the resulting rate look like?
- What changes if the merchant retries with a slightly different amount to dodge a velocity rule?
Measure an underwriting automation programme without hiding cost
An operations programme aims to cut manual underwriting. The proposed metric is manual review rate: decided_by = 'manual' over applications with decision in ('approve','decline') in fct_loan_application. The team controls the referral rules recorded in policy_rule_hits. Explain how they can halve that metric in one release while making the book worse, then design the readout that catches it: primary metric, the cuts it must always be reported in, and the guardrails. Say what evidence you would require before declaring a batch of referrals safe to auto-decide.
Approach
- Name the exploit precisely. Referrals can be converted to automatic declines by a rule change, which removes them from the numerator at no operational cost while they stay in the denominator, so the rate falls and applicants a human would have approved are rejected instead. Auto-approving the same population moves the metric identically with the mirror-image failure mode.
- Refuse the bare rate as a primary. Make it conditional on outcomes: manual review rate measured at a held-constant approval rate within each model_pd_12m band, so a reduction is creditable only when the decisions themselves did not move.
- Require fixed cuts on every report: by model_pd_12m band and by the rule identifier inside policy_rule_hits. A single blended operations number cannot be decomposed after the fact, and the decomposition is the entire diagnostic.
- Set guardrails on the things the exploit disturbs: approval rate within each score band, the override rate where manual decisions disagree with the automated recommendation, and the vintage 90+ rate at months_on_book = 12 for the newly auto-decided population against the referrals of the prior regime.
- Fix the evidence bar for retiring a referral rule: a shadow period where the rule fires and the case is still worked manually, recording what the automation would have decided, what the human decided, and, for the disagreement group alone, the realised performance. Agreement on the easy majority establishes nothing about the disputed minority.
- State the conflict honestly rather than designing around it. The referral population exists because the model is least certain there, so automating it trades loss for cost by construction, and the readout's job is to price that trade rather than to make it disappear.
Worked solution 30 min
- Compute manual review rate for a recent month overall, then by model_pd_12m band, then by rule identifier extracted from policy_rule_hits.
- For each rule, compute the approve share and decline share of the applications it touches, so the rules whose removal would actually change decisions are identified before anyone proposes removing them.
- Simulate the exploit: reclassify one high-volume referral rule's applications as automatic declines, then recompute the blended rate and the band-level approval rates side by side.
- Write the readout spec: the primary with its conditioning, the two mandatory cuts, the three guardrails, and the shadow-period evidence bar.
Follow-up
- The override rate falls to near zero after the change. Is that good?
- How long must the shadow period run, given the outcome matures at twelve months on book?
- The rule fires on 200 applications a month. What can and cannot be established at that volume?
A dispute rate that does not fall at the right edge
Your chargeback chart slopes downward over the last four months every time it is refreshed. It is built from fct_card_dispute counted by opened_at, divided by settled transactions in the same month. Using auth_id, requested_at, dispute_stage, opened_at, network_reason_code and outcome, explain what the chart is actually showing, then specify the metric you would publish: attribution, denominator, maturity rule, and how immature periods appear on the chart. State the condition under which you would publish a development-factor estimate instead of a blank.
Approach
- Separate the two dates. A dispute belongs to the month of the transaction that caused it, which is requested_at on the linked authorization, not to the month the case was filed. Counting by opened_at mixes old transactions into new months and makes the series unreadable in both directions at once.
- Once attribution is fixed, name the censoring. Recent transaction months have not had time to accumulate their disputes, so the right edge falls by construction. The slope will still be there next month, one month further right, which is the diagnostic that distinguishes it from a real improvement.
- Set the maturity threshold empirically rather than by folklore. From completed transaction months, compute the share of the final dispute count filed by 30, 60, 90, 120 and 180 days, split by reason-code family. Consumer dispute rights commonly run around 120 days from the transaction or the expected delivery date, but several reason codes run materially longer, so one global cut-off under-covers the slow families.
- Specify the published metric: disputes reaching dispute_stage 'first_chargeback' or later, attributed to the transaction month, over settled transactions in that same month, with only months past the measured threshold shown as final.
- Decide how immature months appear. Either omit them, or show a chain-ladder style estimate with an interval in visibly different styling, never as a plain point on the same line as final months. Publish the estimate only when the development pattern is stable across recent completed cohorts; if the reason-code mix or the dispute process has shifted, the factors are estimated on a pattern that no longer applies.
- Note that the count series and the value series mature at different speeds, since net_loss_minor is signed and known only after the case resolves, so a loss-value chart is stale for longer than a case-count chart of the same data.
Follow-up
- Your development factors were estimated over a period containing an authentication policy change. What do you do?
- How do you present a month that is 60 days mature to an audience that wants a single number?
- Would you attribute a dispute to the authorization month or the settlement month, and when does the difference matter?
Approval rate rose in every band yet fell overall
Monthly application approval rate on fct_loan_application fell from 62 to 57 percent. Cut by bureau_score band, the rate rose in every band, including the null-bureau band. Columns: application_id, channel, submitted_at, requested_amount_minor, declared_annual_income_minor, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. The denominator is decision in ('approve','decline'). Explain the arithmetic, quantify how much of the five-point fall is mix versus within-band movement, and say what you would tell the team that owns acquisition.
Approach
- Confirm the paradox is real rather than a banding artefact. Rebuild the bands on fixed cutpoints taken from the earlier period, because quantile bands re-cut each month move with the population and can manufacture this pattern on their own.
- Compute the exact decomposition rather than describing it: within = sum of w_i0 * (r_i1 - r_i0), mix = sum of r_i0 * (w_i1 - w_i0), interaction = sum of (w_i1 - w_i0) * (r_i1 - r_i0). The three terms sum identically to the change in the blended rate, so the report can state the split.
- Attribute the weight change by cutting the same fixed bands by channel. Keep null bureau_score as its own band; a thin file is a population signal, not missing data to be imputed away.
- Check whether the new arrivals also changed the population inside a band, by comparing requested_amount_minor and declared_annual_income_minor distributions within one band across the two months.
- Deliver two numbers rather than one story: policy is looser in every band, and the funnel is being fed a different population. Those have different owners and different fixes.
Follow-up
- If the new channel is profitable at its own approval rate, is the blended fall a problem at all?
- How would you present this so that nobody reads the blended series unaccompanied again?
- What breaks if you fix the mix by reweighting to a frozen band distribution every month?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Saying no well is a senior skill and it is rarely rehearsed. Think of a time you told someone their analysis was not worth doing, or that the experiment could not answer their question at the sample size available. Explain what you offered instead. Refusal without an alternative reads as obstruction rather than judgement.
Recommend a decision whose true outcome matures a year later
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
Approach
- Fix the readout before the rollout, because a readout chosen after the data arrives is a story rather than a decision rule: indicator, window, threshold and reversal condition all go in writing first.
- Choose the leading indicator on its measured relationship to the matured outcome in historical vintages rather than on availability. Early delinquency, typically the share reaching dpd_1_29 or missing a first scheduled payment by months_on_book 3, is the usual candidate, and you quantify how well it predicted the 12-month rate across past cohorts.
- State the bias and its direction plainly: early delinquency under-represents default that emerges later and is contaminated by servicing and payment-date effects, so treat it as a floor on risk rather than an estimate of it.
- Buy identification where full randomisation is unavailable: a narrow randomised approval band around the cutoff, or a staged rollout by channel or region read as a difference-in-differences, with the parallel-trends assumption stated and checked in the pre-period rather than assumed.
- Give the executive the binary they asked for with the trigger attached in the same sentence: yes, conditional on the month-3 indicator staying inside a stated band, with an automatic hold if it breaches.
Follow-up
- How would you validate that the month-3 indicator predicts the 12-month outcome, and what evidence would invalidate it mid-rollout?
- Compliance refuses a randomised band. What is your next-best identification strategy, and what precision do you lose by taking it?
State honestly what your cutoff change actually contributed
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
Approach
- Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
- Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
- Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
- Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
- State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.
Follow-up
- The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
- What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?
Allocate one analyst-week across three competing risk requests
Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.
Approach
- Score each request on the decision it unblocks rather than on effort or on how loudly it arrived: what changes if it is late, and is that change reversible.
- Separate deadline from value. The nine-day renewal is a hard, irreversible date with a bounded prize; the six-week cutoff has slack but a much larger downside if it ships unmeasured; the reserving number has no date but feeds external reporting, which is its own kind of hard.
- Hunt for the cheap partial in each: a decline teardown restricted to the top merchants by declined value usually answers the contract question at a fraction of the full cut.
- Sequence by hard date first, then by largest irreversible downside, and deliver the trade-off to all three sponsors in one message rather than three, so nobody negotiates privately against a version you told someone else.
- Name what is dropped and who now owns that consequence, in writing, so the trade-off is visible rather than silently absorbed by you.
Follow-up
- The credit sponsor escalates to your manager. What do you change, and what do you refuse to change?
- How would you make this allocation reproducible so the next contested week is a rule application rather than a negotiation?
- 01
An underwriting rule change must be decided in six weeks. Its real outcome, the vintage 90-plus rate at months_on_book 12 in fct_loan_performance_monthly, matures in a year. The executive wants a yes or no, not a range. Randomising the credit decision across the whole population is not available. Name the leading indicator you would accept, state its bias and the direction of that bias, define the decision rule and stopping condition before any rollout starts, and say what reading would make you recommend reversing the change.
- 02
Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.
- 03
Three requests land in the same week and you have one analyst-week. Payments wants a merchant-level decline teardown before a contract renewal in nine days. Credit wants a swap-set analysis on a cutoff change scheduled to ship in six weeks. Insurance wants accident-quarter loss ratios at 12 months development for a reserving review with no fixed date. Each sponsor believes theirs is first, and each has escalated before. Produce the allocation, the reasoning you would say out loud to all three at once, and what you explicitly drop.
Is this an official Lendbuzz interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Lendbuzz. Rounds and questions reflect what candidates have reported, not a process Lendbuzz has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical is the interview process compared to other data science roles?
The process is highly technical and skews heavily toward software engineering and coding. You should expect multiple live coding rounds in Python (testing data structures and algorithms) and SQL, in addition to core machine learning discussions.
PracHub interview research ↗What is the company culture like for the data science team?
The culture is fast-paced, collaborative, and highly data-driven, characteristic of a successful fintech startup. The team values direct impact, technical autonomy, and practical problem-solving over theoretical research.
PracHub interview research ↗How much preparation time is recommended?
It is highly recommended to spend 2 to 3 weeks preparing. Focus on practicing medium-level Python coding challenges, brushing up on SQL window functions, and reviewing the mathematical foundations of the machine learning algorithms on your resume.
PracHub interview research ↗Does Lendbuzz support remote work for this position?
While policies can vary by team and location, Lendbuzz has a strong presence in Boston, MA, and many of their core technical teams operate on a hybrid schedule. It is best to clarify current location expectations with your recruiter during the initial call.
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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