A Data Scientist at Radial plays a pivotal role in harnessing data to drive strategic decisions and enhance operational efficiency. This position is essential for transforming complex datasets into actionable insights that directly influence product development, customer experiences, and business strategies. By leveraging advanced analytical techniques and machine learning algorithms, you will contribute to optimizing processes that enhance the overall performance of Radial’s services.
In this role, you will collaborate with cross-functional teams, including engineering, product management, and operations, to tackle real-world challenges. The breadth of data you will engage with—from customer interactions to inventory management—provides a unique opportunity to make a significant impact on the business. Expect to work on high-stakes projects that not only require technical expertise but also strategic thinking and innovative problem-solving. As a Data Scientist, you will be at the forefront of strategic initiatives that shape the future of Radial and its offerings.
HR Screening
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Technical Assessment
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Manager Interviews
reportedUnderneath the questions about your past work sits a resourcing question. Given four things worth doing and one of you, which gets done and what happens to the rest? Managers ask because that is the daily texture of the job, and because the answer shows whether you rank work by effort or by what it changes. The weak version sorts by personal interest or by whoever asked most insistently. The strong version ties each candidate piece of work to a decision somebody downstream is waiting on, and then names the one you would drop and who you would tell.
What to demonstrate
- Whether you rank work by the decision it unblocks or by how interesting the method is
- How you describe a request you declined, and whether you can say who you said it to
- Whether your sense of how long something takes survives one follow-up question about the messy part
- How you decide something is good enough to hand over unfinished
How to prepare
- Write out your current queue and, next to each item, the decision that stays stalled until it lands. Anything with no waiting decision becomes your example of work you would cut
- Rehearse turning down a plausible stakeholder request out loud, including the smaller alternative you offered instead
- Have one case where you shipped a rough answer early and one where you refused to, with the reason that separated them
Technical Expert Interviews
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
PracHub editorial advice for the preparation topics above.
Averaging delinquency across a book that is growing
A loan three months old cannot be 90 days past due, so a portfolio with many recent originations reports a low blended 90+ rate purely from age mix. The blended rate falls fastest exactly when originations grow fastest, which is precisely when credit quality most needs watching, so the metric moves in the reassuring direction during the riskiest period. Only comparisons at equal months on book are valid, which is what a vintage or roll-rate view enforces.
Reading the most recent months of fraud and dispute rates as final
Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, and several reason codes run considerably longer, so the disputes belonging to a recent transaction month have simply not been filed yet. Any chart attributed by transaction date therefore slopes down at the right edge regardless of what is happening. The fix is to report only matured cohorts, or to apply development factors estimated from completed months and to show the estimate as an estimate.
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.
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.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Describe a machine learning model you have implemented and the results…
Describe a machine learning model you have implemented and the results it produced.
Approach
- Check what information would not exist at prediction time, and exclude it.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- 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?
How do you evaluate the performance of a classification model?
How do you evaluate the performance of a classification model?
Approach
- Say how the offline result would be validated online before it is trusted.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Estimate a delinquency roll-rate matrix and project twelve months
fct_loan_performance_monthly gives loan_id, as_of_month_end, months_on_book, delinquency_bucket, charge_off_flag, prepaid_in_full_flag and restructured_flag. Build a month-to-month transition matrix over the five delinquency buckets plus absorbing charged_off and prepaid states. Loans that stop appearing must be routed to an absorbing state rather than dropped. Project the current book forward 12 months by repeated matrix multiplication and report the projected share reaching charge-off. Handle restructured_flag explicitly, and name one place the Markov assumption fails on this data.
Approach
- Build consecutive month pairs per loan by shifting as_of_month_end within loan_id, then verify the shifted value is exactly one month later. A gap is not a transition, it is an exit you have not resolved yet.
- Resolve exits before counting anything. A loan whose last row carries charge_off_flag moves to charged_off, one carrying prepaid_in_full_flag moves to prepaid, and one that disappears with neither is a data question to raise rather than silently discard, because discarding it is survivorship that inflates every cure rate.
- Count pairs into a 7 by 7 matrix and row-normalise. Assert every row sums to one and the two absorbing rows are the identity; a row that does not sum to one means exits were dropped.
- Decide and state the restructure rule. Restructuring resets days_past_due, so a dpd_60_89 to current move on a restructured loan is not a cure. Either give restructured loans their own state or carry the pre-restructure bucket, but do not let that move land in the cure cell.
- Project by taking the current bucket distribution as a row vector and multiplying by the matrix twelve times. Report the charged_off entry, and report it again from an all-current starting vector so the reader can see how much of the projection comes from loans that are already delinquent today.
- State the homogeneity failure plainly: transition rates depend strongly on months_on_book, so one pooled matrix applied to a book with a young mix understates early-life delinquency. If the mix is moving, estimate separate matrices by seasoning band.
Worked solution 45 min
- Sort by loan_id and as_of_month_end, shift to form (from_state, to_state) pairs, and flag pairs whose month gap is not exactly one.
- For each loan's final row, assign the absorbing destination from charge_off_flag or prepaid_in_full_flag, and list loans that vanish with neither as an exception count to report.
- Apply the restructure rule, then build the 7 by 7 count matrix with a cross-tabulation over ordered state categories and row-normalise it.
- Assert row sums equal one and absorbing rows are the identity, then take the current month's bucket distribution as a row vector.
- Multiply twelve times, report the charged_off component, and repeat from an all-current vector for comparison.
Follow-up
- How would you validate the projection against what actually happened, and over what window?
- The cure rate out of dpd_30_59 rose five points last quarter. What are the candidate explanations and how would you separate them?
- When would you prefer a vintage curve to a roll-rate projection, and why?
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.
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?
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?
How do you prioritize competing projects and manage your time effectiv…
How do you prioritize competing projects and manage your time effectively?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
If you were tasked with reducing customer churn, what data would you a…
If you were tasked with reducing customer churn, what data would you analyze?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Fix the population and the time window before naming any metric.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
Given a dataset, how would you approach identifying trends?
Given a dataset, how would you approach identifying trends?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you design an experiment to test a new feature in an e-comme…
How would you design an experiment to test a new feature in an e-commerce application?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Decide the analysis before seeing data, including how long it runs and when you look.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
What is the difference between union and union all?
What is the difference between union and union all?
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the decision backwards to the evidence you would need.
- State your assumptions explicitly before working the problem.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Build the metric tree for a new instalment loan
A new instalment loan product launches next quarter. Build the metric tree from fct_loan_application and fct_loan_performance_monthly: one primary metric, the intermediate metrics that explain it, and the guardrails. The product owner wants a go or no-go call six weeks after the first disbursements, while the primary outcome is defined at twelve months on book. Name the leading indicator you would accept for the six-week call, state its bias in one sentence, and say what you would refuse to conclude from it.
Approach
- Put the primary where the trade is real: risk-adjusted margin per 1,000 units of principal originated, evaluated at months_on_book = 12, charging funding cost at the internal transfer rate. Approval rate and origination volume can both be moved in a week by lowering the cutoff, and neither charges anything for the consequence.
- Lay the tree out by layer so a movement can be localised: approval rate on a decision-reaching denominator (decision in ('approve','decline'), excluding withdrawn and expired) feeds offer-to-funded conversion within 30 days by offered_apr_bps band, which feeds the vintage 90+ rate at months_on_book = 12, which feeds risk-adjusted yield on average balances. Each layer's denominator is the numerator of the layer above.
- Choose guardrails that catch the opposite failure to the primary's. A margin target is trivially met by shrinking the book to its safest slice, so report approval rate within each model_pd_12m band, where that collapse shows up first, alongside adverse-impact monitoring of the decision rule under compliance review.
- For the six-week call, accept first-payment behaviour: the share of funded loans missing the first scheduled payment, or reaching days_past_due at or above 30 by months_on_book = 3. Estimate its relationship to the 12-month 90+ rate from completed vintages of the nearest comparable product, and report the dispersion of that ratio across those vintages as the uncertainty rather than quoting a point estimate.
- State the bias in one sentence: early default is loaded toward application fraud and income misstatement and is close to blind to affordability deterioration that develops over months, which is the failure mode a new product with a new population is most likely to have, and the historical ratio breaks exactly when the population or the macro environment changes.
- Say what six weeks cannot answer: nothing about the 12-month level in absolute terms, and nothing whatsoever about the applicants the policy declined.
Worked solution 35 min
- Write each metric in the tree as numerator, denominator, exclusions and window, four lines each, before writing any SQL.
- Build a vintage table from fct_loan_performance_monthly with origination_month as rows and months_on_book as columns for an existing comparable product, and read the diagonal as calendar time.
- For the last eight to twelve complete vintages, compute both the 30+ rate at months_on_book = 3 and the 90+ rate at months_on_book = 12, take the ratio, and record its minimum, median and maximum.
- Write the six-week readout using that ratio range as an interval, with the one-sentence bias statement attached to the number itself rather than to a footnote.
Follow-up
- The first vintage looks excellent at three months on book. What would make you distrust it?
- How do restructured loans enter your twelve-month numerator?
- Which metric in this tree would you refuse to put on a weekly dashboard, and why?
Fraud losses appear to halve in recent transaction months
A weekly chart attributes fct_card_dispute cases to the requested_at month of the linked fct_payment_authorization row. The two most recent months show the first-chargeback rate falling by half, and a risk rule shipped six weeks ago. Columns: dispute_id, auth_id, dispute_category, dispute_stage, opened_at, disputed_amount_minor, liability_shift_flag, outcome, net_loss_minor, resolved_at. Decide whether the rule worked, and produce the version of the chart you would sign your name to.
Approach
- Separate the two dates explicitly. opened_at is when a case was filed, requested_at is when the transaction happened. Attributing by transaction month is the right causal choice and is exactly what makes the newest months structurally incomplete.
- Measure the filing lag rather than assuming it: the distribution of opened_at minus requested_at over fully developed months, split by dispute_category, and the age at which around 95 percent of cases have arrived.
- Build a development triangle of transaction month by months of development on cumulative case counts, and estimate age-to-age factors from the columns that are complete.
- Develop the immature months with those factors and plot the result as an estimate with a visible band, kept visually distinct from the matured series rather than blended into it.
- State the assumption the method needs: a stable development pattern across cohorts. A change in filing behaviour, merchant mix or the dispute team's own backlog breaks it, so inspect factor stability down each column before relying on the estimate.
- Only then evaluate the rule, comparing pre-change and post-change cohorts at equal development age.
Follow-up
- What leading indicator would you accept while the cohort matures, and what is its known bias?
- How does liability_shift_flag change which disputes you should expect to see in the first place?
- If the rule also blocked good transactions, where does that cost appear, and is any of it in this chart?
For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design one test end to end on paper
- Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
- Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
- State in advance what you will do if the primary metric is flat while a secondary metric is significant.
Deliverable: A one-page test design with a decision rule written before launch.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power arithmetic until it is automatic
- Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
- Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
- Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.
Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.
Practice prompt ↗Practice prompt ↗03Variance and the unit-of-analysis problem
- Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
- Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
- Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.
Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.
Practice prompt ↗Practice prompt ↗04Validity threats you can actually test for
- Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
- Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
- Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.
Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When randomization is not available
- Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
- Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
- List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.
Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.
Practice prompt ↗Practice prompt ↗06The readout query
- Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
- Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
- Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.
Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.
Practice prompt ↗Practice prompt ↗07Present it to someone who will not read the appendix
- Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
- Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
- Rewrite your opening line so the recommendation lands before any methodology.
Deliverable: A one-page readout whose first line is the recommendation.
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.
How do you handle feedback on your analyses or models?
How do you handle feedback on your analyses or models?
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Pick a story where you drove the decision, not one where you observed it.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Give an example of a challenging problem you faced and how you overcam…
Give an example of a challenging problem you faced and how you overcame it.
Approach
- Pick a story where you drove the decision, not one where you observed it.
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Explain an incomplete dispute chart to a non-technical executive
A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.
Approach
- Lead with the mechanism in the listener's own terms, not with the statistical name for it: a dispute is attributed to the month the transaction happened, but it can be filed up to roughly 120 days later, so recent months contain only the disputes filed so far.
- Show completeness rather than arguing about the rate: for each transaction month, plot the share of its eventual disputes already filed, estimated from months that are fully matured. The last three months will sit visibly below 100 percent.
- Replace the chart with two artefacts: a matured series that stops 120 days back and is labelled final, and a development-factor estimate for the immature months drawn as a dashed range and labelled an estimate.
- Hand over one sentence the executive can repeat without you in the room: the recent months look better because the disputes have not arrived yet, not because fewer will arrive.
- Offer a weekly signal they can watch instead, such as the risk-score mix of approved volume or the decline-rule hit rate, and state up front what it does and does not predict.
Follow-up
- The deck ships tomorrow regardless. What exactly goes on the slide, and what wording do you insist on?
- How would you estimate the development factors, and how would you notice if they had shifted?
- 01
How do you handle feedback on your analyses or models?
- 02
Give an example of a challenging problem you faced and how you overcame it.
- 03
A finance lead is looking at first-chargeback rate by transaction month, built from fct_card_dispute joined to fct_payment_authorization on auth_id and attributed to requested_at. The last three months slope sharply down and the lead wants to announce a fraud improvement at tomorrow's review. Consumer dispute rights commonly run around 120 days from the transaction or expected delivery date, so those months are not complete. In five minutes, with no statistics vocabulary, explain why the decline is not yet evidence and say exactly what you would put on the slide instead.
Is this an official Radial interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Radial. Rounds and questions reflect what candidates have reported, not a process Radial has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process?
The interview process is moderately challenging, with a balanced focus on technical skills and behavioral fit. Candidates typically spend about 2-4 weeks preparing, depending on their familiarity with the concepts.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates typically demonstrate a strong grasp of data science principles, effective problem-solving skills, and a collaborative approach to teamwork.
PracHub interview research ↗What is the culture like at Radial?
The culture at Radial emphasizes innovation, teamwork, and a customer-centric approach. Employees are encouraged to share ideas and contribute to projects collaboratively.
PracHub interview research ↗What is the typical timeline from initial interview to offer?
Candidates can expect a timeline of 4-6 weeks from the initial screening to the final offer, depending on scheduling and team availability.
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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