As a Data Scientist at Drax, you are at the intersection of complex operational data and the transition to a sustainable energy future. This role is pivotal in transforming vast amounts of industrial and market data into actionable intelligence that drives efficiency, reliability, and strategic decision-making across the organization. You will not be working in a vacuum; your insights directly influence how Drax approaches its core infrastructure and energy production goals.
The position demands a blend of rigorous analytical thinking and the ability to communicate technical complexity to non-technical stakeholders. You will be expected to design robust experiments, monitor critical performance metrics, and build predictive models that help the business navigate a rapidly changing energy landscape. If you enjoy solving high-stakes problems where data integrity and statistical accuracy are paramount, this role offers a unique opportunity to make a tangible impact at scale.
Initial Screening
reportedData 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
Take-Home Assessment
reportedA take-home is graded as an argument, not as a notebook. Somebody reads the submission without you in the room, so every choice has to survive on the page: why the question was framed this way, and what was deliberately left out. The gap between a strong and a weak submission is almost never model quality. It is whether the writeup names the specific question it answers and commits to a recommendation, including what evidence would overturn it. A high-accuracy model attached to no conclusion reads as effort that stopped before the decision.
What to demonstrate
- Whether the question you answered is stated outright, and whether it is the question the prompt posed rather than an easier neighbour of it
- Whether the recommendation is specific enough to act on, with the uncertainty attached to it instead of parked in a caveats section at the end
- Whether analytical choices such as the metric definition, the population filter and the time window are justified in the prose, not merely visible in code
How to prepare
- Take a dataset you have already worked with, write the one-paragraph conclusion first, then check whether the analysis you were planning actually supports it and cut whatever does not
- Practise stating a metric in one sentence that fixes the population, the time window and the denominator, then confirm your query computes exactly that sentence and nothing adjacent to it
- Hand a draft to someone outside the problem and ask them to tell you back what you recommended and why; anything they cannot recover is not on the page yet
Technical Discussion
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
Cultural Discussion
reportedBecause the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.
What to demonstrate
- Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
- Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
- Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs
How to prepare
- For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
- Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
- For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.
PracHub editorial advice for the preparation topics above.
Recalibrating an underwriting cutoff on approved and funded applicants only
Rejected applicants have no repayment outcome, and they were rejected because the incumbent model scored them badly, so the missingness depends directly on the outcome being modelled. Reject inference by augmentation or parcelling fills the gap using the incumbent model's own assumptions, which means it can confirm those assumptions but cannot test them. The only genuinely new information about the reject region comes from bureau performance on rejects who borrowed elsewhere, or from a deliberately randomised approval band around the cutoff.
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.
Dropping rows with missing values without naming the mechanism
Say whether the values are missing at random, missing by a known process, or missing in a way that depends on the outcome, and handle them accordingly. Deleting incomplete rows silently redefines the population whenever missingness correlates with what you are measuring.
Explaining an aggregate move without decomposing the mix shift
Split the change in the aggregate into within-segment movement and movement in segment weights before you explain it. Every segment's rate can fall while the overall rate rises, purely because volume shifted toward segments that already had higher rates.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you approach the ethical considerations of data collection and …
How do you approach the ethical considerations of data collection and model bias?
Approach
- Say how the offline result would be validated online before it is trusted.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
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?
Write integrity checks for the authorization and settlement lifecycle
You are given fct_payment_authorization as a pandas DataFrame with auth_id, requested_at, amount_minor, transaction_currency, auth_result, decline_reason_code, is_reversal, parent_auth_id, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency and settlement_fx_rate. Write a function returning one row per integrity check with the check name, failing row count, failing share and up to five example auth_id values. Cover at least six checks, one of which reconciles captured_amount_minor against settlement_amount_minor through settlement_fx_rate. Partial capture, zero-amount verification and a decline with no capture are all legitimate and must not be flagged.
Approach
- Separate contract violations from observations before writing any code: an approved row carrying a decline_reason_code is structurally impossible, while a capture two days after requested_at is merely slow and belongs in a different severity tier.
- Express each check as a boolean mask over the whole frame and collect the masks in a dict, so the summary table is one comprehension over mask.sum() rather than a row loop.
- For the reconciliation, leave minor units before comparing: expected = captured_amount_minor / 10exponent[transaction_currency] * settlement_fx_rate * 10exponent[settlement_currency]. Build the exponent table covering zero-decimal and three-decimal currencies instead of assuming two everywhere.
- Guard the legitimate cases explicitly so each mask fires only on the genuine contradiction: captured_amount_minor below amount_minor is partial capture, amount_minor of zero on an approved row is account verification, a null captured_at on a declined row is correct.
- Sort the output by failing share times a stated severity weight, because a check firing on 0.01 percent of rows can still be the one that breaks a ledger reconciliation.
Follow-up
- Which of these would you run as a blocking pipeline assertion and which as a monitored metric, and why?
- The FX check fails on 3 percent of rows, all in one settlement currency. How do you decide between a data bug and a rounding convention?
- How would you detect that a currency's minor-unit exponent is wrong in your reference table, using only the transaction data?
Bootstrap a fraud loss rate that clusters within merchant
You have a per-transaction frame with auth_id, merchant_id, settled_amount_reporting and net_loss_reporting, both already in one reporting currency. Most rows carry zero loss, a few carry large ones, and losses cluster within merchant. Using only numpy's random generator and no resampling helper from any library, write a bootstrap that returns a 95 percent interval for net fraud loss in basis points of settled volume, resampling merchants with replacement and taking all rows belonging to each drawn merchant. Also produce the naive row-level interval and state which you would report.
Approach
- State the estimator before writing it: total net loss divided by total settled volume, times 10,000. It is a ratio of sums, so each replicate recomputes both sums. Averaging per-transaction loss rates instead would weight a five-unit transaction like a five-thousand-unit one.
- Pre-aggregate loss and volume to merchant level once. For a ratio of sums, drawing merchants and taking all their rows is arithmetically identical to drawing merchant-level (loss_sum, volume_sum) pairs, so a replicate becomes one integer draw plus two vectorised sums rather than a groupby inside the loop.
- Draw B replicates of M merchant indices with replacement, where M is the observed merchant count, compute the ratio per replicate, and take the 2.5th and 97.5th percentiles. Say explicitly that this is a percentile interval and that BCa would correct the skew-induced bias if the decision is close.
- Repeat with independent row draws for the naive interval and compare widths on the same replicate count.
- Report the clustered interval. Rows within a merchant share an acceptance profile, a category code and a fraud exposure, so they are not independent, and the row-level interval understates variance by roughly the design effect.
Worked solution 30 min
- Compute the point estimate directly on the full data and keep it for comparison.
- Aggregate to merchant-level loss and volume arrays, record M, and set B to 2,000 with a seeded numpy Generator.
- In a vectorised loop, draw integer indices of shape (B, M), index both arrays, sum along axis 1, and take the ratio times 10,000.
- Repeat for the row-level version using the per-transaction arrays and N draws.
- Take the 2.5 and 97.5 percentiles of each replicate array and report both intervals alongside the point estimate.
Follow-up
- Your clustered interval is three times wider. How do you explain that to someone who wanted a tighter number?
- One merchant accounts for 40 percent of losses. What does that do to the interval, and what would you do about it?
- How does this change if the question is whether two months differ rather than what this month's rate is?
Describe a situation where you had to join disparate datasets to answe…
Describe a situation where you had to join disparate datasets to answer a complex business question.
Approach
- 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.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
Using SQL window functions, how would you calculate a rolling 7-day av…
Using SQL window functions, how would you calculate a rolling 7-day average of energy output?
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.
- 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?
Count-weighted and dollar-weighted approval rates on one currency
Using fct_payment_authorization, report the trailing 7-day authorization approval rate two ways for transaction_currency = 'EUR': count-weighted, and dollar-weighted on amount_minor. Exclude is_reversal = true, exclude incremental authorizations (parent_auth_id not null), and exclude zero-amount account verifications. auth_result = 'approved' is the numerator; the four declined_* values make up the rest of the denominator. Return channel, attempts, approved_attempts, approval_rate_count and approval_rate_value. State every exclusion and its reason before you write the SELECT.
Approach
- Say the denominator out loud first: attempts on a single transaction currency, excluding reversals, incremental authorizations and zero-amount verifications, because none of those is a purchase attempt a merchant is trying to get approved.
- Filter requested_at against a half-open interval (>= start AND < end) so the boundary day is neither dropped nor double counted.
- Compute both rates in one pass with FILTER clauses: COUNT() FILTER (WHERE auth_result = 'approved') over COUNT(), and SUM(amount_minor) FILTER (WHERE auth_result = 'approved') over SUM(amount_minor).
- Cast one side of each ratio to numeric before dividing, since amount_minor and the counts are integers and integer division silently truncates to zero.
- Group by channel and sort by the value-weighted rate, then read the gap between the two rates as a statement about where the declines sit rather than as noise.
Worked solution 20 min
- Write the exclusion list as comments above the query: is_reversal = false, parent_auth_id is null, amount_minor > 0, transaction_currency = 'EUR'.
- Build a single aggregate query over fct_payment_authorization with a half-open requested_at predicate and those four filters.
- Emit attempts, approved_attempts, approval_rate_count and approval_rate_value with FILTER clauses and a numeric cast on the numerator.
- Group by channel, order by approval_rate_value ascending so the worst channel is on top.
Follow-up
- The two rates diverge by four points on the ecommerce channel but agree on card_present. What does that tell you, and what would you cut next?
- How would you extend this to all currencies without summing amount_minor across them?
- Which of the four decline reasons belong in the denominator of a rate you would put in front of a risk team, and which are really the network's problem?
If we notice a sudden drop in a key user engagement metric, what is yo…
If we notice a sudden drop in a key user engagement metric, what is your systematic approach to diagnosing the root cause?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
Define a set of North Star metrics for a new digital tool intended for…
Define a set of North Star metrics for a new digital tool intended for internal operations.
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
How would you design a dashboard to track the health of a new energy-e…
How would you design a dashboard to track the health of a new energy-efficiency product?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- 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
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How do you balance trade-offs between two competing product metrics?
How do you balance trade-offs between two competing product metrics?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
- State what result would change your recommendation, so the answer is falsifiable.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
What are the most common experimentation pitfalls you have encountered…
What are the most common experimentation pitfalls you have encountered, and how do you mitigate them?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Decide the analysis before seeing data, including how long it runs and when you look.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you do if you could not randomise at all?
- How would you handle interference between treated and control units?
How do you determine the required sample size for an A/B test to ensur…
How do you determine the required sample size for an A/B test to ensure statistical significance?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Say whether units interfere with each other, and switch design if they do.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
Diagnose a sample ratio mismatch before reading the result
A two-week test of a new risk rule logged 512,340 exposures in treatment and 508,110 in control against an intended 50/50 split. The dashboard reports a 1.8 percent relative lift in the count-weighted authorization approval rate, p equals 0.004. Exposures are written by the events pipeline at the point the rule is evaluated. Before anyone reads the lift, test whether the split is consistent with 50/50, state your verdict, rank the mechanisms that would produce this imbalance, and say what you do with the two weeks of data.
Approach
- Run the test rather than eyeballing the ratio: a chi-square goodness-of-fit on one degree of freedom against the expected 50/50, or equivalently a binomial test on the treatment share. A 0.42 percent imbalance looks trivial and is not, because at a million exposures the null distribution is extremely tight.
- Declare the result uninterpretable rather than adjusting it. An SRM means units are missing from one arm conditional on something, and the something is usually correlated with the outcome, so reweighting or trimming does not restore exchangeability; it just hides the selection.
- Localise the mismatch before hypothesising about it: recompute the split by day, by channel, by issuer_country and by whether the rule actually fired. An SRM confined to one slice points straight at the code path that produced it.
- Rank mechanisms by how often they are the cause here. First, exposure logged downstream of a step the treatment changes, so any arm-specific drop-off before the log line silently deletes units. Second, assignment keyed on something mutable or nullable, such as card_token_id across a reissue or a null customer_id on guest traffic. Third, bot or fraud filtering applied after assignment and hitting arms unequally. Fourth, retry rows deduplicated after assignment rather than before. Fifth, a ramp or a rollback that moved mid-test.
- Close the loop with an A/A run on the fixed pipeline before rerunning the A/B, and add a continuous SRM check with an alert threshold so the next occurrence is caught on day one instead of at readout.
Worked solution 15 min
- Total exposures N = 1,020,450, so expected per arm E = 510,225 and the absolute deviation is 2,115 in each direction.
- Chi-square = 2 * 2115^2 / 510225 = 2 * 4,473,225 / 510,225 = 2 * 8.767 = 17.53 on one degree of freedom.
- Convert: sqrt(17.53) = 4.19 standard deviations, so p is about 3e-5. The observed treatment share is 0.50208 against a null standard error of 0.000495, which is the same statement in another form.
- Declare SRM, withhold the 1.8 percent lift from any decision, and produce the per-day and per-channel split table that localises the cause.
Follow-up
- Suppose the mismatch is entirely in one issuer_country and the treatment adds a 3-D Secure step there. What is the most likely code path, and what does that imply about the sign of the observed lift?
- What SRM alert threshold would you set for a daily check, and how do you keep it from firing constantly across many concurrent tests?
- If the split is exactly 50/50 but the two arms have different distributions of mcc and channel, is that an SRM? What is it, and what do you do about it?
A single day of settled volume comes in thirty percent low
Yesterday's settled volume is 30 percent below the same weekday across the previous eight weeks, while authorization counts and approved counts for the day look normal. You have fct_payment_authorization with requested_at, captured_at, captured_amount_minor, settled_at, settlement_amount_minor, settlement_currency, created_at and updated_at, plus warehouse load metadata. Decide within the hour whether this is a real drop or a load problem, and say what you would post to the on-call channel in either case.
Approach
- Check the data before the business. Compare max(updated_at) and row counts per load partition for the affected day against the prior eight same weekdays; a truncated load usually shows as a count deficit confined to a specific hour range.
- Measure null rates per column by requested_at date. Authorization fields present while settled_at and settlement_amount_minor are null points at the settlement feed; a uniform deficit across all columns points at ingestion.
- Separate late arrival from loss. Settlement lands after authorization by design, so re-measure the same day 24 and 48 hours later. A deficit that closes on the next load is lag, not a drop.
- Reconcile against the independent source: tie the day's settled total to the settlement ledger or acquirer file. If the ledger has the money and the warehouse does not, the business number was never wrong.
- Only after those four steps consider a business explanation, and then only if a segment cut produces a coherent story rather than a flat haircut spread evenly across every merchant and channel.
- Close by splitting the alert: a freshness and completeness check on the settlement feed, separate from the volume alert, so the next occurrence is classified automatically.
Follow-up
- How would you make the daily volume alert immune to settlement lag without also hiding a genuine drop?
- What is the right way to restate a published number after a backfill lands?
- Which check catches a partial load that is uniform across the day rather than concentrated in one window?
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 ↗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 ↗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.
Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.
Explain the concept of p-values to a non-technical stakeholder.
Explain the concept of p-values to a non-technical stakeholder.
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.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Defend a vintage finding that contradicts the portfolio dashboard
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
Approach
- Reconcile before you contradict: show that aggregating your vintage table along the calendar diagonal reproduces the published blended series, so the disagreement is about age mix rather than about data quality.
- Make the mechanism arithmetic rather than rhetorical: a loan cannot reach 90 days past due before it is 90 days old, so rapid origination growth shifts weight onto young months-on-book where the rate is structurally near zero.
- Show every vintage rather than a selected pair, all indexed at months_on_book equal to 12, with cohort sizes printed beside each curve so nobody can claim the divergence rests on a thin cohort.
- Handle restructuring explicitly, because restructured_flag resets days_past_due: count each loan on its worst pre-restructure state, or recent vintages will look better than they are.
- Close on the decision rather than the chart: state what the divergence implies for the cutoff or the channel mix, and state in advance what evidence would make you withdraw the claim.
Follow-up
- Two cohorts differ at month 12. How do you separate a seasoning effect from a genuine credit-quality effect?
- Someone argues the recent vintages are simply a broker-channel mix shift. How do you test that, and what would confirm it?
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
Explain the concept of p-values to a non-technical stakeholder.
- 02
The lending dashboard shows blended 90-plus days-past-due falling for four consecutive quarters while originations grew 60 percent. Using fct_loan_performance_monthly, you build a vintage view keyed on origination_month by months_on_book and find the three most recent vintages are worse than their predecessors at the same age. The business lead presents that dashboard weekly and pushes back hard, suggesting you picked favourable cohorts. You get one meeting and the vintage table. Present the finding so it survives the cherry-picking objection and ends in a decision.
- 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 Drax interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Drax. Rounds and questions reflect what candidates have reported, not a process Drax has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
The assessments are designed to be challenging but fair. They test your ability to apply theoretical knowledge to practical, real-world data scenarios, focusing on your problem-solving process rather than just finding a single "correct" answer.
PracHub interview research ↗What is the best way to prepare for the behavioral rounds?
Use the STAR method (Situation, Task, Action, Result) to structure your stories. Focus on examples that highlight your technical contributions, your ability to influence others, and how you handled ambiguity or conflict.
PracHub interview research ↗Does Drax value specific machine learning frameworks?
Candidates report that Drax weighs a strong understanding of fundamental concepts over expertise in any single library. If you understand how a model works at its core, you can adapt to whichever tools the team uses.
PracHub interview research ↗How long does the hiring process typically take?
Timelines vary by team. You can expect to move through the stages over a period of a few weeks, depending on 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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