The Data Scientist role at Metron is a pivotal position that drives data-informed decision-making across the organization. This role encompasses the collection, analysis, and interpretation of complex data sets to uncover insights that influence product development, strategic initiatives, and user experiences. As a Data Scientist, you will have the opportunity to engage with diverse teams and collaborate on projects that have a significant impact on the company's offerings and overall business strategy.
Your work will be critical in shaping the direction of key products and solutions, enhancing their effectiveness and usability for users. You will be involved in projects that range from predictive analytics to machine learning implementations, providing you with a rich landscape of challenges that leverage your analytical skills. This role not only requires technical proficiency but also a strategic mindset to translate data into actionable insights that drive business success.
Phone 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 Assessment
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
Full-Day Interview
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.
PracHub editorial advice for the preparation topics above.
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.
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.
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.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to calculate the mean and standard deviation of a lis…
Write a function to calculate the mean and standard deviation of a list of numbers.
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
- What sample size would you need to detect an effect half this size?
- Which assumption here is most likely to be violated in practice?
Given a dataset, write a script to visualize the distribution of a spe…
Given a dataset, write a script to visualize the distribution of a specific feature.
Approach
- Write down the assumption the method needs before you use the method.
- Translate the result into the decision it informs, in one plain sentence.
- Say what the estimate is of, and over what population it generalises.
Follow-up
- What sample size would you need to detect an effect half this size?
- How would you explain this result to someone who does not know statistics?
What are common metrics used to evaluate the performance of a classifi…
What are common metrics used to evaluate the performance of a classification model?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- What would you monitor after launch to know the model is still valid?
- How would you choose the decision threshold, and who owns that choice?
Can you explain the concept of overfitting and ways to prevent it?
Can you explain the concept of overfitting and ways to prevent it?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- 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
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Measure calibration of a twelve-month default probability from scratch
fct_loan_application gives application_id, model_pd_12m, model_version, decision, funded_at and loan_id. fct_loan_performance_monthly gives loan_id, months_on_book, days_past_due and charge_off_flag. Define the outcome as ever 90 or more days past due, or charged off, by months_on_book = 12. Without sklearn or scipy, build an equal-count binned reliability table, the expected calibration error, the Brier score and its reliability, resolution and uncertainty components, and report the residual the binned identity leaves behind. Restrict to cohorts that have actually reached 12 months on book.
Approach
- Build the label first and name the population it covers out loud: only funded loans have outcomes, so this measures calibration on the approved population. The declined region is unmeasured, and no binning scheme repairs that.
- Restrict to applications whose loans have reached months_on_book = 12. A cohort observed at 8 months has a mechanically lower default rate and will read as systematic over-prediction that is really just immaturity.
- Bin by equal count, deciles of model_pd_12m through a rank-based cut, not equal width. The PD distribution is heavily right-skewed, so equal-width bins put most of the mass in the first bin and leave the risky bins with single-digit counts whose observed rates mean nothing.
- Per bin compute n, mean predicted, observed rate, and the binomial standard error sqrt(o(1-o)/n) so a gap can be read against noise. ECE is the count-weighted mean absolute gap between mean predicted and observed.
- Compute Brier directly as the mean squared error, then reliability = sum of n_k (pbar_k - obar_k)^2 over N, resolution = sum of n_k (obar_k - obar)^2 over N, uncertainty = obar(1 - obar). Report residual = Brier - (reliability - resolution + uncertainty). That identity is exact only for discrete forecasts, so with binned continuous scores the residual is the within-bin spread of the score; a large one means the bins are too wide to support the decomposition.
- Split by model_version. A mixed-version population can look well calibrated in aggregate while each version is biased in opposite directions.
Worked solution 45 min
- Reduce fct_loan_performance_monthly to one row per loan_id with the maximum days_past_due and any charge_off_flag over months_on_book 0 to 12, plus the maximum months_on_book observed, and keep only loans reaching 12.
- Inner-join to approved and funded applications, and record how many approved applications were dropped for immaturity and how many decisions were declines that never enter the measurement at all.
- Assign deciles with a rank-based cut on model_pd_12m, then aggregate n, mean predicted, observed rate and standard error per bin.
- Compute ECE, Brier, reliability, resolution, uncertainty and the residual, and print all six.
- Repeat the whole computation split by model_version and compare the per-version reliability against the pooled figure.
Follow-up
- AUC is unchanged after a population shift but the reliability curve has moved. What happened, and what do you do about it?
- How would you recalibrate without retraining, and what would you check afterwards?
- The top decile shows observed default well above predicted. Is that a calibration problem or a policy problem?
Monthly delinquency roll rates with honest exiting-account denominators
From fct_loan_performance_monthly, compute month-over-month roll rates: for each as_of_month_end and delinquency_bucket, the share of loans in that bucket that are in a worse state at the following month end. Use this disposition convention and apply it without exception: charge-off is the terminal state one rank past dpd_90_plus, so a loan that charges off has rolled forward, not exited; loans that leave the book any other way — prepaid in full, sold, matured, or simply absent at the next month end — are exited. Bucket order is current, dpd_1_29, dpd_30_59, dpd_60_89, dpd_90_plus, then charged_off. Return from_month, from_bucket, accounts, rolled_forward, cured, stayed, exited and roll_rate, and show that the four dispositions sum to accounts on every row.
Approach
- Map the five delinquency_bucket values to ranks 0 through 4 in a small inline VALUES list, and reserve rank 5 for charged_off, because 'worse' is an ordering statement and enum text does not sort that way reliably. Read charge-off from charge_off_flag on the next month's row rather than from delinquency_bucket, which keeps carrying whatever delinquency value the loan held when it was written off.
- Use LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) OVER (PARTITION BY loan_id ORDER BY as_of_month_end), then verify the leading month end is exactly one month after the current one, since a loan missing a month would otherwise appear to cure or roll across a gap it never traversed.
- Classify each loan-month into exactly one of four labels off the adjacent next row, whose rank is 5 when its charge_off_flag is true and its mapped bucket rank otherwise. rolled_forward: the next rank is strictly greater. cured: strictly lower. stayed: equal. exited: there is no adjacent next row at all, which is where prepayment, sale, maturity and a gap in the monthly history land.
- Define rolled_forward as 'any strictly worse rank', not 'rank plus one'. Under normal monthly aging the only reachable worse state is the next bucket, so the two definitions coincide — except at charge-off, where a loan can go from dpd_60_89 straight to rank 5. A strict plus-one test leaves those loan-months matching no branch and breaks the exhaustiveness invariant.
- Be explicit about why charge-off is a roll rather than an exit: routed to exited, dpd_90_plus has no worse state left, so its roll_rate is identically zero by construction — arithmetic, not a credit finding. At rank 5 the dpd_90_plus row reports roll-to-loss, which is the number a loss forecast actually consumes.
- Aggregate by from_month and from_bucket and require that rolled_forward + cured + stayed + exited equals accounts on every row, which is the invariant that proves the classification is exhaustive and mutually exclusive.
Worked solution 45 min
- CTE ranks: an inline VALUES mapping of the five delinquency_bucket values to ranks 0 through 4, joined onto the monthly rows. Rank 5 is not in the map; it is assigned during classification.
- CTE leads: add LEAD(as_of_month_end), LEAD(bucket_rank) and LEAD(charge_off_flag) partitioned by loan_id ordered by as_of_month_end.
- CTE classified: derive next_rank as CASE WHEN next_charge_off_flag THEN 5 ELSE next_bucket_rank END, then a CASE expression producing exactly one label per loan-month — exited when the next month end is null or is not the current month end plus one month, otherwise rolled_forward / cured / stayed by comparing next_rank to bucket_rank.
- Aggregate to from_month and from_bucket with COUNT(*) and four FILTER counts, then compute roll_rate as rolled_forward over accounts with a numeric cast.
- Assert the exhaustiveness invariant in a final HAVING or a separate verification query before trusting any number.
Follow-up
- Project the next three months of dpd_90_plus inflow from these roll rates. What assumption does that projection make, and when does it break?
- A restructure resets days_past_due to zero. What does that do to the cure rate out of dpd_60_89, and how would you separate a real cure from a reset?
- Argue the other convention: prepayment in exited but charge-off also in exited, with roll rates reported only for the four non-terminal buckets. What does that series answer better, and what does it lose?
- The charge-off policy changed from 180 to 120 days. Show where that appears in this table and what it does to the dpd_90_plus roll rate specifically.
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?
If you were tasked with improving a product based on user feedback dat…
If you were tasked with improving a product based on user feedback data, what steps would you take?
Approach
- Fix the population and the time window before naming any metric.
- 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.
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?
How do you prioritize tasks when working on multiple projects?
How do you prioritize tasks when working on multiple projects?
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
- 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?
Given a dataset with customer purchase history, how would you approach…
Given a dataset with customer purchase history, how would you approach predicting future purchases?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- Which segment would you cut first, and what would that rule out?
Explain the difference between supervised and unsupervised learning.
Explain the difference between supervised and unsupervised learning.
Approach
- Clarify what is being asked and what a complete answer would contain.
- Work from the decision backwards to the evidence you would need.
- Say what you would check first and why it is the highest-information step.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Success metrics for loosening a fraud decline threshold
A risk team proposes lowering the risk_score cutoff that produces auth_result = 'declined_risk_rule'. Settled volume per active customer is the north star; net fraud loss in basis points of settled volume is the guardrail. The two move in opposite directions by construction. Specify the readout: primary metric, guardrail, the maturity window each is read at, and the decision rule agreed before launch. Show the expected-cost arithmetic that sets the cutoff using an average ticket of 200 units, a 1.5 percent contribution margin, 35 percent recovery on fraud losses, and 12 units of downstream value lost per false decline.
Approach
- Refuse the two-metric framing and convert both sides into one currency. Approving a fraudulent transaction costs the amount net of recovery; declining a good one costs the forgone margin plus the downstream value of the customer's reaction. Decline when p times C_FN exceeds (1 minus p) times C_FP, so the break-even probability is p* = C_FP / (C_FP + C_FN).
- Put the numbers in. C_FN = 200 times (1 minus 0.35) = 130, C_FP = 200 times 0.015 plus 12 = 15, so p* = 15 / 145 = 10.3 percent. Then show the threshold is amount-dependent: at a 2,000 ticket C_FN = 1,300 and C_FP = 42, giving p* = 3.1 percent, so a single global cutoff is already the wrong shape before any tuning starts.
- State the precondition that makes this arithmetic legal: risk_score has to be calibrated, so that a score of 0.10 corresponds to an observed 10 percent fraud rate. A score that only ranks makes p* meaningless. Check the reliability curve before quoting any cutoff to anyone.
- Set the maturity windows separately. Volume is readable within days, fraud loss is not, so the guardrail is read only on transaction months with at least 120 days of dispute maturity and the decision stays open until then, or a leading indicator is agreed in advance with its bias written down.
- Agree the stopping rule before launch in the right units: revert if matured net fraud loss per unit of incremental settled volume exceeds the figure implied by p*. Fraud loss is supposed to rise when the cutoff loosens, so a rule that triggers on any rise is a rule that was never going to allow the change.
- Report the swap set rather than portfolio totals: the transactions the new cutoff approves that the old one declined, and their realised loss rate. Portfolio aggregates dilute the change into invisibility.
Worked solution 30 min
- Compute p* at ticket sizes of 50, 200 and 2,000 with the given margin, recovery and false-decline cost, and tabulate them.
- Bucket historical declined_risk_rule authorizations by risk_score decile and, for each bucket, write down what outcome data exists and what does not.
- Write the readout spec: primary metric, guardrail, the 120-day maturity rule, the swap-set table and the numeric stopping rule.
- Write the calibration precondition in two sentences and say how you would test it.
Follow-up
- Fraud loss in basis points falls after launch. Name two ways that happens without any improvement in decisioning.
- How do you keep observing outcomes in the region the rule still declines?
- What changes if the 12 units of downstream value is a guess with no evidence behind 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 someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.
How do you handle missing data in a dataset?
How do you handle missing data in a dataset?
Approach
- Quantify the outcome, including what you would not claim credit for.
- Close with what you would do differently, concretely.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
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?
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?
- 01
How do you handle missing data in a dataset?
- 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
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.
Is this an official Metron interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Metron. Rounds and questions reflect what candidates have reported, not a process Metron has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the interviews at Metron?
The interviews can be challenging, particularly in technical areas, so candidates should prepare thoroughly. Familiarity with data science concepts and hands-on practice is essential.
PracHub interview research ↗What differentiates successful candidates?
Candidates who can effectively communicate their thoughts, structure their problem-solving approach, and demonstrate a strong cultural fit with Metron tend to stand out.
PracHub interview research ↗What is the company culture like at Metron?
Metron promotes a collaborative and innovative work environment, where teamwork and user-focused solutions are prioritized.
PracHub interview research ↗What is the typical timeline from interview to offer?
The timeline can vary, but candidates generally receive feedback within a week of their final interview.
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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