Interview guides · Globallogic

Globallogic Data Scientist Interview Questions & Guide 2026

Data scientist prep for Globallogic: practice defining a payments and lending metric, picking its guardrails and defending the tradeoff.

The 60-second brief

As a Data Scientist at GlobalLogic, you operate at the intersection of complex engineering and advanced analytics. You are responsible for transforming raw data into actionable insights that drive product innovation for a diverse portfolio of high-stakes clients. Your work directly affects how GlobalLogic's partners optimize their operations, personalize user experiences, and solve intricate algorithmic challenges at scale.

Coding rounds for this role are usually data-manipulation shaped rather than data-structure shaped: group-bys, joins, time windows, ranking within a partition. Confirm the format before spending a week on graph traversal.

Focus areas

Separate authorization, settlement and dispute outcomes cleanly
Read vintage curves, not blended portfolio averages
Decompose expected loss into PD, LGD, EAD

Overview

This role is both technically demanding and strategically significant. You will often work across cross-functional teams, bridging the gap between technical implementation and business value. While the environment is fast-paced, it offers the opportunity to tackle diverse problem spaces—from predictive modeling to system architecture—making it an ideal position for those who thrive in dynamic, client-facing engineering environments.

The Globallogic interview process

Globallogic candidates report 4 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

A 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

Before anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.

What to demonstrate

Whether an ambiguous term becomes a specific column and filter before any computation happens
Whether you read the schema for keys and cardinality rather than only for column names
Whether the result answers the question at the grain it was asked at, per user or per session or per day

How to prepare

Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over

Client Interaction

An added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.

What to demonstrate

Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact

How to prepare

Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.

Team Fit Evaluation

Expect a live problem with pieces of it missing, closer to a conversation than an exam. A metric moved, or somebody wants to know whether a change worked, and you are asked how you would find out. The manager is watching the first ninety seconds, specifically whether you establish what decision hangs on the answer before you start proposing methods. Candidates who open with a technique get steered back. Once the decision is clear, describe what the data would look like if the story were true, and say what you would accept as evidence that it is not.

What to demonstrate

Whether you fix the decision the analysis serves before choosing an approach
How you continue when you are told the data you just asked for does not exist
Whether you state what would change your mind, not only what would confirm the hypothesis you started with
How you size an effect before you have measured it

How to prepare

Take a metric you know well and practise explaining in under two minutes the four things that could have moved it and how you would separate them
Pick a recent launch or experiment and write the single number you would ask for first, plus what you would conclude if it came back flat
Practise being interrupted: have someone remove a data source halfway through your answer and carry on without restarting

Where candidates lose points

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.

Accepting a metric definition without asking about the denominator

Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.

Ending an analysis without a recommendation or next step

Close with what you would do and what would change your mind, stated as a condition you can check later. If the evidence is genuinely inconclusive, recommend the specific next measurement and say what it costs in time or exposure.

Questions & practice

Explain the difference between bagging and boosting algorithms.

Medium · machine learning and modelling

Approach

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.
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?

Describe the role of OS fundamentals in deploying machine learning models.

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
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.

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?

Measure calibration of a twelve-month default probability from scratch

Hard · calibration · brier score · binning

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.

Expected result: A ten-row reliability table with n, mean predicted, observed and standard error; an ECE of a few tenths of a percentage point to a couple of points; a Brier score close to the uncertainty term, because a rare-event model has little resolution to subtract; and a residual small relative to the reliability term.

Verify it yourself

Bin counts sum to the filtered row count, and the count-weighted mean of bin observed rates equals the overall observed rate.
Uncertainty equals obar(1 - obar) computed directly, and reliability is zero on a synthetic input where observed is set equal to predicted in every bin.
The residual shrinks as the bin count rises from 5 to 20; if it does not, the label or the binning is wrong.
Multiplying every PD by 1.5 must raise reliability and ECE while leaving resolution, uncertainty and AUC unchanged, since equal-count bins are invariant to a monotone transform.

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?

How do you optimize a SQL query that is performing poorly on a large dataset?

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
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

How would you verify this result without re-running the same query?
How does the query change if the join becomes one-to-many?

Accident-quarter loss ratio on earned rather than written premium

Hard · loss ratio · earned vs written · development age

From fct_policy_period_monthly, compute the accident-quarter loss ratio by product_line: incurred losses, being paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor, over earned_premium_minor for the same accident quarter. State explicitly whether loss_adjustment_expense_minor is included and apply that choice consistently. Also output the same ratio computed on written_premium_minor so the two can be compared. The table holds current values with no valuation-date snapshot. Say in one line which comparison this schema cannot support and what you would need to support it.

Approach

Derive the accident quarter from as_of_month with date_trunc, and note that the table already attributes losses to the month of the loss event while earning premium pro rata into the same month, which is what makes the two sides comparable at all.
Aggregate earned_premium_minor, written_premium_minor and the three loss components to product_line and accident quarter in one pass, keeping loss adjustment expense as its own column so the inclusion choice is a final-select decision rather than something buried in a CTE.
Compute both ratios side by side and a third column for their difference, because the size and sign of that difference is a direct read on whether the book grew or shrank in the quarter.
State the limitation plainly: every row carries today's reserve estimate, so each accident quarter is observed at a different development age and a cross-quarter comparison mixes development with underwriting. A fixed development age needs a valuation-date dimension, that is one row per accident period per valuation, which this table does not have.
Guard against the mirror-image error on the numerator by confirming ibnr_reserve_minor is non-zero on recent quarters; if it is null or zero there, the recent periods are understated twice over and the series is not usable.

Worked solution (40 min)

CTE quarterly: group fct_policy_period_monthly by product_line and date_trunc('quarter', as_of_month), summing earned_premium_minor, written_premium_minor, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor and loss_adjustment_expense_minor.
Final SELECT: build incurred_minor as the three loss components plus the LAE column, with the LAE inclusion written as a named expression so the choice is visible on the page.
Emit loss_ratio_earned and loss_ratio_written, both cast to numeric, plus their difference and the written-to-earned premium ratio.
Order by product_line and accident quarter, and append the one-line note about the missing valuation dimension to the query as a comment.

Expected result: One row per product_line and accident quarter with earned and written premium, incurred losses, both ratios and their difference. In quarters where written premium exceeds earned, loss_ratio_written is strictly below loss_ratio_earned, and the gap widens with the growth rate.

Verify it yourself

For a mature product line with flat volume, summed earned premium over a full policy term is close to summed written premium, so the two ratios converge there.
The sign of loss_ratio_earned minus loss_ratio_written matches the sign of written premium minus earned premium in every quarter, which is an arithmetic identity and not a data finding.
Recent accident quarters have non-zero ibnr_reserve_minor; a zero there means the numerator is missing its tail and the ratio is understated.
Within a quarter, loss_ratio_earned divided by loss_ratio_written equals written_premium_minor divided by earned_premium_minor exactly, because both ratios share one numerator. Any deviation means the LAE term was built into one ratio and not the other, which is the real test that the inclusion choice was applied consistently.
Recomputing with LAE excluded scales each quarter's ratio by incurred_minor / (incurred_minor + lae_minor). That factor is not constant across quarters unless LAE happens to be booked as a fixed fraction of incurred losses, so a uniform shift is evidence about how LAE is loaded, not evidence that the query is self-consistent — use the previous check for that.

Follow-up

Written premium exceeds earned premium by 18 percent this quarter and by 3 percent two years ago. What happened to the book, and what does it do to each ratio?
How would you build a development triangle from a valuation-dated version of this table, and what would you use the chain-ladder factors for?
Statutory presentation conventionally takes the expense ratio on written premium while the loss ratio uses earned. How do you avoid a combined ratio that quietly mixes the two bases?

What are the trade-offs between various dimensionality reduction techniques?

Medium · product sense and metrics

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.
State what result would change your recommendation, so the answer is falsifiable.

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?

How do you evaluate the performance of a model when the business metric differs from the technical metric (e.g., RMSE vs. Revenue)?

Medium · product sense and metrics

Approach

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.
Fix the population and the time window before naming any metric.

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 client-facing projects simultaneously?

Medium · product sense and metrics

Approach

Restate the decision this analysis has to support, and who acts on the answer.
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?

What are the key differences between memory management in Python and C++?

Medium

Approach

Say what you would check first and why it is the highest-information step.
Clarify what is being asked and what a complete answer would contain.
Work from the decision backwards to the evidence you would need.

Follow-up

How would you know your answer was wrong?
What assumption would you test first?

Cut variance on a heavy-tailed settled-volume metric

Medium · cuped · stratification · heavy tails

The primary metric is settled payment volume per customer per month in one reporting currency, randomised by customer_id, computed from fct_payment_authorization rows that are approved, captured and not reversed. The available population powers a minimum detectable effect of 3.0 percent, but the plausible effect is about 1.2 percent. You have eight pre-randomisation weeks of the same metric per customer, and the distribution is heavily right-skewed. Propose a variance-reduction plan, state the estimator and its preconditions, quantify the improvement you expect, and say whether it closes the gap.

Approach

Apply CUPED with the pre-period metric as the covariate. Define Y_adj = Y - theta * (X - Xbar) with theta = Cov(Y, X) / Var(X) estimated on the pooled experiment data. The variance of the adjusted difference is Var(Y) * (1 - rho^2) where rho is the correlation between Y and X, so the entire benefit is determined by rho and nothing else.
Check the precondition that makes the estimator unbiased: X must be measured strictly before assignment. A covariate computed over any window that overlaps the test period absorbs part of the treatment effect and biases the estimate toward zero. Pin the covariate window end date to the randomisation timestamp, not to the analysis date.
Handle customers with no pre-period. Set X to zero plus a no-history indicator, or stratify them as their own cell. Either is valid because the imputation rule is fixed before assignment and does not depend on the arm; what is invalid is imputing with an arm-specific mean.
Attack the skew separately, because CUPED reduces variance but does not make a heavy tail light. Pre-register a winsorisation point such as the 99.5th percentile of the pre-period distribution, state plainly that this changes the estimand from the mean to a capped mean, and report both. Capping at a pre-period quantile rather than a post-period one keeps the cap from depending on the treatment.
Stratify the assignment on pre-period decile crossed with segment, which removes the same variance CUPED does when the covariate is the stratifying variable and additionally guarantees balance rather than achieving it in expectation. Post-stratified analysis is the fallback if assignment is already running.
Pin the currency conversion to a fixed rate table dated at test start. Using settlement_fx_rate as realised injects market movement into the metric, and if the arms have different currency mixes it injects it unequally.

Worked solution (25 min)

Estimate rho on historical data by correlating each customer's eight-week pre-period volume with their following-month volume; assume the answer is 0.62.
Compute the variance multiplier: 1 - rho^2 = 1 - 0.3844 = 0.6156, so the standard error falls by sqrt(0.6156) = 0.785 and the effective sample size rises by 1 / 0.6156 = 1.62.
Apply to the MDE: 3.0 percent * 0.785 = 2.36 percent.
Invert the requirement: reaching 1.2 percent needs a variance multiplier of (1.2 / 3.0)^2 = 0.16, which requires rho = sqrt(1 - 0.16) = 0.92. That correlation is not attainable from an eight-week pre-period on this metric.
Price the remaining shortfall in duration. Variance of the mean falls as 1/n, so the extra exposure needed is the ratio of the variance multipliers taken the right way round: 0.6156 / 0.16 = 3.85. The CUPED-adjusted plan must run about 3.85 times its planned exposure to detect 1.2 percent, holding rho and the traffic mix fixed; put the other way, the plan as written carries only about 26 percent of the sample it needs.

Expected result: At rho = 0.62, CUPED lowers the MDE from 3.0 percent to about 2.36 percent, a 21 percent reduction, and raises effective sample size by 1.62x. It does not reach 1.2 percent; that would need rho of about 0.92. Closing the rest with duration alone takes about 3.85 times the CUPED-adjusted exposure (0.6156 / 0.16), so the plan as written holds roughly 26 percent of the sample required. The correct deliverable states the shortfall and prices that additional duration rather than declaring the test adequately powered.

Verify it yourself

The duration factor must come out the same by both routes: 0.6156 / 0.16 = 3.85, and squaring the MDE ratio 2.354 / 1.2 = 1.962 also gives 3.85. A factor below 1 means the ratio has been inverted, since more sample is needed, not less.
Run CUPED on an A/A split of historical data: the point estimate must stay centred on zero and the standard error must fall by the predicted factor. If the estimate moves, the covariate is contaminated by post-randomisation data.
Regressing Y on X with an arm indicator should give an arm coefficient numerically equal to the CUPED-adjusted difference; the two are the same estimator.
Recompute rho within each segment. If the pooled rho is much higher than every within-segment rho, the gain is coming from between-segment variation that stratification already removes and should not be double counted.

Follow-up

Your pre-period correlation is 0.62 on existing customers and near zero on customers onboarded in the last month. How do you report a single effect across both groups without the low-correlation group eating the gains?
Someone proposes using a post-randomisation covariate that correlates 0.9 with the outcome. Why is that not a better CUPED covariate?
If you winsorise, what do you say to a sponsor who points out that the capped customers are the ones the business cares about most?

Portfolio delinquency improving while the loan book doubles

Easy · vintage analysis · age mix · right censoring

The blended 90-plus days-past-due rate across fct_loan_performance_monthly fell from 3.1 to 2.2 percent over two quarters while monthly funded volume roughly doubled. Credit leadership wants to know whether underwriting improved. Columns: loan_id, as_of_month_end, origination_month, months_on_book, original_principal_minor, principal_balance_minor, days_past_due, delinquency_bucket, restructured_flag, charge_off_flag, charge_off_date. Produce the view that answers the question honestly, and state in one sentence what the blended rate can and cannot tell you.

Approach

Name the mechanical floor first. A first instalment falls due roughly a month after funding, so a loan cannot reach dpd_90_plus until around its fourth month on book. Every recent origination therefore enters the denominator with a numerator that is structurally zero.
Build a vintage table: rows origination_month, columns months_on_book, cell equal to the share of that cohort whose worst days_past_due reached 90 or more, or whose charge_off_flag became true, at or before that age.
Use each loan's worst state to date rather than its current bucket, and take the pre-restructure worst state, because restructuring resets days_past_due and would otherwise read as a cure.
Compare cohorts only at equal months_on_book, and render cells beyond a cohort's current maturity as absent rather than zero, so the table cannot be misread left to right.
Decompose the blended move into an age-mix component and a within-age component, so the write-up states how much of the 0.9 point improvement is arithmetic rather than asserting it.

Follow-up

What does the diagonal of a vintage table represent, and when is reading it the right thing to do?
How would a change in charge-off timing policy show up in this table, and how would you separate it from credit quality?
Which single chart goes in front of the credit committee, and what do you say when someone asks for the blended series anyway?

Suggested practice checklist

Four days spend equal time on query work, statistics, modelling and product judgement at deliberately shallow depth, which produces a scored map of where you actually stand. The last three days spend everything on the two areas the role weights most, and close by re-running day one to measure movement.

Day 1 — Breadth pass: query fluency

Solve six prompts spanning aggregation, joins, window functions and date arithmetic in 60 minutes total, stopping at 10 minutes each whether or not it works, and mark every prompt as solved, solved slowly, or stuck.
For each unsolved prompt write the single blocking sentence (I lost the grain, I did not know the frame clause, I could not express the date boundary) instead of reading the solution.
Translate one pandas transformation you know well into SQL and one SQL query into pandas, checking that both return the same row count and the same totals.

Deliverable: A scored six-row table, one line per prompt, saved for the day-seven re-run.

Day 2 — Breadth pass: statistics and inference

Answer ten short questions in writing with nothing open: what a p-value is conditional on, what a 95 percent interval covers across repeated samples, when a paired test is the right one, what the bootstrap estimates, why multiple comparisons inflate false positives, how controlling the family-wise error rate differs from controlling the false discovery rate, what power depends on, what a missed real effect costs a product, the three situations where the central limit theorem does not rescue you (small n, very heavy tails, dependent observations), and what a standard error is the standard deviation of.
Grade yourself against a reference and count only the answers that were exactly right, not the ones that were nearly right.
Rewrite the two weakest answers the following morning from memory in full sentences.

Deliverable: Ten graded answers with an honest count of exact hits.

Day 3 — Breadth pass: modelling

Take one tabular dataset end to end in 90 minutes: a leakage-safe split, a baseline that is not a model (majority class or historical mean), one regularized linear model, one gradient-boosted tree, and a single evaluation metric chosen before you look at any result.
Write why that metric fits the cost structure: precision at a fixed recall for alerting, calibration for anything feeding a price or a threshold, ranking metrics for retrieval, and note that area under the ROC curve is insensitive to class balance in a way that can flatter a rare-positive problem.
Name the leak you were most likely to introduce (an encoding fit on all rows before splitting, or a feature computed after the label's timestamp) and write the check that would have caught it.

Deliverable: A notebook whose first cell states the metric and the baseline, plus two lines on what beat what and by how much.

Day 4 — Breadth pass: product judgement

Answer three case prompts aloud at 15 minutes each, timing how long passes before you state a success metric.
For one case write the first segmentation you would run and the row counts you expect per segment, so that a tiny segment cannot quietly drive the conclusion.
Take a metric definition you did not write, from a public dashboard, a textbook, or documentation you already have open, and list every place two analysts implementing it would diverge: which rows the denominator admits, whether the unit is an account or a person, what the time window is anchored to, and what happens to data that arrives late. Then write the one question that would close the largest of those gaps.

Deliverable: Three recorded case answers plus an ambiguity list for a metric someone else defined, ending in the single question you would ask about it.

Day 5 — Depth, first area

Rank the four areas by how many bullet points in the role description each one covers, pick the top one, and spend the entire day inside it.
Work the six hardest problems you can find in that area and for each write the generalizable move you should have reached for first, rather than the answer.
Re-solve the two you failed the same evening with notes closed.

Deliverable: Six generalizable moves written as instructions to yourself, not as solutions.

Day 6 — Depth, second area, and the seam between them

Repeat the depth protocol on the second-ranked area with the same six-problem structure.
Construct one problem that requires both areas at once, for example a metric redefinition whose effect you must validate with a test whose readout you then have to query.
Solve your own combined problem end to end and note where the handoff between the two areas cost you time.

Deliverable: One combined problem, solved end to end, with the handoff failure written down.

Day 7 — Integration and re-measurement

Re-run the six prompts from day one under the same clock and compare both correctness and time.
Run a 60-minute mixed mock that moves between areas without warning, since switching cost is what breadth passes do not train.
Write the two areas you would still fail on, and the sentence you will use in the interview when you hit one of them.

Deliverable: A before-and-after score table plus a written plan for the two remaining gaps.

Explaining a method to someone who will not follow the maths

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 imbalanced datasets in a production environment?

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Quantify the outcome, including what you would not claim credit for.
Pick a story where you drove the decision, not one where you observed it.

Follow-up

What would you do differently if you ran that project again?
What did you decide not to do, and why?

Tell me about a time you had to explain a complex technical finding to a non-technical stakeholder.

Medium · behavioural and stakeholder questions

Approach

Name the disagreement or constraint, and how you resolved it with evidence.
Pick a story where you drove the decision, not one where you observed it.
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?
How did you know the outcome was caused by your change?

Retract a published number after finding a currency bug

Medium · error disclosure · minor units · process repair

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

Approach

Size the error before announcing it, because saying the number is wrong without a magnitude and a direction forces every reader to assume the worst case.
Check whether the conclusion actually flips: if the ranking that drove the pricing decision is unchanged, that belongs in the first sentence beside the correction rather than buried at the end.
Tell the person acting on it first and directly, then the wider distribution, using the same text, so nobody learns about it secondhand.
Write the correction as four parts: the old number, the cause in one clause, the effect on the pending decision, and the new number. Leave out self-flagellation, which makes the reader do emotional work instead of acting.
Fix the class rather than the instance: a rule that a sum over amount_minor either groups by transaction_currency or passes through both conversion steps, exponent scaling and then a dated rate into one named reporting currency, plus a standing reconciliation of the settled subset to the settlement ledger inside each settlement_currency.

Follow-up

The corrected figures do not change the decision. Do you still send the correction, and what does that choice signal?
What automated check would have caught this, where would it live, and what would it cost in false alarms?