General Mills Data Scientist Interview Questions & Guide 2026

The 60-second brief

As a Data Scientist at General Mills, you are positioned at the intersection of global scale and consumer-centric innovation. This role is critical to the company’s digital transformation, where you leverage vast datasets to drive decision-making across supply chain optimization, e-commerce growth, and personalized marketing initiatives. You are not just building models; you are solving real-world challenges that impact iconic food brands and millions of consumers worldwide.

Scope your preparation by the data you would actually touch, because the title will not tell you. A seat that lives in event logs and weekly readouts rewards fluency in aggregation and metric definitions; a seat that owns a model in production rewards fluency in train/serve skew, retraining cadence and drift monitoring. The fastest way to find out which one you are interviewing for is to ask what the team shipped last quarter and what it gets paged about.

Focus areas

• Set fraud thresholds by expected cost
• Separate authorization, settlement and dispute outcomes cleanly
• Reconcile amounts in minor units and currency

Overview

The work is multifaceted and highly collaborative. You will frequently partner with engineering, marketing, and product teams to translate complex business requirements into scalable technical solutions. Whether you are optimizing logistics to ensure product availability or developing recommendation engines for platforms like Betty Crocker, your contributions directly influence the strategic direction of the business. You can expect a high-paced, data-driven environment where rigor and practical application are equally valued.

The General Mills interview process

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

Recruiter Screening

Data 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

Technical Assessment

A handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.

What to demonstrate

• Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
• Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
• Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.

How to prepare

• Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
• Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
• If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows

Stakeholder Interviews

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.

Where candidates lose points

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.

Counting authorizations instead of weighting them, and summing amounts across currencies

Declines skew toward high-value, cross-border and card-not-present transactions, so an unweighted approval rate can sit flat while approved value falls. Merchant retry logic also turns one declined purchase into several rows, inflating the denominator by an amount that varies by merchant and by decline reason. Amounts are held in the minor unit of the transaction currency and that unit is not always two decimals, since some currencies have none and some have three, so summing amount_minor across currencies produces a figure with no interpretation at all.

Reading an observational correlation as a causal effect

Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.

Interpreting a change before checking data quality and logging

Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.

Questions & practice

What is the difference between normalization and standardization, and when would you use each?

Medium · statistics and probability

Approach

1. Sanity-check the answer against a simple bound or a simulated case.
2. Write down the assumption the method needs before you use the method.
3. Say what the estimate is of, and over what population it generalises.

Follow-up

• How would you explain this result to someone who does not know statistics?
• What sample size would you need to detect an effect half this size?

How do you interpret an ROC-AUC curve in the context of a classification model?

Medium · machine learning and modelling

Approach

1. Frame the prediction: the label, the moment of prediction, and the action it triggers.
2. Say how the offline result would be validated online before it is trusted.
3. Set a baseline first, so any model has something honest to beat.

Follow-up

• What would you monitor after launch to know the model is still valid?
• Where could label leakage enter this setup?

Build a vintage delinquency table without pivot or unstack

Easy · vintage analysis · cohorts · groupby · cumulative max

fct_loan_performance_monthly gives loan_id, origination_month, months_on_book, days_past_due, charge_off_flag and restructured_flag. Produce a DataFrame with one row per origination_month and columns for months_on_book 0 through 12, each cell holding the share of that vintage's funded loans that had ever reached 90 or more days past due, or charge-off, by that age. You may not use pivot, pivot_table, crosstab or unstack. Cells for ages a cohort has not yet reached must be NaN rather than zero.

Approach

1. Define the per-row indicator as days_past_due >= 90 or charge_off_flag, then take a cumulative maximum of it per loan ordered by months_on_book, because the metric is reached-by-age-m, not in-that-state-at-age-m.
2. Deal with restructuring before the cumulative max. Restructuring resets days_past_due, so a restructured loan re-enters at current and, without the cumulative maximum carrying its pre-restructure worst state, reads as a cure.
3. Fix the denominator once as the count of distinct loan_id per origination_month across the whole cohort. Prepaid and charged-off loans stop producing rows, so a denominator recomputed at each age silently shrinks exactly where losses land.
4. Aggregate with groupby(['origination_month','months_on_book'])['ever_90'].sum(), then pre-build the output frame indexed by sorted origination months with integer columns 0 to 12 and assign from the grouped Series by .loc on its index.
5. Mask cells beyond each cohort's maximum observed months_on_book so an immature cell reads NaN instead of an artificially low rate.

Worked solution (30 min)

1. Sort by loan_id and months_on_book, build the ever_90 indicator, then apply groupby('loan_id')['ever_90'].cummax().
2. Compute cohort_size as the distinct loan_id count per origination_month, before any filtering on age.
3. Group the cumulative indicator by origination_month and months_on_book and sum it to get the numerator per cell.
4. Create the output frame with the sorted origination months as index and range(0, 13) as columns, fill it from the grouped Series, and divide each row by its cohort_size.
5. Compute each cohort's maximum observed months_on_book and set every cell to the right of it to NaN.

Expected result: A DataFrame with one row per origination_month and columns 0 to 12, values non-decreasing left to right within every row, near zero in column 0, and NaN forming an upper-right triangle where cohorts have not yet aged.

Verify it yourself

• Every row is non-decreasing across columns; a decrease means the cumulative maximum was not applied or a restructure reset leaked through.
• The denominator per row equals the cohort's distinct loan count and does not change across columns.
• Column 0 is at or near zero for every vintage, since a loan cannot be 90 days past due at its first month end.
• origination_month plus months_on_book is constant along an anti-diagonal, so one anti-diagonal should reproduce a direct calendar-month filter.

Follow-up

• Two adjacent vintages diverge at months_on_book 6. How would you separate seasoning, mix shift and a genuine credit-quality change?
• The three most recent vintages look best on this table. What do you check before saying so?
• How does the table change if charge-off policy moved from 180 to 120 days past due partway through the series?

Using SQL window functions, how would you calculate a rolling 7-day average of user interactions?

Medium · sql and data manipulation

Approach

1. Compute rates by summing numerator and denominator separately, never by averaging rates.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. State the window function and its partition and ordering out loud before writing it.

Follow-up

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

Explain the logic behind your approach to joining multiple large-scale tables for a recommendation system.

Medium · sql and data manipulation

Approach

1. Check whether any join is one-to-many before aggregating, or the sums inflate.
2. Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
3. Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

• What breaks if events arrive late or out of order?
• 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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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)

1. 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.
2. 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.
3. Emit loss_ratio_earned and loss_ratio_written, both cast to numeric, plus their difference and the written-to-earned premium ratio.
4. 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?

How do you determine if an observed lift in a metric is statistically significant and actionable?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. State what result would change your recommendation, so the answer is falsifiable.
3. 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?

If the conversion rate for a core product page drops suddenly, how would you systematically investigate the cause?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Fix the population and the time window before naming any metric.
3. Name one primary metric, then the guardrail that stops it being gamed.

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 would you design a metric to measure the success of a new recipe recommendation feature on our website?

Medium · product sense and metrics

Approach

1. State what result would change your recommendation, so the answer is falsifiable.
2. Restate the decision this analysis has to support, and who acts on the answer.
3. Fix the population and the time window before naming any metric.

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 common pitfalls when running an A/B test in a real-world e-commerce environment?

Medium · a/b testing and experimentation

Approach

1. Name the guardrails that would stop a launch even on a positive primary result.
2. Name the randomisation unit first; it decides the variance and what the test can detect.
3. State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

• How would you handle interference between treated and control units?
• What would you do if you could not randomise at all?

Measure an underwriting automation programme without hiding cost

Medium · metric gaming · swap set · operations metrics

An operations programme aims to cut manual underwriting. The proposed metric is manual review rate: decided_by = 'manual' over applications with decision in ('approve','decline') in fct_loan_application. The team controls the referral rules recorded in policy_rule_hits. Explain how they can halve that metric in one release while making the book worse, then design the readout that catches it: primary metric, the cuts it must always be reported in, and the guardrails. Say what evidence you would require before declaring a batch of referrals safe to auto-decide.

Approach

1. Name the exploit precisely. Referrals can be converted to automatic declines by a rule change, which removes them from the numerator at no operational cost while they stay in the denominator, so the rate falls and applicants a human would have approved are rejected instead. Auto-approving the same population moves the metric identically with the mirror-image failure mode.
2. Refuse the bare rate as a primary. Make it conditional on outcomes: manual review rate measured at a held-constant approval rate within each model_pd_12m band, so a reduction is creditable only when the decisions themselves did not move.
3. Require fixed cuts on every report: by model_pd_12m band and by the rule identifier inside policy_rule_hits. A single blended operations number cannot be decomposed after the fact, and the decomposition is the entire diagnostic.
4. Set guardrails on the things the exploit disturbs: approval rate within each score band, the override rate where manual decisions disagree with the automated recommendation, and the vintage 90+ rate at months_on_book = 12 for the newly auto-decided population against the referrals of the prior regime.
5. Fix the evidence bar for retiring a referral rule: a shadow period where the rule fires and the case is still worked manually, recording what the automation would have decided, what the human decided, and, for the disagreement group alone, the realised performance. Agreement on the easy majority establishes nothing about the disputed minority.
6. State the conflict honestly rather than designing around it. The referral population exists because the model is least certain there, so automating it trades loss for cost by construction, and the readout's job is to price that trade rather than to make it disappear.

Worked solution (30 min)

1. Compute manual review rate for a recent month overall, then by model_pd_12m band, then by rule identifier extracted from policy_rule_hits.
2. For each rule, compute the approve share and decline share of the applications it touches, so the rules whose removal would actually change decisions are identified before anyone proposes removing them.
3. Simulate the exploit: reclassify one high-volume referral rule's applications as automatic declines, then recompute the blended rate and the band-level approval rates side by side.
4. Write the readout spec: the primary with its conditioning, the two mandatory cuts, the three guardrails, and the shadow-period evidence bar.

Expected result: A table in which the simulated rule change lowers the blended manual review rate materially while approval rate in the affected model_pd_12m bands falls at the same time, together with a readout spec that makes it impossible to publish the first effect without the second.

Verify it yourself

• The simulated change lowers the manual review rate and the band-level approval rate together. If band approval rate is unchanged, that rule was not binding and its removal is genuinely free.
• Rule-level counts reconcile to the blended rate once applications hitting several rules are counted once. A jsonb array of rule identifiers is the usual place this double-counts.
• The newly auto-decided population and the prior manual referrals are compared at equal months on book, not at equal calendar dates.

Follow-up

• The override rate falls to near zero after the change. Is that good?
• How long must the shadow period run, given the outcome matures at twelve months on book?
• The rule fires on 200 applications a month. What can and cannot be established at that volume?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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

For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.

Day 1 — Metric anatomy

• For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
• For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
• Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.

Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.

Day 2 — Diagnosing a drop without guessing

• Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
• List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
• Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.

Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.

Day 3 — Should we build it

• Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
• Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
• Write the counter-metric that would make you kill the feature even if it wins on the primary metric.

Deliverable: A one-page product memo ending in a decision rather than a list of considerations.

Day 4 — The places aggregate numbers lie

• Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
• Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
• Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.

Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.

Day 5 — Technical maintenance, aimed at metrics

• Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
• Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
• Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.

Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.

Day 6 — Turning engineering work into data science stories

• Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
• For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
• Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.

Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.

Day 7 — Mock case and gap list

• Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
• Listen back and mark every moment you proposed a solution before the success metric existed.
• Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.

Deliverable: A recorded case plus a rewritten opening 90 seconds.

Ambiguous requests are the test

Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.

How do you handle missing values or corrupted data in a production pipeline?

Medium · behavioural and stakeholder questions

Approach

1. Quantify the outcome, including what you would not claim credit for.
2. Pick a story where you drove the decision, not one where you observed it.
3. 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?

State honestly what your cutoff change actually contributed

Hard · impact attribution · swap set · counterfactual

Six months ago your recommendation moved a credit cutoff, using fct_loan_application and fct_loan_performance_monthly. Since then approval rate rose four points and the 12-month vintage 90-plus rate on affected cohorts is flat. In the same window the bureau changed a score attribute, marketing shifted channel mix toward broker, and the internal funding rate moved. Your performance review asks for impact in currency terms. Give the number you would stand behind, the counterfactual it rests on, and the part of the observed movement you would not claim.

Approach

1. Define the counterfactual before computing anything: the claim is not what happened after the change, it is what would have happened had the old cutoff scored the same applications, which means replaying the old threshold on the post-change population.
2. Build the swap set: applications the new cutoff approves that the old one declined, applications the old one approved that the new one declines, and everyone else held out as unaffected. Only the swap groups carry your effect. Note that the swap-out group has no outcome under the new rule, because those loans were never funded, so its forgone margin must be estimated from matched pre-change approvals rather than observed.
3. Price each swap group at months_on_book equal to 12 on the measure the cutoff was meant to move: interest and fees collected, minus net charge-offs, minus funding cost at the internal transfer rate.
4. Strip the confounders explicitly. Cohorts affected by the bureau attribute change are either recomputed on the old attribute or excluded; channel mix is held fixed by reweighting to the pre-change mix; funding cost is charged at the rate in force each month rather than one blended average.
5. State the residual you will not claim, with its size, and give a range rather than a point wherever cohorts have not yet reached 12 months on book.

Follow-up

• The swap-in group is only 6 percent of applications. How does that change the way you present the number, and to whom?
• What would you have needed to set up at launch to make this attribution clean, and why was a randomised band around the cutoff not used?

Defend a vintage finding that contradicts the portfolio dashboard

Medium · vintage analysis · mix shift · stakeholder pushback

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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?