Interview guides · GM Financial

GM Financial Data Scientist Interview Questions & Guide 2026

Timed data scientist practice for GM Financial: short SQL and probability sets run end to end, then scored against a rubric.

The 60-second brief

A Data Scientist at GM Financial plays a pivotal role in driving the financial engine of General Motors. By leveraging advanced machine learning, predictive modeling, and statistical analysis, you will directly influence credit underwriting, risk management, asset pricing, and customer retention strategies. The insights and models you develop impact millions of auto loans and leases globally, ensuring that automotive financing remains accessible, competitive, and secure.

Learn the economics of the product category before the loop. Marketplaces, subscription products and ad-supported products turn on different core quantities (match rate and liquidity, retention and churn, fill rate and yield) and fail in different characteristic ways.

Focus areas

Decompose expected loss into PD, LGD, EAD
Set fraud thresholds by expected cost
Reconcile amounts in minor units and currency

Overview

At its core, this role bridges the gap between massive datasets and strategic business decisions. You will work on sophisticated products and initiatives, such as optimizing credit scoring algorithms, analyzing vehicle telematics data to predict driver behavior, and building lifetime value models for dealership networks. Operating in a highly regulated financial landscape, your work requires not only technical precision but also a deep commitment to model interpretability, fairness, and governance.

Joining the GM Financial data science team means working at the intersection of automotive innovation and financial services. As General Motors transitions toward an electrified and connected vehicle future, the data science team is tasked with modeling entirely new risk paradigms, such as electric vehicle residual value forecasting and connected-car data monetization. This makes the role both intellectually challenging and highly impactful to the parent company's broader strategic vision.

The GM Financial interview process

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

HR Screening Call

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

Online Assessment

Much of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.

What to demonstrate

Whether the query you write matches the plan you just described
What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly

How to prepare

Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock

Technical and Managerial Interviews

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

Panel Interviews

A loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.

What to demonstrate

Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience

How to prepare

Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story

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.

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.

Reaching for a model before the target metric exists

Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.

Reading experiment results before checking the arm split

Compare observed arm counts against the intended allocation ratio, not an assumed even split, and set the alarm far below the conventional 0.05: at 0.05 roughly one healthy experiment in twenty trips it, which is why sample-ratio checks usually run at p < 0.001 or stricter. The test's power scales with sample size, so it misses a real diversion on a small experiment and fires on an imbalance too small to move the estimate on a very large one. A flag means go find the assignment or logging fault before reading any outcome, not report a mismatch.

Questions & practice

Explain the bias-variance tradeoff and how you would address high variance in a credit risk model.

Medium · statistics and probability

Approach

Quantify uncertainty explicitly rather than reporting a point estimate alone.
Write down the assumption the method needs before you use the method.
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?

Walk me through the most complex machine learning project on your resume. Why did you choose that specific architecture?

Medium · machine learning and modelling

Approach

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

Follow-up

How would you choose the decision threshold, and who owns that choice?
Where could label leakage enter this setup?

How do you keep up with the latest advancements in data science and machine learning, and how have you applied a new concept to your work recently?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
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

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

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

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.
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.
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.
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.
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)

Sort by loan_id and months_on_book, build the ever_90 indicator, then apply groupby('loan_id')['ever_90'].cummax().
Compute cohort_size as the distinct loan_id count per origination_month, before any filtering on age.
Group the cumulative indicator by origination_month and months_on_book and sum it to get the numerator per cell.
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.
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?

Write a SQL query to calculate the rolling 3-month average of loan originations per dealership region.

Medium · sql and data manipulation

Approach

Compute rates by summing numerator and denominator separately, never by averaging rates.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
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?
What breaks if events arrive late or out of order?

Given an array of integers, write a Python function to find the contiguous subarray with the largest sum.

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
State the window function and its partition and ordering out loud before writing it.
Say which table is the grain you start from, and join outward from it.

Follow-up

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

Customers with no credit application, avoiding the NOT IN trap

Medium · null semantics · anti-join · scd type 2

Count current customers who have never submitted a credit application, broken out by segment. dim_customer is a slowly changing dimension type 2, so restrict to is_current = true, kyc_status = 'verified' and closed_at null. In fct_loan_application, customer_id is null for applicants who were not customers when they applied. Write the anti-join, return segment and customer_count, and state in one line what NOT IN (SELECT customer_id FROM fct_loan_application) returns against this table and why.

Approach

Pin the dimension to one row per customer first: is_current = true already guarantees that, but say so out loud, because forgetting it multiplies every count by the number of attribute versions a customer has accumulated.
Write the anti-join as NOT EXISTS with a correlated predicate on customer_id, which evaluates per row and is unaffected by nulls anywhere in the applications table.
Name the failure explicitly: NOT IN against a nullable column compares each candidate to a set containing NULL, the comparison yields UNKNOWN rather than TRUE, and the whole predicate is therefore never satisfied, so the query returns zero rows.
If NOT IN is required for some reason, add WHERE customer_id IS NOT NULL inside the subquery, which restores the intended semantics, and note that a LEFT JOIN with an IS NULL filter is equally safe.
Group by segment and sanity-check the total against the unfiltered current-customer count minus the count of distinct applying customers.

Worked solution (20 min)

SELECT segment, COUNT(*) FROM dim_customer c WHERE c.is_current AND c.kyc_status = 'verified' AND c.closed_at IS NULL.
Add AND NOT EXISTS (SELECT 1 FROM fct_loan_application a WHERE a.customer_id = c.customer_id).
Group by segment and order by the count descending.
Run the NOT IN variant alongside it and record that it returns zero rows, then run it again with IS NOT NULL added to the subquery and confirm the counts match the NOT EXISTS version.

Expected result: One row per segment present in the current dimension (retail, sme, corporate), each with a non-zero customer_count. The NOT EXISTS and the null-guarded NOT IN forms agree exactly; the unguarded NOT IN form returns no rows at all.

Verify it yourself

COUNT(DISTINCT customer_id) equals COUNT(*) on the filtered dimension, which confirms is_current really collapsed the type 2 versions.
The total across segments plus COUNT(DISTINCT customer_id) from fct_loan_application restricted to current verified open customers equals the full current verified open customer count.
Swapping NOT EXISTS for the unguarded NOT IN changes the result from a populated set to an empty one, which is the whole point of the exercise.

Follow-up

Rewrite it as a LEFT JOIN with IS NULL and say when you would prefer that form to NOT EXISTS.
How does the answer change if you want customers who never applied as of a historical date rather than today?
The applications table has 40,000 rows with a null customer_id. What are those rows, and are they a data quality problem or a product fact?

How do you optimize a Python script that is running slowly due to large-scale data manipulation?

Medium · product sense and metrics

Approach

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

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?

Describe an academic or professional project where you had to pre-process messy, unstructured data. What was your approach?

Medium

Approach

Work from the decision backwards to the evidence you would need.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.

Follow-up

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

Judge a rate increase without letting price flatter the ratio

Hard · mix shift · adverse selection · loss development

A rating change raises premium on one product_line's renewal book. Using fct_policy_period_monthly (earned_premium_minor, written_premium_minor, exposure_units, paid_loss_minor, case_reserve_minor, ibnr_reserve_minor, loss_adjustment_expense_minor, rating_tier, policy_status, renewal_flag, policy_term_end), the accident-period loss ratio improves two points over the following year. Explain why that number alone cannot tell you the change worked, decompose the movement into the effects you would separate, and specify the primary metric and guardrails you would commit to before the next rating change.

Approach

Start with the arithmetic. A rate increase raises earned premium per exposure unit, so the loss ratio falls even if every policyholder behaves identically and every claim is unchanged. Part of the two points is mechanical and carries no information about risk at all.
Switch the risk measure to one price cannot move: pure premium, being incurred losses (paid_loss_minor plus case_reserve_minor plus ibnr_reserve_minor, with loss_adjustment_expense_minor included or excluded consistently and the choice stated) divided by exposure_units, evaluated by accident period at a fixed development age. Flat pure premium alongside an improved loss ratio means the improvement was entirely price.
Decompose the loss ratio movement into three named components: the price effect at constant exposure and mix, the mix effect from which rating_tiers renewed and which lapsed, and the residual change in pure premium within tier. Only the third is evidence about risk selection, and it is usually the smallest.
Name the adverse-selection risk directly. Price sensitivity and loss propensity are not independent, and the policyholders most able to leave after a rate rise are often the ones worth keeping. Retention by rating_tier is therefore a guardrail with teeth, and it has to be read at tier level because a flat blended retention hides tiers moving in opposite directions.
Add prior-period reserve development as the second guardrail. The same two points are producible by setting case reserves or IBNR light, which surfaces only later as adverse development, so the reserve guardrail is what stops the primary metric being satisfiable by an accounting choice.
Commit the primary before the next change: underwriting margin per exposure unit, being earned premium minus incurred losses minus loss adjustment expense minus allocated expense, over exposure_units, at a fixed development age, reported by accident period and by rating_tier with exposure volume printed beside it so that improving margin by shrinking the book is visible in the same table.

Worked solution (40 min)

By accident quarter at a fixed twelve-month development age, compute both the loss ratio (incurred over earned_premium_minor) and the pure premium (incurred over exposure_units), before and after the change.
Build the three-way decomposition: move price only at constant mix and exposure, then move the tier mix to the post-change distribution at constant price, then take the remainder as the within-tier pure premium change.
Compute renewal retention by rating_tier from policies reaching policy_term_end, excluding cancelled_midterm and terms where no renewal offer was made, and cross each tier's retention with its prior pure premium.
Pull prior-period reserve development for the periods used and state whether the improvement survives it.

Expected result: A decomposition splitting the two-point improvement into price, mix and within-tier pure premium components that reconcile to the total, alongside retention by rating_tier and an explicit statement of whether the within-tier component is distinguishable from zero.

Verify it yourself

The three components reconcile to the observed change up to the interaction term. Decide in advance where the interaction is allocated and say so on the chart.
Pure premium contains no price term by construction, so a step change in it exactly at the rate effective date points to an exposure or earning-pattern error rather than to a risk effect.
Recent accident periods have no value at the fixed development age, which means unavailable rather than favourable, so any period younger than that age is excluded from the comparison.
Retention measured at tier level reconciles to the blended figure once terms with no renewal offer are excluded from both.

Follow-up

Retention is flat overall but fell nine points in the lowest-loss tier. What do you expect next year's pure premium to do?
Why not use written premium as the denominator, and where would you still legitimately see it used?
How would you separate a genuine underwriting improvement from a year of mild weather?

Approval rate rose in every band yet fell overall

Medium · mix shift · simpsons paradox · segmentation

Monthly application approval rate on fct_loan_application fell from 62 to 57 percent. Cut by bureau_score band, the rate rose in every band, including the null-bureau band. Columns: application_id, channel, submitted_at, requested_amount_minor, declared_annual_income_minor, bureau_score, model_pd_12m, model_version, policy_rule_hits, decision, decided_by, decision_at. The denominator is decision in ('approve','decline'). Explain the arithmetic, quantify how much of the five-point fall is mix versus within-band movement, and say what you would tell the team that owns acquisition.

Approach

Confirm the paradox is real rather than a banding artefact. Rebuild the bands on fixed cutpoints taken from the earlier period, because quantile bands re-cut each month move with the population and can manufacture this pattern on their own.
Compute the exact decomposition rather than describing it: within = sum of w_i0 * (r_i1 - r_i0), mix = sum of r_i0 * (w_i1 - w_i0), interaction = sum of (w_i1 - w_i0) * (r_i1 - r_i0). The three terms sum identically to the change in the blended rate, so the report can state the split.
Attribute the weight change by cutting the same fixed bands by channel. Keep null bureau_score as its own band; a thin file is a population signal, not missing data to be imputed away.
Check whether the new arrivals also changed the population inside a band, by comparing requested_amount_minor and declared_annual_income_minor distributions within one band across the two months.
Deliver two numbers rather than one story: policy is looser in every band, and the funnel is being fed a different population. Those have different owners and different fixes.

Follow-up

If the new channel is profitable at its own approval rate, is the blended fall a problem at all?
How would you present this so that nobody reads the blended series unaccompanied again?
What breaks if you fix the mix by reweighting to a frozen band distribution every month?

Suggested practice checklist

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.

Day 1 — Build 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.

Day 2 — Joins, 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.

Day 3 — Window 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.

Day 4 — The 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.

Day 5 — Write 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.

Day 6 — One 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.

Day 7 — Full 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.

Being wrong in public

A number you shipped turned out to be wrong, and someone had already acted on it. That is one of the most useful stories a data person can carry. What is being scored is how fast you noticed, who you told first, and what you changed in the process so the same class of error could not repeat quietly.

Why do you want to work for GM Financial specifically, rather than a traditional tech company or a commercial bank?

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Name the disagreement or constraint, and how you resolved it with evidence.
Close with what you would do differently, concretely.

Follow-up

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

In your previous role, how did you translate a complex technical model's outputs into actionable insights for non-technical business stakeholders?

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Quantify the outcome, including what you would not claim credit for.
Close with what you would do differently, concretely.

Follow-up

What did you decide not to do, and why?
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?