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Telus Digital Data Scientist Interview Questions & Guide 2026

Case practice for Telus Digital data scientist candidates: scope a vague prompt into a measurable question before you analyze it.

The 60-second brief

As a Data Scientist at Telus Digital, you are at the intersection of complex data ecosystems and high-impact digital solutions. Your role is critical to transforming raw information into actionable insights that drive product strategy, optimize user experiences, and solve sophisticated business challenges. You will work within a collaborative environment where data-driven decision-making is not just encouraged—it is the foundation of the company’s operational excellence.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

Measure churn only on renewal-eligible accounts
Separate contracted seats from actively used seats
Power experiments for heavy-tailed account revenue

Overview

You will contribute to a culture that values both technical depth and practical application. Whether you are building predictive models, optimizing algorithms, or communicating complex findings to non-technical stakeholders, your work directly influences the efficiency and scalability of Telus Digital services. This role is ideal for individuals who are passionate about applying machine learning to real-world problems and who thrive in an environment that balances individual contributions with cross-functional partnership.

The interview process at Telus Digital is widely noted for being approachable and conversational. Focus on demonstrating clarity in your thought process rather than just reaching the "correct" answer.

Where to focus your preparation

Treat the sections below as preparation areas and confirm the format with your recruiter.

Preparation focus

No round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.

What to demonstrate

Breadth across SQL, experimentation and product reasoning
Ability to state assumptions before choosing a method

How to prepare

Drill the practice exercises below and time yourself
Prepare three quantified stories about decisions you drove

Where candidates lose points

Reporting a mean over accounts when account revenue is heavy-tailed

When a small number of accounts hold most of the revenue, the sample mean is dominated by whichever of them happens to be in the sample, and the sample variance keeps growing as more data arrives instead of stabilising. In that regime the usual central-limit-based confidence interval understates uncertainty, and a single renewal or a single large account's batch job can flip the sign of a measured effect. The fixes are to pre-register a winsorisation or capping rule before looking at the outcome, to report account counts crossing a threshold alongside the revenue figure, or to define the estimand on a bounded transform. Choosing the cap after seeing the result is a separate and worse problem, because the cap then encodes the answer.

Treating raw request or usage volume as engagement

Most traffic in this domain is emitted by machines. Continuous-integration pipelines, scheduled batch jobs, synthetic monitors, backfills and client retries can all grow by an order of magnitude from one configuration change made by one engineer, and none of it represents a new decision to use the product. The inversion is what makes it dangerous: when the platform degrades, clients retry, so error-driven retry volume rises at the exact moment the customer is most likely to leave, and an engagement dashboard built on raw counts shows growth immediately before a churn. Filter on traffic_class and on successful status before anything else, and keep failed-request volume as its own separate series.

Ignoring interference between units in a marketplace experiment

Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.

Naming a model class before naming the deployment constraints

Set out the latency budget, the label delay, the retraining cadence, the interpretability requirement and the number of labelled examples, then pick the model that fits them. A boosted-tree answer to a problem where each decision must be explained to the affected user is a well-executed answer to the wrong question.

Questions & practice

Explain the difference between bagging and boosting.

Medium · statistics and probability

Approach

Sanity-check the answer against a simple bound or a simulated case.
Translate the result into the decision it informs, in one plain sentence.
Write down the assumption the method needs before you use the method.

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?

When would you choose one objective function over another?

Medium · statistics and probability

Approach

Translate the result into the decision it informs, in one plain sentence.
Say what the estimate is of, and over what population it generalises.
Write down the assumption the method needs before you use the method.

Follow-up

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

What are the primary metrics used to evaluate a classification model?

Medium · machine learning and modelling

Approach

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

Follow-up

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

Explain the bias-variance tradeoff in the context of model performance.

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?

Simulate how the renewal calendar distorts monthly churn rates

Medium · simulation · churn · numpy · cohorts

Simulate 1,200 accounts on annual contracts. Draw each account's renewal month from a deliberately lumpy calendar: 30% renew in January and the remaining 70% are spread evenly over the other eleven months. At each renewal an account churns with probability 0.18, independent of month; survivors renew and come back twelve months later. Run 24 simulated months. For each month compute two rates: churned accounts over all live accounts, and churned accounts over accounts whose term ended that month. Report the mean and the month-to-month standard deviation of each series, and state which one belongs in an executive summary.

Approach

Build the panel with numpy state arrays rather than a per-account loop: a next_renewal_month vector, an alive boolean vector, and a loop over the 24 months only. Looping over 24 months is fine; looping over 1,200 accounts inside it is what makes the simulation too slow to iterate on.
Maintain the live set honestly. An account that churned in month m must leave the denominator from m+1 onward and can never be renewal-eligible again; if it stays, the naive rate drifts downward for reasons that have nothing to do with churn and the calendar effect gets buried.
Compute both series over the same months and compare dispersion, not only level. The eligible-base rate should sit near 0.18 with binomial noise scaled by that month's renewal count; the naive rate spikes in January and collapses in thin months.
Quantify the gap instead of describing it: the ratio of the two means is roughly the reciprocal of the average monthly renewal-eligible fraction, and the naive series' standard deviation is driven by the signing calendar rather than by customer behaviour.
Check against the closed form before trusting the output. With the live set maintained correctly the eligible-base rate is an unbiased estimator of 0.18 in every month, so a systematic offset means the bookkeeping is wrong, not that the simulation found something.

Worked solution (30 min)

rng = np.random.default_rng(0); p = [0.30] + [0.70/11]*11; next_renewal = rng.choice(12, size=1200, p=p); alive = np.ones(1200, bool)
For m in range(24): eligible = alive & (next_renewal == m); churn = eligible & (rng.random(1200) < 0.18); record churn.sum(), eligible.sum(), alive.sum() at month start; alive &= ~churn; next_renewal[eligible & ~churn] += 12
naive = churned / live_at_start; eligible_rate = churned / eligible, left as NaN where eligible == 0.
Report naive.mean(), naive.std(ddof=1), np.nanmean(eligible_rate), np.nanstd(eligible_rate, ddof=1) and the ratio of the two means.

Expected result: The eligible-base rate averages close to 0.18 (roughly 0.17 to 0.19 at this sample size) with no January spike. The naive rate averages near 0.015, about one twelfth of it, and its January values run several times a typical month, so its month-to-month standard deviation is a large fraction of its own mean.

Verify it yourself

The 24 monthly churn counts sum to 1200 minus the final alive count.
Switching the calendar to uniform (p = 1/12 everywhere) collapses the naive series' dispersion while leaving the eligible-base mean unchanged.
Setting the churn probability to 0 makes both series identically zero and leaves 1,200 accounts alive.
Quadrupling the account count roughly halves the standard deviation of the eligible-base rate without moving its mean.

Follow-up

Compounded over twelve months the naive rate lands close to the true annual churn. Does that rescue it?
How would you report churn in a month where only nine accounts were renewal-eligible?
Eighteen-month terms are now being sold alongside annual ones. What breaks?

Running commitment burn-down and the date consumption crosses it

Medium · window functions · running totals · committed consumption

fct_subscription_period gives committed_amount_cents, term_start_date and term_end_date for each account's current version where pricing_model = 'committed_consumption'. fct_usage_daily gives account_id, workspace_id, sku_code, usage_date and net_amount_cents, at one row per workspace and SKU per day. Inside each account's current term, return the running total of net_amount_cents by usage_date, the first usage_date on which that running total reaches committed_amount_cents, and the fraction of the term elapsed at that point. Accounts that have not reached their commitment must still appear, with a null crossing date.

Approach

Collapse usage to one row per (account_id, usage_date) first. The fact is grained by workspace and SKU, so a raw running total leaves several rows per date and the first-crossing date becomes dependent on the arbitrary order of rows inside that day.
Restrict to the term with usage_date BETWEEN term_start_date AND term_end_date on the account's current row, so no prior term's consumption leaks into this term's burn-down.
Compute SUM(net_cents) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). Name ROWS explicitly: the default frame is RANGE, which includes all peer rows at the same ORDER BY value and would hand back the whole day's total on each of that day's rows.
Extract the crossing with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) grouped per account, which returns NULL for accounts still under commitment instead of dropping them.
Elapsed fraction is (crossing_date - term_start_date)::numeric / NULLIF(term_end_date - term_start_date, 0); Postgres date subtraction yields whole days, so the guard matters for same-day terms.
Exclude the trailing days still inside the metering settling window, measured from first_written_at against restated_at, and say how many days you cut and why.

Worked solution (30 min)

Build terms: SELECT account_id, committed_amount_cents, term_start_date, term_end_date FROM fct_subscription_period WHERE is_current AND pricing_model = 'committed_consumption' AND committed_amount_cents IS NOT NULL.
Build daily: join fct_usage_daily to terms on account_id with usage_date inside the term, then GROUP BY account_id, usage_date summing net_amount_cents.
Add running_cents with SUM(...) OVER (PARTITION BY account_id ORDER BY usage_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).
Aggregate per account with MIN(usage_date) FILTER (WHERE running_cents >= committed_amount_cents) AS crossing_date, then compute the elapsed fraction with the NULLIF guard.
LEFT JOIN that summary back onto terms so every committed account appears, and verify the last running_cents per account equals a plain SUM over the term.

Expected result: A per-account-per-day series of running_cents inside the current term, plus one summary row per committed account carrying crossing_date and term_elapsed_fraction, with crossing_date NULL exactly for accounts whose final running total is below committed_amount_cents.

Verify it yourself

The maximum running_cents per account equals SUM(net_amount_cents) over that account's term computed independently.
crossing_date is NULL if and only if the final running total is below the commitment.
term_elapsed_fraction falls in [0, 1] for every non-null crossing date.
running_cents is non-decreasing within each account, which fails immediately if a negative credit row was included without comment.

Follow-up

Turn this into an end-of-term overage forecast. What breaks if you extrapolate a linear run rate on a consumption product?
An account crosses its commitment at 40 percent of the term. Is that an expansion signal or a billing-surprise risk, and what would you check to tell them apart?

Net revenue retention on a cohort frozen twelve months back

Hard · retention · as-of join · cohort arithmetic

fct_subscription_period carries subscription_period_id, account_id, arr_cents, plan_code, term_start_date, term_end_date, booked_at, amendment_type, superseded_by_id (the subscription_period_id of the version that replaced this one, null on the live version of a lineage) and is_current. Compute net revenue retention for month M: the summed arr_cents at M for the set of accounts holding arr_cents > 0 at M-12, divided by that same set's arr_cents at M-12. An account can hold more than one live subscription, a churned account contributes zero rather than dropping out, and nothing signed after M-12 may enter either side. Return the ratio plus the expansion, contraction and churn components in cents.

Approach

Write one reusable as-of ARR snapshot parameterised by a date: rows whose term brackets the date AND whose booked_at is at or before the date, then only the version of each lineage that is still live at that date, then sum arr_cents per account. The booked_at guard matters because an amendment signed in advance otherwise co-exists with the term it replaces and double counts the account.
Collapse the lineage on superseded_by_id, not on any attribute of the contract. Keep a row when superseded_by_id IS NULL, or when the successor it points at was booked after the date. Ranking with ROW_NUMBER() OVER (PARTITION BY account_id, plan_code ...) instead is wrong in both directions: an amendment that moves the account from one plan_code to another puts the old and new versions in different partitions, so both are rank 1, both bracket the date, and the account's arr_cents is counted twice; and two genuinely concurrent subscriptions that happen to share a plan_code land in one partition, so one of them is deleted.
Resolve the successor with a LEFT JOIN back to fct_subscription_period on subscription_period_id, and treat a missing successor as not superseded. An inner join would silently delete an account's ARR on a dangling pointer, which is a data-quality bug in the source, not a retention movement.
Never use is_current for the M-12 side. is_current describes today; using it at the historical snapshot backdates the present contract onto last year's cohort and makes retention look like 100 percent by construction.
Freeze the cohort from the M-12 snapshot where arr_cents > 0, then LEFT JOIN the M snapshot onto it and COALESCE the missing side to zero. An inner join deletes exactly the churned accounts, which is the single largest way this number gets overstated.
Return a ratio of sums, not a mean of per-account ratios. The two are different estimands: contraction is floored at zero while expansion is unbounded, so the mean of ratios is both biased relative to the aggregate and far noisier on a skewed revenue base.
Decompose per account on the delta: positive delta is expansion, negative delta with a non-zero M value is contraction, a zero M value is churn. The three components must reconcile to numerator minus denominator.
Prove no leakage: any account whose first contract began after M-12 must be absent from both sides, and the cohort row count must be identical in the numerator and denominator.

Follow-up

Net revenue retention can rise while the business shrinks. Show one mechanism and name the guardrail that catches it.
How do you handle an account that co-terms two subscriptions into one mid-window, so the subscription count changes but the money does not?
Finance computes this from invoiced amounts and gets a different number. Which is right for which question?

How would you optimize a script that is running too slowly on large datasets?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
Name one primary metric, then the guardrail that stops it being gamed.
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?
What would you do if the primary metric and the guardrail moved in opposite directions?

Explain the time complexity of common data structures like dictionaries or lists.

Medium

Approach

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

Follow-up

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

Cut variance with pre-period usage before the test starts

Medium · cuped · variance-reduction · stratification

You are planning an account-randomised test on 2,800 paying accounts. The outcome is a 28-day sum of billable_quantity for sku_code = 'compute_hours' from fct_usage_daily, and the same account's 28-day pre-period sum correlates 0.80 with it. 420 accounts were created inside the pre-period and have partial or no history. Specify the variance-reduction plan: the adjusted estimator and where its coefficient comes from, how assignment is stratified, how the 420 incomplete accounts are handled, and what must change in the pre-period window given that fct_usage_daily rows are restated after first write.

Approach

Write the estimator explicitly: Y_adj = Y - theta * (X - mean(X)), with theta = Cov(X, Y) / Var(X). Estimate theta from pre-experiment history or pooled across arms, never separately by arm and never from post-treatment outcomes. Fitting theta on treatment-arm outcomes folds the effect being measured into the adjustment and biases the result toward whatever the treatment did.
Quantify the gain and convert it into the currency the team cares about. The residual variance multiplier is 1 - 0.80^2 = 0.36, so the standard error falls to 0.60 of its unadjusted value and the MDE falls with it. That is the same precision as running with 1 / 0.36, about 2.8 times as many accounts, which matters because the account population is fixed and cannot be bought with a longer run.
Stratify assignment on pre-period usage decile crossed with the three paid plan tiers, giving 30 cells at roughly 93 accounts each, and collapse any cell below about 20. Use the same strata in the analysis through strata fixed effects or post-stratification: stratified assignment analysed pooled discards much of the gain, and stratified analysis without stratified assignment risks empty cells in the top decile, which is precisely where the revenue sits.
Handle the 420 incomplete accounts by imputing X at the stratum mean and adding a binary indicator for missing pre-period, rather than dropping them. Dropping silently redefines the population to established accounts, which is usually the opposite of the segment a new feature targets, and it makes the result non-generalisable in a way the readout will not disclose.
Fix the window against restatement. Measure the empirical settling time by comparing a usage_date's total at first_written_at against its value after restated_at has stopped moving, then end the pre-period that many days before assignment. A pre-period whose last days are still settling carries recency-correlated measurement error in the covariate, which both weakens rho and can correlate with assignment date.
Pre-register the whole plan before assignment: the estimation set for theta, the strata definition and collapsing rule, the imputation rule, the winsorisation cap and the trailing exclusion. Every one of these can be tuned after the fact to move a p-value, which is why they are worth nothing if decided afterwards.

Worked solution (30 min)

Estimate rho on a historical pair of adjacent 28-day windows, applying exactly the traffic-class and is_internal filters the live experiment will use.
Compute the variance multiplier 1 - 0.80^2 = 0.36 and translate it to a standard-error multiplier of 0.60 and an effective-sample multiplier of about 2.8.
Build 30 strata from ten pre-period usage deciles crossed with three paid plan tiers, inspect the minimum cell count and collapse cells below about 20 accounts.
Measure the metering settling time from first_written_at against restated_at and shift the pre-period window back by that many days.
Write the pre-registration: theta source, strata, imputation for the 420 incomplete accounts, winsorisation cap, trailing exclusion.

Expected result: Standard-error multiplier 0.60 and effective-sample multiplier about 2.8 at rho = 0.80, with 30 strata collapsed where thin, the 420 incomplete accounts retained through stratum-mean imputation plus a missingness indicator, and the pre-period ending one measured settling window before assignment.

Verify it yourself

Adjusted and unadjusted point estimates must be close under valid randomisation, because this reduces variance rather than moving the estimate. A large shift means theta was fitted on post-treatment data or that randomisation itself failed.
The multiplier 1 - rho^2 is monotone in the absolute correlation and equals 1 at rho = 0, so at rho = 0.30 the standard error can only fall by about 5%. Any larger claimed reduction at that correlation is arithmetically wrong.
Computing the pre-period sum twice, a week apart, should return the same number once the settling window is excluded. If it does not, the exclusion window is too short.

Follow-up

Once continuous-integration and synthetic traffic are excluded, rho turns out to be 0.45 rather than 0.80. What is the revised variance reduction, and is the added complexity still worth it?
How would you extend this to more than one covariate, and what stops you from adding twenty?
Does this adjustment repair an imbalance you discover after assignment, or only reduce variance? Be precise about the difference.

Net revenue retention jumps sixteen points in one month

Medium · versioned tables · fan-out · metric definition

Trailing-twelve-month net revenue retention printed around 108 percent for months and now reads 124 percent, with no unusual deals closed. The query sums arr_cents from fct_subscription_period (account_id, arr_cents, term_start_date, term_end_date, amendment_type, superseded_by_id, is_current, booked_at) filtered on is_current = true at month M, across accounts holding arr_cents > 0 at month M-12. Find the defect, correct the number, and rewrite the definition so the next person cannot reintroduce it.

Approach

Audit the grain before the arithmetic: count account_ids holding more than one row with is_current = true and superseded_by_id null. A versioned contract table that double counts one amendment batch inflates the numerator while leaving the denominator untouched.
Replace is_current with an as-of selection on both dates, taking the version whose term_start_date and term_end_date bracket the reporting date and tie-breaking on latest booked_at. The numerator is read as of M and the denominator as of M-12; neither uses today's live version.
Verify the cohort is frozen. The account set is fixed at M-12 and nothing acquired since may enter the numerator, so check that no join to a current-period table quietly re-admits new accounts.
Confirm the estimand is a ratio of sums rather than a mean of per-account ratios. Contraction is floored at zero while expansion is unbounded, so the two constructions differ systematically and the second is far noisier.
Reissue the definition with the failure modes written into it: exactly one row per account per date by construction, cohort frozen at M-12, churned accounts contributing zero rather than dropping out of the numerator.

Follow-up

A churned account should contribute zero rather than disappear. What does the ratio do under each treatment, and which one is correct?
How would you unit-test this metric so a future amendment batch with the same defect fails a check instead of reaching a board slide?

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.

Stories where someone disagreed with you

Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.

How do you handle imbalanced datasets in a production environment?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Pick a story where you drove the decision, not one where you observed it.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

How did you know the outcome was caused by your change?
What would you do differently if you ran that project again?

Allocate one analyst week across three competing requests

Easy · prioritisation · stakeholders · scoping

Three requests arrive the same morning and you have one week. Finance wants per-account gross margin from fct_usage_daily for a pricing review in three weeks. Sales wants a renewal-risk list for accounts with term_end_date inside 60 days. A product manager wants an experiment readout for a decision being taken on Thursday. Produce your allocation with hours attached, what you say to whoever receives less, and one thing you refuse to do this week, with the reason each decision is defensible to the person it costs.

Approach

The interviewer is probing whether you prioritise on decision timing and reversibility or on who asked most forcefully. Sort by the date each decision is actually taken and by what the default outcome is if nothing arrives.
Apply that sort concretely. The Thursday readout has a hard irreversible deadline and no value afterwards. The pricing review has three weeks of slack. The renewal list has a rolling deadline set by term_end_date, so part of it is urgent this week and the rest is not, which means it can be split rather than deferred whole.
Find the cheapest sufficient version of each request rather than the full version. The readout goes in full. The renewal list ships as a filtered query over renewal-eligible accounts ranked by two inspectable signals rather than as a model. The margin work is scoped to the accounts that dominate the pricing decision, since revenue is heavily skewed and the tail will not change the conclusion.
Make the trade visible in one written note to all three at once, with dates. Telling each person separately that they are the priority is how an allocation becomes a credibility problem.
Refuse something explicitly and say why. The model version of the renewal list is the usual candidate, because it cannot be evaluated without a holdout nobody has agreed to yet, and building it this week forecloses that.
Leave slack. A plan with none is a plan to miss the one deadline that cannot move.

Follow-up

The sales leader escalates to your manager. What did you already do that makes that a short conversation?
Which of the three deadlines would you push back on, and what exactly would you ask for?
What would you change about how these requests reach you so next week is not the same?

Defend a churn number twelve times the one in the board deck

Hard · stakeholder management · retention metrics · definitions

You recompute logo churn on the renewal-eligible base from fct_subscription_period, counting only accounts whose term_end_date fell in the month and allowing a 45-day grace for late paperwork. Annualised, about 14 percent of accounts that reach a renewal date do not renew. A revenue leader has been quoting 1.2 percent to the board for two quarters, computed by dividing non-renewed accounts by the entire customer base in each month and printing that monthly figure with no period attached. You have 20 minutes with that leader and the finance lead. Decide which number is reported from now on, and what happens to the two quarters already published.

Approach

The interviewer is probing whether you can hold a correct definition under social pressure without turning it into a competence dispute. Open by reproducing their 1.2 percent exactly, with their denominator and their months, so the disagreement is arithmetic both sides can see rather than a claim about who was careless.
Separate the two defects, because they are different in kind. The denominator is wrong: on annual contracts only about one twelfth of the base reaches a renewal date in any month, so an account eleven months from renewal sits in the denominator while being structurally incapable of entering the numerator, which suppresses the rate by a factor near twelve. The period is merely unstated: a monthly figure printed beside annual revenue targets gets read as an annual rate.
Say out loud that those two defects nearly cancel in the level, before the leader finds it. Twelve times 1.2 percent is about 14 percent, which is your number. That is the strongest thing you can say in the room, because it proves both figures rest on the same non-renewal count and moves the meeting onto which denominator and which period get published rather than onto whose query is right.
The level is recoverable; the series is not. Non-renewals in a month are the eligible base for that month times the churn rate, so dividing by a fixed whole base makes the published line proportional to how many contracts happen to come up that month. Where signings cluster at quarter ends, the eligible base in a quarter-end month can be several times a quiet month's, and the month-over-month moves the board has been reading as satisfaction are the signing calendar.
Separate the measurement change from a business change. Nothing got worse this week; the loss rate was always this. Bring net revenue retention over the same period as a ratio of sums on a cohort frozen twelve months earlier, because logo churn concentrated in small accounts can sit beside healthy revenue retention, and that combination is the actual story.
Offer a migration path rather than a correction. Report both rates for one quarter with a written bridge, restate the prior two quarters in an appendix instead of silently, and pin the definition, including the period it is stated over, somewhere finance and product both read it. Concede the limits of your own number: the 45-day grace means the most recent 45 days are not reportable, and churn must be dated on term_end_date rather than on updated_at. A strong answer volunteers this; a generic one only defends.

Follow-up

The leader multiplies their monthly figure by twelve, lands on your annual number, and concludes nothing was ever wrong. What do you say?
The leader says publishing the corrected rate costs the team its credibility with the board this quarter. What do you do?
Gross logo retention worsened while net revenue retention improved. Which do you lead with, and what does the combination tell you about who is leaving?