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Alexander Thamm Data Scientist Interview Questions & Guide 2026

Alexander Thamm data scientist prep: probability, sampling distributions, and stating the assumption behind every test you run.

The 60-second brief

A Data Scientist at Alexander Thamm serves as a vital bridge between complex data landscapes and actionable business strategy. As a consultancy-focused organization, this role requires more than just technical proficiency; it demands the ability to translate ambiguous client challenges into rigorous analytical solutions. You will be expected to operate in dynamic environments, often moving between different industries and problem spaces to deliver high-impact insights.

The boundary with engineering varies enough to be worth asking about directly. Some seats end at the analysis and the writeup; others carry feature pipelines, scheduling and alerting, which decides whether production data plumbing belongs in your prep at all.

Focus areas

Separate bookings, recognised revenue and collected cash
Reconstruct pipeline stage history from mutable rows
Compute utilisation against a defended availability denominator

Overview

Your work will directly influence how clients leverage their data, ranging from predictive modeling to the design of robust experimentation frameworks. Success in this role requires a blend of deep mathematical understanding, engineering discipline, and a client-facing mindset. You are not just building models; you are acting as a strategic advisor who ensures that data-driven decisions are backed by statistical integrity and clear product logic.

The Alexander Thamm interview process

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

Initial 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 Rounds

This round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.

What to demonstrate

Whether your row counts survive each join, and whether you notice on your own when they do not
Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
Reaching a defensible answer inside the window instead of a refined one after it

How to prepare

Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither

HR Conversations

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

Using calendar hours, or a current rate card, as the denominator

Available hours is not days x 8. It must subtract approved leave and region-specific public holidays, scale by fte_fraction, and prorate for hire_date and termination_date inside the period; skipping this produces cross-region utilisation gaps that are purely holiday-calendar artifacts and a fake dip every time a cohort of new hires starts. Equally, bill and cost rates change over time, so joining today's dim_consultant row to last year's hours restates history. Use the rate snapshotted on the time entry, or join dim_consultant on work_date BETWEEN effective_from AND COALESCE(effective_to, 'infinity').

Computing days-to-pay or proposal cycle time over completed records only

At any snapshot date, invoices that have already been paid are disproportionately the fast ones, and proposals that already have a decision are disproportionately the quick ones. Averaging over the completed set alone biases both numbers downward, and the bias grows exactly when the business is deteriorating, because the slow cases are the ones still open. Unpaid and undecided records are right-censored; use Kaplan-Meier or a restricted mean up to a fixed horizon, and never fill paid_at with a placeholder.

Ending an analysis without a recommendation or next step

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

Analysing at a different unit than the one randomised

Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.

Questions & practice

Attach effective-dated cost rates without merge_asof

Medium · type-2 scd · interval join · searchsorted

time_entries (consultant_id, work_date, hours) has roughly two million rows. dim_consultant is a type-2 dimension (consultant_sk, consultant_id, standard_cost_rate_usd, effective_from, effective_to nullable, is_current) with a handful of rows per consultant. Attach the cost rate in force on each entry's work_date. You may not use pandas.merge_asof and you may not materialise the consultant_id cartesian product. Treat intervals as half-open, meaning [effective_from, effective_to), with a NULL effective_to open-ended. Return unmatched entries separately with a reason rather than dropping them.

Approach

Fix the semantics before writing code: half-open intervals are what make the join single-valued on the day a rate changes. Compare work_date at start-of-day in the reporting timezone against effective_from in the same zone, and write that comparison down as a stated assumption.
Sort dim_consultant by (consultant_id, effective_from) and assert non-overlap inside each consultant, since searchsorted only returns the right answer if the intervals are ordered and disjoint.
Build the composite sort key so it cannot overflow: dense-code the consultant ids through one shared sorted category mapping, express time as whole days since a floor date earlier than every date in both frames, and take SCALE as a power of two above the largest day offset, giving key = code * SCALE + offset. Assert code.max() * SCALE + off.max() < np.iinfo(np.int64).max before using it. Multiplying a raw consultant_id by a scale larger than a nanosecond timestamp (about 1.75e18 for present-day dates) leaves an id budget of only int64_max / SCALE, roughly five consultants, before the product passes 9.22e18; beyond that numpy wraps silently, and it does not even wrap consistently negative (with SCALE = 2e18 an id of 12 lands back on a large positive key), so the order breaks without a sign flip to catch it.
With a key that fits, np.searchsorted(dim_key, entry_key, side='right') - 1 is the index of the last interval starting at or before work_date, in O((n + k) log k) and with no cartesian product. SCALE only has to exceed the span of the offsets, never an absolute epoch value, so if a rate can change intraday, switch the offset unit to seconds (a 2^31 scale covers 68 years) and re-run the same assertion.
Validate the candidate rather than trust it: the match holds only if work_date < effective_to or effective_to is NULL. Everything else is an exception, either work logged before the first dimension row or a genuine gap in the SCD.
Return matched rows carrying consultant_sk and the rate, plus an exceptions frame with counts by reason, so a gap in the dimension surfaces as a number rather than as quietly missing cost.

Follow-up

Rates were backdated a quarter after the fact. How do you keep last quarter's published cost from moving when someone reruns the report?
How does the implementation change if effective_to is inclusive rather than exclusive, and what breaks on the boundary day if you get that wrong?

Implement billable utilisation against a defended availability denominator

Medium · metric implementation · denominators · proration

Implement billable utilisation for one calendar month at consultant grain, then roll it up to practice_area. Inputs: time_entries (consultant_id, work_date, hours, is_billable, charge_code, status), consultants (consultant_id, practice_area, fte_fraction, home_region, is_billable_role, hire_date, termination_date) and holidays (region, holiday_date). Numerator is approved hours with is_billable true. Denominator is scheduled workdays in the month, clipped to [hire_date, termination_date] and net of that region's holidays, times 8 times fte_fraction, minus approved leave hours. Roll up as sum(numerator) / sum(denominator).

Approach

Build the workday set per region first: business days in the month minus that region's holiday_dates. State the Monday-to-Friday assumption out loud, because a region with a different working week makes np.busday_count wrong rather than approximate.
Prorate by clipping each consultant's interval to [max(month_start, hire_date), min(month_end, termination_date or month_end)] and counting workdays inside the clipped interval, so a mid-month start produces a smaller denominator instead of a fake dip in the ratio.
Subtract approved leave hours (charge_code in leave_paid, leave_unpaid) only for leave falling on days already counted as workdays; leave logged on a public holiday would otherwise be subtracted twice and can push a denominator negative.
Filter to is_billable_role, then drop consultant-months whose denominator is zero or negative (full-month leave, a hire on the last day) rather than emitting inf or NaN, and return the dropped count as part of the result.
Roll up by summing numerators and denominators separately. Averaging per-person ratios gives a 0.2 FTE consultant the same weight as a full-time one and produces a practice number that no individual reconciles to.

Follow-up

Utilisation rose two points this month while realisation fell. Reconcile those two movements with one mechanism.
A regional lead says their team looks four points below another region. What do you check before you answer, and in what order?

Collapse time entries into contiguous staffing spells

Medium · sessionisation · gap and island · vectorisation

From approved delivery time entries (consultant_id, engagement_id, work_date, hours, charge_code, status, with engagement_id not null), build staffing spells. Per consultant and engagement, collapse weeks containing any logged hours into contiguous runs, where three or more consecutive zero-hour weeks end a spell. Output one row per spell with consultant_id, engagement_id, start_week, end_week, active_weeks, gap_weeks and total_hours. Consultants sit on several engagements at once, so spells from different engagements may overlap in time and must not be merged. No Python loop over rows.

Approach

Aggregate to (consultant_id, engagement_id, week_start) with summed hours and keep only weeks with positive hours. The absent weeks are the signal, so materialising zeros here would destroy the thing you are detecting.
Convert week_start into an integer week index, ((week_start - epoch_monday).dt.days // 7), so gap detection is integer subtraction rather than calendar arithmetic that breaks over month and year boundaries.
Sort by [consultant_id, engagement_id, week_index], diff the week index inside each pair, and mark a spell start where the diff is null (first row of the pair) or greater than 3. A diff of 1 is adjacent weeks and a diff of 3 is two empty weeks, which the tolerance permits.
Take spell_id = the cumulative sum of that boolean over the whole frame so ids are globally unique, then a single groupby on [consultant_id, engagement_id, spell_id] yields min and max week, active week count and summed hours; gap_weeks = (end - start + 1) - active_weeks.
Do not deduplicate overlapping spells across engagements. A consultant on two engagements in the same week is the normal case, and that overlap is the fact any capacity or context-switching question needs.

Worked solution (30 min)

Filter to status == 'approved', charge_code == 'client_delivery' and engagement_id.notna(); derive week_start by subtracting the weekday offset from work_date.
wk = df.groupby(['consultant_id','engagement_id','week_start'], as_index=False).hours.sum(); wk = wk[wk.hours > 0]; wk['wi'] = (wk.week_start - pd.Timestamp('1970-01-05')).dt.days // 7.
wk = wk.sort_values(['consultant_id','engagement_id','wi']); d = wk.groupby(['consultant_id','engagement_id']).wi.diff(); wk['new_spell'] = d.isna() | (d > 3); wk['spell_id'] = wk.new_spell.cumsum().
spells = wk.groupby(['consultant_id','engagement_id','spell_id']).agg(start_week=('week_start','min'), end_week=('week_start','max'), active_weeks=('wi','size'), total_hours=('hours','sum'), span=('wi', lambda s: s.max() - s.min() + 1)).reset_index(); gap_weeks = span - active_weeks.
Run the conservation assertion (spell hours sum to input hours) and the toy case below before returning.

Expected result: One row per spell, where a single pair with active week indices [1, 2, 5, 9] produces two spells: weeks 1 to 5 with active_weeks 3 and gap_weeks 2, and week 9 alone with active_weeks 1 and gap_weeks 0.

Verify it yourself

Summed total_hours across spells equals the summed hours of the filtered input, exactly.
Within a pair, spells are disjoint, ordered, and separated by at least three empty weeks.
No spell contains an internal gap longer than two weeks, verified by re-expanding one spell against the week-level frame.
Two consultants with identical week patterns on the same engagement produce two separate spells, not one.

Follow-up

Re-run with a one-week and a four-week tolerance. What happens to the spell count, and which tolerance would you defend to a staffing lead?
Using these spells, how would you measure how many engagements a consultant is split across in a given week, and why is that not just a count of rows?

How would you handle missing data or outliers when preparing a dataset for modeling?

Medium · sql and data manipulation

Approach

State the window function and its partition and ordering out loud before writing it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.

Follow-up

How would you verify this result without re-running the same query?
What breaks if events arrive late or out of order?

How do you use SQL window functions to calculate running totals or moving averages?

Medium · sql and data manipulation

Approach

Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Say which table is the grain you start from, and join outward from it.
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?

Engagement margin without fan-out across two fact tables

Medium · fan-out · join grain · mix shift

fct_engagement holds engagement_id, pricing_model and contract_value_usd. fct_invoice_line holds engagement_id, line_type, amount_usd and status. fct_time_entry holds engagement_id, hours, cost_rate_usd, is_billable and status. For engagements closed last year, return fees (line_type IN ('fees','milestone','credit_note') and status <> 'draft'), delivery cost (SUM(hours * cost_rate_usd) over approved entries, billable and non-billable alike) and gross margin, grouped by pricing_model. A single SELECT joining the engagement to both fact tables and then aggregating produces wrong numbers: say what the error is, then write the correct query.

Approach

Do the arithmetic out loud. An engagement with 40 qualifying invoice lines and 900 approved time entries yields 36,000 rows on the double join: SUM(amount_usd) comes back 900 times too large and SUM(hours * cost_rate_usd) 40 times too large. The two inflation factors are different, so the ratio moves too — per engagement the double join replaces the cost-to-fee ratio with (cost / fees) * (line count / entry count), so a true 30 percent margin reads as 1 - 0.7 * (40 / 900), about 97 percent.
Know which shape of this bug actually survives review. The ratio is preserved only where an engagement carries the same number of qualifying invoice lines as approved time entries, and then only as a row-count-weighted margin, 1 - SUM(cost_i * k_i) / SUM(fees_i * k_i), which still differs from the true pooled margin unless k_i is constant across engagements. So you get either a total absurd enough to tempt someone into a scaling patch, or, on a population where the two counts happen to track each other, a believable-looking ratio sitting on fees and cost that are each wrong by orders of magnitude.
Aggregate each fact table to engagement grain in its own CTE, then LEFT JOIN both onto fct_engagement. One row per engagement then holds by construction rather than by inspection.
Include non-billable delivery hours in cost. Rework, unbilled travel and pursuit time on a live account are real fully-loaded cost, and excluding them flatters fixed-fee work specifically, which is the mix you most need to see clearly.
Treat zero and NULL as different: an engagement with no invoice lines has undefined margin, not zero. Divide by NULLIF(fees, 0) and keep those engagements in a labelled bucket instead of letting an inner join hide them.
Group by pricing_model before reporting any firm-wide figure, and publish the fee mix next to it. Fixed-fee margin falls as hours rise, uncapped time-and-materials margin does not, so a shift in what was sold moves the pooled number with no change in delivery at all.

Worked solution (30 min)

Compute standalone control totals: total fees over the filtered invoice-line set and total cost over the approved time-entry set, both for the closed-engagement population.
Build fees_by_engagement and cost_by_engagement as separate CTEs at engagement grain.
LEFT JOIN both onto fct_engagement, compute margin with NULLIF on the denominator, and group by pricing_model.
Run the naive double join on a single engagement and confirm its fee total equals the true total times that engagement's time-entry count, and its cost total the true cost times its invoice-line count.
Report the fee mix by pricing_model alongside the margin column.

Expected result: One row per pricing_model with total fees, total cost, gross margin as (fees - cost) / fees, and the share of fees that model represents. Engagements with fees of zero or NULL are reported as a separate count rather than folded into the ratio.

Verify it yourself

Total fees across the grouped output equals the standalone control total for fees; the same holds for cost.
Joining the two CTEs and counting rows returns exactly the number of qualifying engagements.
The naive double join on one engagement reproduces both predicted multipliers, confirming the diagnosis rather than assuming it.
Margin computed per pricing_model and then fee-weighted back together equals the pooled margin over the same population.

Follow-up

Decompose a period-over-period margin move into a within-pricing-model component and a between-model mix component, and show the two sum to the total.
Where does not_to_exceed_usd change the time-and-materials picture, and how would you find engagements that crossed it?
Roll this up to the account through parent_client_id. What must the recursive CTE guard against?

How do you balance short-term optimization with long-term product health?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
Decompose the metric into the rates that drive it, and say which one you would check first.
Restate the decision this analysis has to support, and who acts on the answer.

Follow-up

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 product metric to track the success of a new feature?

Medium · product sense and metrics

Approach

Decompose the metric into the rates that drive it, and say which one you would check first.
State what result would change your recommendation, so the answer is falsifiable.
Fix the population and the time window before naming any metric.

Follow-up

Which segment would you cut first, and what would that rule out?
What would you do if the primary metric and the guardrail moved in opposite directions?

How do you define success for a recommendation system in a retail context?

Medium · product sense and metrics

Approach

State what result would change your recommendation, so the answer is falsifiable.
Restate the decision this analysis has to support, and who acts on the answer.
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?

A key engagement metric has dropped suddenly; how would you diagnose the root cause?

Medium · product sense and metrics

Approach

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

Follow-up

What would you do if the primary metric and the guardrail moved in opposite directions?
Which segment would you cut first, and what would that rule out?

What is the difference between statistical significance and practical significance?

Medium · a/b testing and experimentation

Approach

Decide the analysis before seeing data, including how long it runs and when you look.
State the primary metric and the minimum effect worth shipping, then size the test.
Say whether units interfere with each other, and switch design if they do.

Follow-up

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

What are the common experimentation pitfalls that lead to biased results?

Medium · a/b testing and experimentation

Approach

Name the randomisation unit first; it decides the variance and what the test can detect.
Say whether units interfere with each other, and switch design if they do.
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 conclude if the result is positive but the test is underpowered?

Define success for a rate-card increase before any rollout

Hard · pricing · decision threshold · clustering · power

A 6% rate-card increase is proposed for one practice area next quarter. Before it ships, define what success means. Available: fct_proposal (expected_value_usd, stage, loss_reason, is_competitive, submitted_at, decided_at, practice_area), fct_engagement (pricing_model, contract_value_usd, practice_area, client_id) and fct_invoice_line (line_type, amount_usd, billed_hours). Name the primary metric, the guardrail that genuinely opposes it, the unit of analysis, and the numeric threshold that would reverse the decision. State how many account-quarters are needed to detect an effect large enough to change the decision, and what you do if that exceeds what the practice has.

Approach

Make the primary realised revenue per billable hour, not the list rate. The increase can be conceded straight back through discount and show up as no change at all. Numerator: SUM(amount_usd) on line_type IN ('fees','milestone'). Denominator: SUM(billed_hours) over the same lines.
Name the opposing guardrail: value-weighted competitive win rate in that practice area, with the share of losses carrying loss_reason = 'price' as the supporting diagnostic. Raising price lifts the primary and depresses the guardrail by construction, so the rollout pays off only if revenue per hour rises faster than volume falls.
Derive both break-evens before looking at outcome data. Revenue is preserved while the volume loss stays under 6 / 106, roughly 5.7%. For margin, write p for realised price per billable hour and c for avoidable cost per billable hour, and require c < p so the baseline margin is positive. Setting post-change margin (1 - L) x (1.06p - c) equal to pre-change margin (p - c) gives 1 - L = (p - c) / (1.06p - c), so the break-even volume loss is L = 0.06p / (1.06p - c). That is strictly above 5.7% whenever c > 0, collapses to 5.7% at c = 0, and stays below 100% as long as c < p; a practice already below cost at the old rate is not a pricing question. It holds only where c is genuinely avoidable on the volume you lose; on a salaried bench that nobody stands down or resells, no cost is saved and the margin break-even falls back to the revenue one. Report both ends and name the assumption the threshold uses.
Fix the unit and confront the identification problem. Assignment is at practice_area while the outcome sits at engagement or account, so cluster at client_id. With one treated practice area there is a single treated cluster and a conventional difference-in-differences is not identified; propose a synthetic control over the other practice areas with the donor-pool condition stated, or a pre-registered before-and-after whose parallel-trends check runs on the pre-period with a named failure action.
Run the power calculation before proposing the rollout and be willing to conclude the practice cannot detect the effect. Competitive decisions in one practice area number in the tens per quarter, so a 3-point win-rate move needs many quarters. If the requirement exceeds what exists, the honest outputs are a staged rollout with a pre-committed stopping rule, or shipping without measurement and saying so, not a manufactured p-value.

Worked solution (40 min)

Compute realised revenue per billable hour by practice-quarter for eight pre-period quarters from fct_invoice_line fees and milestone lines over billed_hours.
Compute value-weighted competitive win rate per practice-quarter, cohorted on decided_at, denominator stage IN ('won','lost'), is_competitive = TRUE.
Derive the two break-even volume losses: 6 / 106 for revenue, and 0.06p / (1.06p - c) for margin using the practice's realised price and avoidable cost per billable hour, then recompute with c treated as unavoidable to get the other end of the range.
Count competitive decisions per quarter in the practice and compute the minimum detectable win-rate change at 80% power from a two-proportion test, treating that as a floor because clustering raises the real requirement.
Write the rule: threshold, horizon, stopping condition, and the action taken when the power calculation says the effect is undetectable.

Expected result: Primary metric realised revenue per billable hour; opposing guardrail value-weighted competitive win rate. Revenue break-even volume loss 6 / 106, roughly 5.7%. Margin break-even 0.06p / (1.06p - c), strictly larger than 5.7% for any positive avoidable cost per hour and equal to 5.7% when that cost is zero or unavoidable; the reversal threshold sits in that range with the avoidability assumption stated. Unit of analysis engagement or account, clustered at client_id, with the single treated practice area named as the identification problem and a synthetic control or pre-registered before-and-after proposed in response.

Verify it yourself

With c < p, the margin break-even volume loss sits between 5.7% and 100%, equalling 5.7% exactly at c = 0. If you have written (p - c) / (1.06p - c) you are holding the retained-volume fraction, the complement of the answer: at c = 0 it returns 94.3% rather than 5.7%.
The power calculation runs on the count of competitive decisions, not on engagements or invoice lines, since the win-rate guardrail is what triggers the reversal.
The parallel-trends check is specified with a stated failure action, not merely mentioned as a caveat.

Follow-up

Realised revenue per hour rose 4% and win rate fell 2 points on 31 competitive decisions. What do you conclude, and what is your interval on the win-rate move?
How would you separate a price-driven loss from a capability-driven one when loss_reason is entered by the partner who lost the deal?

Two-year-old engagement margins moved after a dimension join change

Hard · type-2 scd · restatement · effective dating

After a warehouse release, gross margin on engagements closed two years ago changed by up to six points, upward on some and downward on others. The cost model now joins dim_consultant on consultant_id AND is_current = TRUE to read standard_cost_rate_usd, instead of using the cost_rate_usd already snapshotted on each fct_time_entry row. Available columns include consultant_sk, consultant_id, level, standard_cost_rate_usd, effective_from, effective_to, is_current, termination_date. Explain the restatement, quantify who it favours, and specify the join you would ship.

Approach

State the mechanism in one line: joining a type-2 dimension on is_current pastes today's cost rate onto historical hours, so every consultant promoted or re-rated since restates their own history in the direction their rate moved. Because promotions differ per person, engagement margins move in both directions, which is exactly the observed pattern.
Quantify with three parallel cost computations per engagement: (a) the snapshotted fct_time_entry.cost_rate_usd, (b) an effective-dated join using work_date BETWEEN effective_from AND COALESCE(effective_to, 'infinity'), and (c) the shipped is_current join. Report the distribution of (c) minus (a) and correlate it with the share of hours delivered by staff who have since been promoted.
Check cardinality as a separate defect: an is_current join can drop consultants whose current row is missing or whose termination retired the row, and can fan out if consultant_id carries more than one current row. Compare hour totals before and after the join; they must be identical.
Decide the canonical source and justify it: the snapshotted rate on the time entry is what was booked and is authoritative for financial restatement. The effective-dated join is the reconciliation, and a systematic disagreement between (a) and (b) is its own defect worth raising rather than papering over.
Reissue the affected periods with an explicit changeover note, and pin the rule in a test that fails if is_current appears in any join from a fact table to a type-2 dimension on a historical grain.

Follow-up

Some entries have cost_rate_usd NULL or zero. Which source wins then, and what does that do to your reconciliation?
The same pattern exists for bill_rate_usd. What does it do to realisation, and in which direction?
How would you sweep every model in the warehouse for this class of bug without reading each one?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

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.

What does "consultancy" in the field of Data Science mean to you?

Medium · behavioural and stakeholder questions

Approach

Close with what you would do differently, concretely.
Quantify the outcome, including what you would not claim credit for.
Name the disagreement or constraint, and how you resolved it with evidence.

Follow-up

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

Explaining a censored days-to-pay number on a board slide

Easy · censoring · kaplan-meier · executive communication

A finance lead wants average days to get paid for the last four quarters, for a board slide. In fct_invoice_line, rows with status in ('issued','partially_paid','disputed') have paid_at NULL. The mean of (paid_at - issued_at) over paid invoices is 38 days. A Kaplan-Meier median, right-censoring the open invoices at snapshot_date minus issued_at, is 51 days. You get two sentences and one chart. Explain the number you put on the slide, the gap between the two figures, and what will make that number move next quarter.

Approach

Name the probe: whether you can give a non-technical decision-maker one number, the direction of the error in the alternative, and the reason, without teaching survival analysis.
Explain the mechanism in business language. Invoices that have been paid are disproportionately the ones that pay fast; the slow ones are still open and therefore missing from the 38-day average. The error is one-directional and it grows as collections get worse, which is exactly when the number matters.
Commit to one headline. Either the Kaplan-Meier median, or a restricted mean days-to-pay capped at a fixed horizon such as 90 days, which is easier for a finance audience to audit. Say which you used and why, and do not put both headline numbers on the slide.
Make the chart the survival curve or a simple share-paid-by-day-k curve rather than a bar of averages, because the audience question is really when cash arrives, not a single moment.
State the forward behaviour before you are asked: the figure for a recent quarter will rise or fall as open invoices resolve, so the slide carries the snapshot date and the share of invoices still open.

Follow-up

The exec asks why the number printed on last quarter's slide no longer reproduces. What is your answer, and what would have prevented the question?
How would you report this by account_tier without putting six survival curves on one slide?

Your utilisation dashboard caused a staffing decision on incomplete data

Medium · late-arriving data · postmortem · reporting windows

Six weeks ago you shipped a weekly billable-utilisation dashboard keyed on fct_time_entry.work_date. It showed a six-point drop across the three most recent weeks. A practice lead pulled two consultants off an engagement in response. The drop reversed on the next refresh, because time entries are created days to weeks after the work happens and entered_at trails work_date. Describe what you do now: the diagnosis, the change to the artifact, and the conversation with the person who acted on your number.

Approach

Name the probe: whether you own a reporting-design error rather than reclassifying it as someone else's timesheet compliance problem, and whether you fix the class of bug instead of the single week.
Quantify before explaining. Measure the backfill curve directly from entered_at: for work_date D, the share of final hours that existed as of D plus k, for k from 1 to 45. That gives an observed tail length instead of a guessed cutoff.
Change the artifact so the incomplete region cannot be read as a trend. Either end the trended series at snapshot minus the measured tail, or publish an as-of-entered_at series that is internally consistent, and label which one is on screen.
Tell the person who acted, first and directly, with the corrected series and the specific decision to revisit. A correction that arrives after they notice costs more than the original error.
Add a standing completeness tile: timesheet submission lag, the share of entries where entered_at date minus work_date exceeds seven days, so the dashboard shows its own reliability rather than depending on you remembering.

Follow-up

Leadership still wants to see the current week. What do you show, and how do you label it?
One practice runs a three-day lag and another twenty days. Do you set one firm-wide cutoff or one per practice, and what does that cost in comparability?