HiSolutions AG Data Scientist Interview Questions & Guide 2026

The 60-second brief

At HiSolutions AG, the Data Scientist role sits at the intersection of complex problem-solving, strategic consulting, and technical rigor. As a leading consultancy in IT security and management, HiSolutions AG relies on data to provide actionable insights that help clients navigate high-stakes digital environments. You will be instrumental in transforming raw data into clear, evidence-based recommendations that directly impact client security posture and business efficiency.

Allocate prep to your weakest link rather than your favourite topic. Of the three things that usually gate the outcome (SQL that is correct under messy joins, sound reasoning about experiments, and structured framing of an open-ended problem), candidates tend to over-invest in modelling theory and under-invest in framing.

Focus areas

• Reconstruct pipeline stage history from mutable rows
• Cluster inference at the account, not the engagement
• Correct late-entered timesheets before trending recent weeks

Overview

This role is not merely about model building; it is about translating abstract business requirements into measurable data products. You will work across diverse projects, ranging from performance optimization to risk assessment, requiring a blend of statistical depth and product intuition. Success in this role means you can bridge the gap between technical complexity and stakeholder clarity, ensuring that every analysis you deliver provides tangible value.

The HiSolutions AG interview process

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

Technical Deep-Dives

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

Scenario-Based Discussions

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

• Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
• Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
• How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

• Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
• Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
• Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Interaction with Leads

Rounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.

What to demonstrate

• Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
• The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
• Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options

How to prepare

• Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
• Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
• Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.

Where candidates lose points

Pooling margin, realisation or overrun across pricing models

Fixed-fee margin falls with hours worked; uncapped time-and-materials margin rises with hours worked; retainer margin depends on neither. A quarter in which the firm sells more fixed-fee work will show a margin change caused entirely by mix, not by delivery performance, and the aggregate can move in the opposite direction to every individual pricing model. Always stratify by fct_engagement.pricing_model before comparing periods, and report the mix shift alongside the within-stratum change.

Modelling win rate on proposals with a recorded outcome, using fields written after the decision

Two failures compound here. First, stage IN ('withdrawn','no_decision') is not missing at random: those are disproportionately deals that were going to be lost, so training on won-plus-lost only inflates apparent win rate and distorts the coefficients. Second, fields like engagement_id, final scope and revised pricing are populated after the outcome is known, so including them leaks the label and produces a model with excellent backtest accuracy and no forward value. Restrict features to values knowable at submitted_at.

Writing SQL without stating NULL and tie-breaking behaviour

Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.

Reporting a p-value with no effect size or interval

Give the estimated difference with a confidence interval in the units the business cares about, then say whether that whole interval is worth acting on. A p-value only addresses whether you can rule out exactly zero; it says nothing about magnitude.

Questions & practice

Cluster bootstrap a margin change across few, unequal accounts

Hard · cluster bootstrap · few clusters · mix shift

engagements has engagement_id, client_id, pricing_model, fees_usd, cost_usd and period in {pre, post} around a rate-card change. There are about 35 client_ids across roughly 400 engagements, and the top five clients carry a large share of fees. Estimate the pre-to-post change in value-weighted gross margin within pricing_model, and give a 95% interval by resampling whole client_ids with replacement, B = 2000. Write the bootstrap yourself; no resampling library. Report the account-weighted estimate alongside and explain any divergence.

Approach

1. Write the statistic as a pure function of a dataframe first. Within each pricing_model, margin = (sum(fees) - sum(cost)) / sum(fees) per period; the headline is the fee-weighted average of the within-model differences using fixed pre-period weights, so a shift in the mix of work sold cannot masquerade as a change in delivery.
2. Resample clusters, not rows: draw 35 client_ids with replacement and concatenate all engagements of each drawn client. A client drawn twice contributes its rows twice under distinct pseudo-ids, which is what preserves within-account correlation instead of averaging it away.
3. Recompute the statistic on each replicate and take a percentile interval from the 2.5th and 97.5th quantiles. With concentrated fees the replicate distribution is skewed, so a symmetric point plus or minus 1.96 times a standard error is wrong in the tail that matters.
4. Compute the account-weighted version (mean across clients of each client's margin change) next to the value-weighted one, and attribute the divergence to concentration rather than to noise; if they disagree in sign, that fact is the finding.
5. Name the regime honestly: at roughly 35 clusters both cluster-robust standard errors and the pairs cluster bootstrap under-cover, so state the fix you would run next, which is a wild cluster bootstrap with Rademacher weights or CR2 with t(G-1) critical values.

Follow-up

• Implement the wild cluster bootstrap and show at what cluster count its p-value separates from the naive one.
• One client is 30% of fees. Show the interval with and without that account and say which one you would present, and to whom.
• What pre-period check would make you willing to call this a causal effect of the rate card rather than a correlation?

Kaplan-Meier days to payment with unpaid invoices censored

Hard · survival analysis · censoring · kaplan-meier

invoice_lines has invoice_line_id, engagement_id, line_type, issued_at, due_date, paid_at (null when unpaid), amount_usd and status in draft, issued, partially_paid, paid, disputed, written_off. At a given snapshot_date, estimate the median days from issue to full payment. Implement Kaplan-Meier yourself; no lifelines or equivalent. Treat issued, partially_paid and disputed as right-censored at snapshot_date, and decide and justify what to do with written_off. Report the naive mean over paid lines alongside your estimate and state the sign of its bias.

Approach

1. Build the duration and event table explicitly. Drop draft lines, which have no clock. For status paid, duration = (paid_at - issued_at).days with event = 1. For issued, partially_paid and disputed, duration = (snapshot_date - issued_at).days with event = 0.
2. Handle written_off as a competing event rather than a censor. Censoring it makes the estimator answer 'time to payment if written-off invoices could still pay', which overstates collection. Either report a cumulative-incidence version alongside, or censor them and say plainly that the result is conditional on eventual collection.
3. Implement the estimator directly: sort unique event times, at each t take n_i as the count with duration >= t and d_i as the payments at exactly t, and accumulate S(t) = product of (1 - d_i / n_i). Censored rows leave the risk set without causing a drop, which is the whole mechanism and the reason the answer differs from any completed-case average.
4. Read the median as min{t : S(t) <= 0.5}. If S never reaches 0.5 within observed follow-up, report 'not reached'; interpolating past the last observation invents data that the snapshot does not contain.
5. Add Greenwood's formula for Var(S(t)) to put a band on the curve, then invert the band at 0.5 for an interval on the median rather than quoting a point estimate alone.
6. Compare against the mean over paid lines only and name the direction: at any snapshot the paid set over-represents fast payers, so the naive mean is biased low, and the bias widens exactly when collections deteriorate.

Follow-up

• A large account moved to a monthly payment run. Is administrative censoring still independent of payment time, and what would you check?
• Finance wants one DSO number against a target. What do you give them, and what do you refuse to give them?
• Stratify by line_type. Do milestone lines behave like fees lines, and what would it mean for the firm if they do not?

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.

Worked solution (30 min)

1. dim = dim.sort_values(['consultant_id','effective_from']); assert within each consultant that effective_from is strictly increasing and effective_to.fillna(max) >= the next effective_from.
2. cats = np.union1d(time_entries.consultant_id.unique(), dim.consultant_id.unique()), which is sorted, so code = pd.Categorical(ids, categories=cats).codes.astype('int64') preserves consultant_id order and the sort above still holds; an id absent from the dimension codes to -1 instead of matching something else.
3. floor = min(work_date.min(), effective_from.min()).normalize(); off = (ts.dt.normalize() - floor).dt.days.to_numpy() in both frames; SCALE = 1 << 20 (1,048,576 days, about 2871 years); assert off.min() >= 0, off.max() < SCALE and code.max() * SCALE + off.max() < np.iinfo(np.int64).max; key = code * SCALE + off.
4. idx = np.searchsorted(dim_key, entry_key, side='right') - 1; entries whose code is -1 are exceptions with reason 'consultant_not_in_dimension'; rows with idx < 0, or whose matched dimension code differs from the entry's code, are exceptions with reason 'no_interval_before_work_date'.
5. Check the upper bound: keep rows where effective_to is NaT or work_date < effective_to; the rest are exceptions with reason 'gap_in_scd'.
6. Brute-force 200 random entries with a straight boolean filter over that consultant's rows and diff the rates against the vectorised answer.

Expected result: Matched rows equal to the input count minus exceptions, each carrying exactly one consultant_sk and standard_cost_rate_usd, plus an exceptions frame grouped by reason. No input row is duplicated and none is silently dropped.

Verify it yourself

• len(matched) + len(exceptions) == len(time_entries), which is the property an inner join would break.
• The 200-row brute-force sample agrees on every rate.
• An entry whose work_date equals an effective_from takes the new rate, not the old one, exactly once.
• Per-consultant total cost changes by (new_rate - old_rate) * hours after the change date, and by nothing before it.
• np.diff(dim_key) is strictly positive, asserted rather than assumed: this is the only cheap guard against a key that has wrapped, since an overflowed product can land back on a positive value and a non-negativity check alone would pass it. Renumbering the consultant_ids (say to 4000001..4000050) must not change a single attached rate, which is the fixture a raw-id-times-nanoseconds key fails once ids run past int64_max / SCALE.

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?

Given two tables (users and transactions), write a query to identify users who haven't made a purchase in the last 90 days.

Medium · sql and data manipulation

Approach

1. Check whether any join is one-to-many before aggregating, or the sums inflate.
2. Compute rates by summing numerator and denominator separately, never by averaging rates.
3. Say which table is the grain you start from, and join outward from it.

Follow-up

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

Explain the difference between RANK and DENSE_RANK in a practical scenario.

Medium · sql and data manipulation

Approach

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

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?

Longest consecutive bench spell per consultant from weekly activity

Hard · gaps and islands · window functions · run length

You have a dense series (consultant_id, week_start, billable_hours, full_week_leave BOOLEAN) covering 26 weeks for every billable-role consultant, with zero-hour weeks present as 0. Return every maximal run of consecutive weeks where billable_hours = 0, as consultant_id, spell_start_week, spell_end_week and week_count, then each consultant's longest run. Weeks flagged full_week_leave must neither break a run nor count toward its length. Mark runs still open at the last week of the series rather than reporting them as finished.

Approach

1. Remove full_week_leave weeks from the sequence first, then re-number what remains with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_start). Deleting before numbering is precisely what makes a leave week transparent to a run instead of a break in it; filtering afterwards would leave a gap in the sequence and split the spell.
2. Flag each surviving week as bench (billable_hours = 0) or active, then compute two row numbers per consultant: one over all surviving weeks, one partitioned additionally by the bench flag. Their difference is constant inside any run of adjacent same-flag weeks, which is the island key.
3. Group the bench weeks by (consultant_id, island key) and take MIN(week_start), MAX(week_start) and COUNT(*). Do not island on week_start minus an interval times a row number: that variant assumes the spine has no holes, and the leave removal has just deliberately put holes in it.
4. Pick the longest spell per consultant with ROW_NUMBER() OVER (PARTITION BY consultant_id ORDER BY week_count DESC, spell_start_week) so ties resolve deterministically, rather than a MAX that cannot carry the accompanying dates.
5. Handle both edges as censoring. A spell touching the last week of the series is still open and its length is a lower bound, so flag it; a spell touching the first week may have started earlier, so either flag it too or exclude it from any length distribution and say which.

Worked solution (40 min)

1. Filter out full_week_leave rows and count the surviving weeks per consultant.
2. Add the bench flag and the two row numbers, then print one consultant's full sequence to see the island key stay constant across a run.
3. Group bench weeks by consultant and island key and aggregate to spell boundaries and lengths.
4. Add the open-at-the-edge flag by comparing spell_end_week to the consultant's last surviving week.
5. Rank spells per consultant and select the longest with the deterministic tiebreak.

Expected result: One row per maximal bench run with its first and last week, its length in weeks and an is_open flag, plus a one-row-per-consultant longest-spell table. A consultant with no bench weeks appears in neither, which is correct and worth stating.

Verify it yourself

• For each consultant, the sum of week_count across all spells equals their count of bench weeks in the leave-filtered series.
• No two spells for the same consultant are adjacent in the re-numbered sequence; adjacency would mean the island key was computed wrongly.
• The longest-spell table has at most one row per consultant and its week_count matches the maximum in the spells table.
• Hand-check one consultant by printing their 26 weeks as a strip of zeros, hours and leave markers, and reading the runs off it.

Follow-up

• A spell begins before your 26-week window. How do you detect that, and what do you report as its length?
• Rebuild this at daily grain. What changes about weekends, holidays and the island key?
• How would you separate true bench from unbilled delivery work on a live engagement, given only charge codes?

How would you design a set of success metrics for a new security dashboard feature?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Name one primary metric, then the guardrail that stops it being gamed.
3. Decompose the metric into the rates that drive it, and say which one you would check first.

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 decide which features to prioritize when building a predictive model for client churn?

Medium · product sense and metrics

Approach

1. Name one primary metric, then the guardrail that stops it being gamed.
2. State what result would change your recommendation, so the answer is falsifiable.
3. 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?
• What would you do if the primary metric and the guardrail moved in opposite directions?

What are the trade-offs between choosing a short-term engagement metric versus a long-term retention metric?

Medium · product sense and metrics

Approach

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

Follow-up

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

If an experiment shows a positive lift in a primary metric but a negative movement in a guardrail metric, how do you proceed?

Medium · product sense and metrics

Approach

1. Fix the population and the time window before naming any metric.
2. Name one primary metric, then the guardrail that stops it being gamed.
3. Restate the decision this analysis has to support, and who acts on the answer.

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 determine the required sample size for an A/B test to achieve statistical significance?

Medium · a/b testing and experimentation

Approach

1. Name the randomisation unit first; it decides the variance and what the test can detect.
2. Say whether units interfere with each other, and switch design if they do.
3. State the primary metric and the minimum effect worth shipping, then size the test.

Follow-up

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

How do you account for seasonality when analyzing A/B test results?

Medium · a/b testing and experimentation

Approach

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

Follow-up

• What would you conclude if the result is positive but the test is underpowered?
• What would you do if you could not randomise at all?

Measure a staggered process rollout across five practice areas

Hard · difference-in-differences · staggered adoption · wild cluster bootstrap · parallel trends

A delivery-review process was rolled out one practice area at a time over six quarters, and all five practice areas now have it, so there is no never-treated group. You have engagement-quarter gross margin reconstructible from fct_invoice_line and fct_time_entry, plus pricing_model on fct_engagement. Leadership wants the effect on margin. Explain why a two-way fixed effects regression of margin on a treated indicator is not trustworthy here, name the estimator you would use instead and what identifies it, and specify how you would obtain a defensible p-value from five clusters.

Approach

1. Explain the two-way fixed effects failure concretely. Under staggered timing the coefficient is a weighted average of every available two-by-two comparison, and that set includes comparisons that use already-treated units as controls for later-treated ones. Those comparisons difference out not only the common time shock but also the earlier cohort's ongoing treatment-effect growth, so when effects grow after adoption the aggregate is pulled toward zero and can flip sign. Decomposed onto the underlying ATT(g,t), the implied weights can be negative, which is why the coefficient is not an average of treatment effects at all. This is a bias in the estimand, not a small-sample artefact, so more data does not fix it.
2. Switch to a heterogeneity-robust estimator: Callaway and Sant'Anna group-time ATT(g,t) using not-yet-treated units as the comparison group, aggregated into an event-study path and a single summary. Sun and Abraham's interaction-weighted estimator or Borusyak-Jaravel-Spiess imputation answer the same objection. With every area eventually treated, identification rests entirely on not-yet-treated windows, so the last-adopting area carries disproportionate weight; say that out loud rather than burying it.
3. Test parallel trends honestly. Plot event-time leads with confidence intervals and refuse to read a non-rejection as confirmation, because at five clusters that test is badly underpowered. Pair it with a sensitivity analysis that bounds the post-treatment violation as a multiple of the largest pre-period violation and reports the multiple at which the conclusion breaks.
4. Fix the inference, and be precise about what the bootstrap can deliver at five clusters. Five sits far below the roughly forty at which cluster-robust standard errors become approximately valid, and below that they are sharply downward-biased. A wild cluster bootstrap with Rademacher weights draws from 2^5 = 32 sign vectors, but the null-imposed bootstrap t-statistic is odd in the sign vector: negating every weight negates t* and leaves |t*| unchanged, because the coefficient deviation is linear in the weights while the cluster-robust variance is quadratic in them. The 32 vectors therefore collapse to 2^4 = 16 distinct values of |t*|, so the smallest attainable two-sided p-value is 1/16 = 0.0625. That is above 0.05, so at five clusters a Rademacher bootstrap cannot reject at the conventional level however large the true effect is. Use Webb six-point weights, whose 6^5 = 7,776 vectors collapse to 3,888 distinct |t*| and a floor near 0.00026, or randomisation inference permuting the five observed adoption dates across the five practice areas, which gives 5! = 120 assignments and a floor of 1/120 = 0.0083.
5. Deal with the mix confound before reporting anything. Fixed-fee margin falls with hours worked while uncapped time-and-materials margin rises with them, so a rollout that coincided with a pricing-mix shift contaminates the pooled estimate. Estimate within pricing_model and report the within-model effects alongside the mix decomposition.

Worked solution (45 min)

1. Build the engagement-quarter panel with an adoption quarter per practice area, keyed on the quarter the process went live rather than when it was announced.
2. Run the Goodman-Bacon decomposition of the two-way fixed effects estimate and read off how much of the total weight sits on already-treated-as-control comparisons; that share is the diagnostic there, since those weights are non-negative by construction. For negative weights on the underlying ATT(g,t), run the de Chaisemartin and D'Haultfoeuille decomposition and report the negative share.
3. Estimate ATT(g,t) with not-yet-treated controls, aggregate to event time, and plot leads and lags with confidence bands.
4. Compute the p-value three ways, cluster-robust, Rademacher wild bootstrap and Webb wild bootstrap, and report the Webb figure while noting that the Rademacher floor at five clusters is 1/16 = 0.0625 and therefore cannot clear 0.05 at all.
5. Re-estimate separately for fixed_fee and time_and_materials engagements and report the mix decomposition against the pooled change.

Expected result: An event-study path whose pre-period leads are statistically indistinguishable from zero, a post-period aggregate ATT reported with a Webb wild-bootstrap p-value, an explicit note that a Rademacher bootstrap at five clusters cannot produce a p below 1/16 = 0.0625 and so cannot reject at 0.05 on any data, and separate estimates by pricing model with the mix shift quantified.

Verify it yourself

• Goodman-Bacon weights are non-negative and sum to one by construction, so a negative one there is an implementation bug rather than a finding; the number to read is the share of weight resting on already-treated-as-control comparisons. Negative weights on the underlying ATT(g,t) come from the de Chaisemartin and D'Haultfoeuille decomposition, and a large negative share means the two-way fixed effects coefficient should not be reported as an average effect.
• Restricted to one adoption cohort and two periods against not-yet-treated units, the group-time estimator and two-way fixed effects must return the identical two-by-two difference in differences; a gap there is an implementation bug, not heterogeneity.
• At five clusters, enumerate all 32 Rademacher sign vectors rather than drawing B of them. The resulting |t*| must come back as 16 identical pairs, one per sign-flip orbit; 32 distinct values means the null is not being imposed or the statistic being bootstrapped is not the cluster-robust t.
• Wild bootstrap p-values should be weakly larger than cluster-robust ones at five clusters; if they come out smaller, check that the null is being imposed in the bootstrap.
• Within-pricing-model effects plus the mix-shift term must reconcile to the pooled period-over-period change.

Follow-up

• If the rollout order was not random, say the worst-performing practice area went first, what does that do to parallel trends and what would you do about it?
• How would you choose between a synthetic control built on a single practice area and the group-time estimator?
• What would the event-study path have to look like for you to believe the effect is real rather than a continuation of a pre-existing trend?

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

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

Explaining a method to someone who will not follow the maths

Half of this section is about translation. Be ready to describe how you explained a result to someone who did not want the method, only the implication, and what you did when the simplified version started being repeated in a way that overstated it. Correcting your own simplification is a strong beat.

Tell me about a time you mentored a teammate or improved a team process.

Medium · behavioural and stakeholder questions

Approach

1. State the situation in two sentences and spend the rest on your reasoning.
2. Pick a story where you drove the decision, not one where you observed it.
3. Quantify the outcome, including what you would not claim credit for.

Follow-up

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

Three requests, one week, and the one you defer

Medium · prioritisation · stakeholder management · scope reduction

Three requests arrive in one week. Finance needs days sales outstanding recomputed for a board meeting in four days. A partner wants a proposal win-rate model for a pursuit review in three weeks. Delivery wants a staffing forecast, with no date attached. You have one week of your own capacity and no analyst. Write the prioritisation you send back, the request you defer, and the message you send to the person whose request you defer.

Approach

1. Name the probe: whether you prioritise on decision dates and reversibility, or on who asked most forcefully.
2. Score each request on four things: what decision it changes, the date that decision is made, the cost of being late, and the cost of being wrong. A board figure has a hard date and a high cost of being wrong; a forecast with no date has neither.
3. Reduce scope rather than dropping work. The win-rate model is the largest piece and the easiest to get wrong, because features written after the decision, such as engagement_id and revised pricing, leak the label, and because withdrawn and no_decision proposals are not missing at random. A two-day descriptive win-rate cut by is_competitive and loss_reason answers most of what a pursuit review needs, with the model scoped separately.
4. Defer explicitly, with a date and a smaller substitute, rather than leaving a request to decay quietly. Silence is read as agreement and then as failure.
5. Put the trade-off in writing so it can be overturned by someone with more context than you have, and say what you would drop if the deferred request becomes urgent.

Follow-up

• The partner escalates to the practice lead. What do you change, and what do you refuse to change?
• What evidence would make you drop the finance work instead?

Answering a request to redefine the utilisation denominator

Hard · denominator integrity · restatement · escalation

Before a quarterly review, a practice lead asks for two changes to reported billable utilisation: drop consultants allocated to engagements with status = 'paused' out of the denominator, and count approved travel hours booked to client_delivery as billable. Together the changes move reported utilisation from 71 to 79 percent. You must answer in writing today. Give your answer, the principle you apply to each change separately, and what you do if you are overruled.

Approach

1. Name the probe: whether you can hold a measurement line by adjudicating each proposal on its merits, rather than treating the request as bad faith or accepting it to avoid a fight.
2. Adjudicate the paused-engagement change against what the denominator means. Billable utilisation measures the share of available consultant hours that were sold. A consultant whose engagement is paused is still available and still costs salary, which is precisely the situation the metric exists to reveal. Removing them defines away the signal, so this one is refused on definitional grounds, not on suspicion.
3. Adjudicate the travel change as an empirical question, because it is one. If those hours are billed to the client, is_billable and bill_rate_usd should already reflect it; if they are charged and then written off or never invoiced, counting them raises utilisation while realisation falls. Check the invoice lines for the same engagement and period rather than arguing about principle.
4. Quantify each change separately, and the interaction. An eight-point move made of one point and seven points is a different conversation from two four-point moves, and the lead cannot make a good decision without that split.
5. If a change is accepted, restate history on the new definition and publish the old series alongside it with the date of the change. A definition applied forward only makes every period-over-period comparison meaningless. If overruled, comply with the restatement and the footnote, and escalate only the part that is a misstatement of fact rather than a defensible choice.

Follow-up

• Which of the two changes would you accept, and what evidence flips your answer on the other one?
• The lead wants the new definition applied only from this quarter forward. Why is that worse than applying it to history, and how do you explain that in one sentence?