Interview guides · Lufthansa Industry Solutions

Lufthansa Industry Solutions Data Scientist Interview Questions & Guide 2026

Practice explaining analysis before a Lufthansa Industry Solutions data scientist interview: the recommendation, the uncertainty, the follow-ups.

The 60-second brief

A Data Scientist at Lufthansa Industry Solutions (LHIND) plays a pivotal role at the intersection of advanced analytics, cloud technology, and business consulting. Unlike traditional in-house data roles, your work here is heavily consulting-driven. You will not only build machine learning models but also act as a trusted advisor to internal Lufthansa Group business units and external clients spanning aviation, logistics, energy, manufacturing, and automotive sectors.

If the team owns experimentation, expect depth past a two-sample test: minimum detectable effect and its roughly inverse-square-root dependence on sample size (holding power, significance level and variance fixed), variance reduction from pre-period covariates, interference between units, and when a sequential design is the right call.

Focus areas

Reconstruct pipeline stage history from mutable rows
Cluster inference at the account, not the engagement
Compute utilisation against a defended availability denominator

Overview

The impact of this position is substantial. You will translate complex business challenges into scalable data products, working on diverse initiatives such as predictive maintenance for aircraft, supply chain optimization, passenger flow forecasting, and energy efficiency modeling. The role requires a unique blend of technical mastery and client-facing communication, as you will guide stakeholders through their digital transformation journeys from initial scoping to production-grade deployment.

To succeed in this role, you must thrive in an agile, project-based environment. You will collaborate closely with,, and to deliver robust solutions. For a professional who enjoys variety, high-impact problem solving, and the complexity of industrial-scale data, this position offers an exceptionally rich and rewarding career path.

The Lufthansa Industry Solutions interview process

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

HR Screening

A screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.

What to demonstrate

Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
Whether timeline, location and compensation expectations make the rest of the loop worth scheduling

How to prepare

Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice

Technical Evaluation

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

What to demonstrate

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

How to prepare

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

Final Interview

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

What to demonstrate

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

How to prepare

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

Where candidates lose points

Treating accounts as independent observations

Revenue is concentrated: a small number of client_ids typically carries a large share of fees, and engagements within one account share a partner, a rate card and a delivery team. Ordinary standard errors computed over engagements therefore understate uncertainty badly. Cluster at client_id, and with fewer than roughly 40 clusters use a wild cluster bootstrap or a CR2 correction, because cluster-robust standard errors are downward-biased in that regime and will manufacture significance that a replication will not reproduce.

Trending utilisation or revenue on work_date without accounting for timesheet backfill

Time entries are created days to weeks after the work happens, and the backfill tail often runs two to six weeks. A dashboard keyed on work_date therefore shows the most recent weeks as a decline that reverses on every refresh. The fix is either to hold the reporting window back past the observed backfill tail (measure the tail with the timesheet submission lag metric rather than guessing) or to report an as-of-entered_at snapshot so the series is internally consistent, and to state which one you used.

Never asking what decision the analysis will inform

Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.

Averaging per-user rates to produce a population rate

Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.

Questions & practice

How do you transition a prototype model from a local environment to a production-ready cloud deployment?

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

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?

Explain the trade-offs between using a simple, interpretable model (like a decision tree) versus a complex black-box model (like a deep neural network) in a client-facing project.

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Set a baseline first, so any model has something honest to beat.
Check what information would not exist at prediction time, and exclude it.

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?

How do you measure the business value or return on investment (ROI) of a data science model once it is deployed?

Medium · machine learning and modelling

Approach

Frame the prediction: the label, the moment of prediction, and the action it triggers.
Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.

Follow-up

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

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

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.
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.
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.
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.
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.
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.

Worked solution (40 min)

Assemble a frame of (duration_days, event) per non-draft line, with snapshot-based durations for the three open statuses and an explicit, documented rule for written_off.
times = np.sort(df.loc[df.event == 1, 'duration_days'].unique()); for each t, n = (df.duration_days >= t).sum() and d = ((df.duration_days == t) & df.event).sum().
S = np.cumprod(1 - d / n) aligned to times; prepend S(0) = 1.0 so the curve starts where it should.
median = times[np.argmax(S <= 0.5)] if (S <= 0.5).any() else 'not reached'; compute Greenwood variance as S(t)^2 * cumsum(d / (n * (n - d))).
naive = df.loc[df.event == 1, 'duration_days'].mean(); report both, plus the censored share, since the gap between the two scales with it.

Expected result: A step function starting at S(0) = 1.0 that drops only at observed payment days, an integer median or an explicit 'not reached', a Greenwood-based interval, and a naive completed-case mean that sits below the Kaplan-Meier median on data with material censoring.

Verify it yourself

S is non-increasing, starts at 1.0, and never goes below 0; the number at risk at the first event time equals the count of all non-draft lines.
Re-running with every censored row deleted makes the Kaplan-Meier median equal the empirical median of paid durations, since with no censoring the estimator reduces to the empirical survival function.
A six-row toy example worked by hand reproduces the implementation's S values exactly.
The gap between the naive mean and the Kaplan-Meier median grows when you move snapshot_date earlier, which raises the censored share.

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?

Deduplicate to the latest issued invoice line per engagement

Easy · window functions · dedup · determinism

fct_invoice_line holds invoice_line_id, invoice_id, engagement_id, line_type, period_end_date, amount_usd, issued_at and status; draft lines have issued_at NULL. Return exactly one row per engagement_id, its most recently issued line, with invoice_line_id, line_type, amount_usd and issued_at. Two lines on the same engagement can share an issued_at value to the second, so the result must be identical on every run against unchanged data. Write it with a window function, then say in one sentence when DISTINCT ON would be the better choice.

Approach

Filter to issued lines (issued_at IS NOT NULL, status <> 'draft') before ranking. Ranking first and filtering afterwards can leave an engagement whose top-ranked row was a draft with no row at all in the output.
Rank with ROW_NUMBER() OVER (PARTITION BY engagement_id ORDER BY issued_at DESC, invoice_line_id DESC). The surrogate key as the final ORDER BY term is what makes ties resolve the same way on every execution; without it the planner is free to return either tied row.
Wrap the ranked query in a CTE or subquery and filter rn = 1, since a window function cannot appear in the WHERE clause of the query that computes it.
Name why RANK() is wrong here: on tied issued_at it assigns 1 to both rows, so the result carries two rows for one engagement and any downstream SUM double counts.
Compare against DISTINCT ON (engagement_id) ... ORDER BY engagement_id, issued_at DESC, invoice_line_id DESC: same answer, one sort, usually cheaper, but the window form generalises the moment you also want rn <= 3 or a second ranking in the same pass.

Follow-up

Now return the latest line per engagement per line_type. What changes in the PARTITION BY, and what does that do to the row count?
Return the latest and second-latest line together. Which of the two constructs survives that requirement?
issued_at is TIMESTAMPTZ and finance reports on a single local calendar. Where can 'latest' flip between the two conventions?

Build a client cohort revenue table from invoice lines

Easy · cohorts · date truncation · revenue recognition

dim_client holds client_id and first_engagement_date. fct_engagement maps engagement_id to client_id. fct_invoice_line holds engagement_id, line_type, period_start_date, amount_usd and status; recognised fees are lines with line_type IN ('fees','milestone','credit_note') and status <> 'draft', and credit notes carry negative amount_usd. Return one row per (cohort_month, months_since_first) giving distinct clients billed and net recognised fees. cohort_month is the month of first_engagement_date; months_since_first is the whole-month gap between period_start_date and first_engagement_date. Long format, no pivot. Decide explicitly what to do with clients whose first_engagement_date is NULL.

Approach

Filter fct_invoice_line to recognised lines first (line_type, status <> 'draft'), and key recognition on period_start_date rather than issued_at or paid_at. Billing period, issue date and payment date are three different clocks, and mixing them produces a cohort table nobody can reconcile against finance.
Join invoice lines to fct_engagement to reach client_id, since invoice lines carry engagement_id only, then to dim_client for first_engagement_date.
Truncate both dates to month, then compute months_since_first as 12 * (year(bill_month) - year(cohort_month)) + (month(bill_month) - month(cohort_month)). Doing it on truncated months is what keeps a client billed on day 2 of the third month in bucket 2 instead of bucket 1.
Aggregate to (cohort_month, months_since_first) with SUM(amount_usd) and COUNT(DISTINCT client_id), keeping credit_note lines inside the sum so the figure is net rather than gross.
Handle NULL first_engagement_date as an explicit exclusion and report how many clients and how much revenue that removes, rather than letting the join drop them silently. That exclusion is why the output's total is smaller than the filtered line set's total, so carry the excluded figure as a named number rather than discovering the gap later.

Worked solution (25 min)

Write the recognised-lines CTE with the line_type and status predicates, and check its row count against the unfiltered table so you know how much you excluded.
Join through fct_engagement to dim_client and confirm the join does not multiply rows: a line has exactly one engagement, an engagement exactly one client.
Add the two date_trunc expressions and the integer month arithmetic, then eyeball ten rows against their source dates.
Group and aggregate, then sort by cohort_month and months_since_first.
Run a separate count of clients with NULL first_engagement_date, and a separate sum of their recognised fees, and note both beside the output.

Expected result: A long-format table of (cohort_month, months_since_first, active_clients, net_fees_usd), plus two scalars reported beside it: the count of clients with a NULL first_engagement_date and the recognised fees attached to them. months_since_first is a non-negative integer on every row; a negative value means an invoice period precedes the recorded first_engagement_date, which is a data-quality finding to report, not something to clamp to zero.

Verify it yourself

SUM(net_fees_usd) over the whole result equals SUM(amount_usd) over the filtered lines whose engagement resolves to a client with a non-NULL first_engagement_date. It does not equal SUM(amount_usd) over the full filtered line set, which is larger by exactly the excluded NULL-cohort revenue: add that separately reported figure back and the two agree. Any remainder after that is a line whose engagement_id has no matching row in fct_engagement, which is a referential finding to name rather than absorb.
COUNT(DISTINCT client_id) at months_since_first = 0 within a cohort_month is less than or equal to the number of clients whose first_engagement_date falls in that month.
Zero rows have a negative months_since_first; if any appear, name the clients rather than filtering them out.
Re-running with period_end_date in place of period_start_date moves only lines whose billing period spans a month boundary.

Follow-up

How would you turn this table into a 12-month net revenue retention ratio, and what exactly becomes the denominator?
One account contributes 40 percent of a cohort's month-13 fees. How do you present the cohort so a reader sees that before they act on the ratio?
If subsidiaries roll up to parent_client_id, which first_engagement_date defines the cohort, and does the rollup change the cohort a subsidiary lands in?

How do you manage your time and prioritize tasks when working across multiple client projects simultaneously?

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?
Which segment would you cut first, and what would that rule out?

How do you gather requirements from a non-technical client who knows they want "AI" but cannot define their business problem?

Medium

Approach

Say what you would check first and why it is the highest-information step.
State your assumptions explicitly before working the problem.
Clarify what is being asked and what a complete answer would contain.

Follow-up

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

Imagine a client project is falling behind schedule because of data quality issues. How do you communicate this to the client and manage their expectations?

Medium

Approach

Work from the decision backwards to the evidence you would need.
State your assumptions explicitly before working the problem.
Clarify what is being asked and what a complete answer would contain.

Follow-up

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

Cut variance with pre-period margin before buying more accounts

Medium · cuped · variance reduction · stratification · ancova

You are designing an account-randomised pilot of a delivery-review process, measured on engagement gross margin over one quarter. The design is underpowered at the account count available. Each account has two prior quarters of margin history reconstructible from fct_invoice_line and fct_time_entry, correlated 0.6 with the pilot-quarter outcome. Specify a variance-reduction plan: the estimator, the coefficient it uses, how you handle accounts whose first engagement starts inside the pilot window, and the sample-size equivalent of the reduction you expect. State the precondition that makes the adjustment safe.

Approach

Define the adjusted outcome Y* = Y - theta * (X - Xbar) with X the pre-period margin and theta = Cov(Y, X) / Var(X). The precondition is that X is measured strictly before assignment, so its arm means agree in expectation and subtracting it removes the account-level variance component without touching the treatment contrast.
State the payoff exactly: Var(Y*) = Var(Y) * (1 - rho^2). At rho = 0.6 that is a 36% variance reduction, a 20% standard-error reduction since sqrt(0.64) = 0.8, and a sample-size equivalent of 1 / (1 - rho^2) = 1.56 times the accounts. Real, but it will not rescue a design that is five times short.
Implement it as the ANCOVA regression of Y on treatment and X, which is numerically the same estimator when theta is the OLS coefficient and which hands you cluster-robust inference for free. Estimate theta on pooled data, never separately per arm, which reintroduces bias.
Keep accounts with no history rather than dropping them: impute X at the sample mean and add a no-history indicator. Because X is pre-treatment, imputation cannot bias the treatment coefficient, while dropping those accounts silently changes the estimand to accounts with history.
Balance at assignment as well as adjusting after it. Block on account_tier and pre-period margin tercile, randomise within block, and carry block fixed effects into the model. At roughly 120 accounts, blocking guarantees balance where post-hoc adjustment only achieves it on average.

Worked solution (25 min)

Build the account-quarter margin panel: fees from fct_invoice_line where line_type in (fees, milestone, credit_note), cost as approved fct_time_entry hours times cost_rate_usd, rolled up through parent_client_id.
Estimate rho between the pre-period window and a historical quarter that stands in for the pilot, and confirm it is near the assumed 0.6 rather than assuming it.
Fit Y on treatment, X and block fixed effects with standard errors clustered at client_id; read theta off the X coefficient.
Compare the standard error on the treatment coefficient with and without X in the model.
Recompute the MDE from the reduced standard error and test it against the decision threshold before committing to the pilot.

Expected result: At rho = 0.6 the adjusted standard error should come in about 20% below the unadjusted one, equivalent to 1.56 times the accounts, and the two treatment-effect point estimates should agree within about one standard error since both are unbiased.

Verify it yourself

The treatment coefficient with and without the covariate must be close; a large shift means X is imbalanced across arms, which is either a randomisation failure or a covariate that is not actually pre-treatment.
Realised variance reduction measured on the pilot data should be near 1 - rho^2; a much smaller reduction means rho was estimated on a period that does not resemble the pilot.
Dropping the no-history accounts entirely should move the point estimate only within noise; a large move means those accounts differ systematically and the estimand has changed.

Follow-up

Why is pre-period outcome usually a better covariate than account_tier alone, and when does that ranking flip?
A colleague wants to add hours worked during the pilot quarter as a second covariate. What happens to the estimate?
Your pre-period window is two quarters. What sets the optimal length, and what degrades if you extend it to eight?

Firm margin rose while every pricing model's margin fell

Easy · mix shift · margin · decomposition

Firm-wide engagement gross margin rose from 31% to 34% quarter over quarter. Split by fct_engagement.pricing_model, margin fell inside time_and_materials, fixed_fee, retainer and outcome_based. Sources are fct_invoice_line (engagement_id, line_type, amount_usd, period_start_date, period_end_date) and fct_time_entry (engagement_id, hours, cost_rate_usd, status). Explain the aggregate move, quantify how much of the 3-point change is mix and how much is within-model, and state which number you put in front of practice leadership and why.

Approach

Write the aggregate as a weighted mean: M = sum over pricing models p of w_p * m_p, where m_p is margin within p and w_p is p's share of the fee base. The weights must be the fee base, because margin is a fees-weighted ratio; weighting by engagement count answers a different question.
Apply the exact three-term decomposition: dM = sum (w_p1 - w_p0) * m_p0 + sum w_p0 * (m_p1 - m_p0) + sum (dw_p)(dm_p). Verify the three components sum to the reported +3.0 points before interpreting any of them.
Identify which weights moved and in which direction; the aggregate can only rise this way if fee share shifted toward the structurally higher-margin models, typically fixed_fee delivered under budget or retainer.
Guard against composition inside each stratum: repeat the same decomposition within pricing_model by practice_area and account_tier, so a within-model decline is not itself an unexamined mix effect.
Confirm both sides of the ratio are on the same clock and the same scope: numerator includes line_type IN ('fees','milestone','credit_note') with credits signed negative; the cost side includes all approved delivery hours, billable and non-billable alike.
Report within-model margin as the headline delivery signal and mix as a separate, named line with its own owner, since mix is a sales decision and margin is a delivery one.

Follow-up

Next quarter the mix reverts. What happens to the aggregate, and what will you have already told leadership so this is not a surprise?
Which of the four pricing models should not be pooled with the others even for a mix-adjusted number, and why?
How would you present this if the mix shift were deliberate strategy rather than accident?

Suggested practice checklist

For a candidate whose interviews will centre on A/B testing, metric movement and causal claims. Design comes before arithmetic, arithmetic before analysis, and the week ends by rehearsing the readout rather than the derivation.

Day 1 — Design one test end to end on paper

Take a single feature change and write the full design: randomization unit, the exact point of exposure, the primary metric with its grain, guardrails, allocation, planned duration, and the decision rule committed before any data exists.
Write why the randomization unit must sit at or above the level where treatment can spill over, and give one case where user-level randomization is still contaminated (shared accounts or devices, or two participants in the same marketplace).
State in advance what you will do if the primary metric is flat while a secondary metric is significant.

Deliverable: A one-page test design with a decision rule written before launch.

Day 2 — Power arithmetic until it is automatic

Compute required sample size per arm for a binary metric with the normal approximation, n is approximately 2 times (z for alpha/2 plus z for power) squared times p(1 minus p) divided by delta squared, for baselines of 2, 10 and 40 percent at a 5 percent relative lift, and note that for a fixed relative lift the requirement falls as the baseline rises because delta grows proportionally with p.
Redo the calculation for a continuous metric using variance in place of p(1 minus p), and show why a heavy-tailed quantity such as revenue per user needs either far more traffic or a capped version with a stated cap.
Convert one of the results into weeks given a weekly eligible traffic figure, then list the two honest ways to shorten it (accept a larger detectable effect, or reduce variance) and write why quietly lowering the power target is a decision to miss more real wins, not a speedup.

Deliverable: A small script or sheet that maps baseline, minimum detectable effect, alpha and power to sample size and weeks, cross-checked against a published calculator.

Day 3 — Variance and the unit-of-analysis problem

Take a ratio metric whose denominator is not the randomization unit (clicks per session, randomized by user) and compute the standard error twice, once naively at session level and once by the delta method or a user-level bootstrap, then record how much the naive version understates it.
Implement CUPED on simulated data: choose a pre-period covariate X measured before assignment, estimate theta as Cov(Y, X) divided by Var(X), and analyse Y minus theta times (X minus its mean) in place of Y. Confirm the variance of the adjusted outcome equals the raw variance multiplied by one minus the squared correlation between Y and X, so a correlation of 0.45 removes about 20 percent of the variance and not 80.
Now run that simulation a few hundred times and confirm the adjusted effect estimate is unbiased for the same effect rather than numerically identical to the raw one. Within any single run the two differ, sometimes by a large fraction of the true effect, because the two arms' pre-period covariate means never coincide exactly in a finite sample; they agree in expectation, which is the property that matters and the one to state out loud.

Deliverable: A notebook showing the adjusted estimator with a measurably smaller variance than the raw one, plus a repeated-simulation table showing the two estimators agreeing on average while differing run by run.

Day 4 — Validity threats you can actually test for

Run a sample ratio mismatch check as a chi-square goodness-of-fit test against the intended allocation, and write the three causes you would chase first (assignment logged before exposure, an arm-specific redirect or load failure, bot filtering applied asymmetrically).
Simulate peeking: generate A/A data, test daily at alpha 0.05 across 14 looks, record the inflated false positive rate, then apply an alpha-spending boundary or commit to a fixed horizon and confirm the rate returns to nominal.
Write how you would separate a novelty effect from a durable lift using the treatment effect plotted against days since first exposure, and what shape would change your recommendation.

Deliverable: One table showing the peeking false positive rate before and after correction, plus a written SRM triage list.

Day 5 — When randomization is not available

Write the identifying assumption for difference-in-differences (parallel trends in the absence of treatment), then plot pre-period trends for two candidate control groups and justify rejecting one of them.
Design a switchback test for a change where user-level randomization would leak across participants, choosing a time-block length against the carryover you expect and saying how you would detect carryover in the data.
List what an interrupted time series or a synthetic control buys you and the one thing neither can rule out: an unobserved shock that coincides with the launch.

Deliverable: A one-page memo recommending a single quasi-experimental design and naming its weakest assumption explicitly.

Day 6 — The readout query

Write the assignment-to-exposure join that returns exactly one row per unit per experiment, and handle units appearing in both arms by excluding and counting them rather than silently keeping one.
Compute the per-arm metric, its variance and the relative lift with a confidence interval in SQL, then reproduce the identical numbers in a notebook as a cross-check.
Add a segment breakdown and write the sentence that keeps it from being p-hacking: segments declared in advance, everything else reported as exploratory and corrected for multiplicity.

Deliverable: A single query that outputs the full readout table, matched to a notebook recomputation.

Day 7 — Present it to someone who will not read the appendix

Give a 10-minute readout of a real or simulated experiment in the order decision, number, uncertainty, caveat.
Have your listener ask "can we ship it" in the case where the primary is flat and a guardrail moved, and answer with a recommendation rather than a request for more data.
Rewrite your opening line so the recommendation lands before any methodology.

Deliverable: A one-page readout whose first line is the recommendation.

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.

Why do you want to work for Lufthansa Industry Solutions specifically, rather than a product-focused tech company?

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Describe a time when a project or model did not perform as expected. What did you learn from the failure, and how did you pivot?

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

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?