Interview guides · Hunter Douglas

Hunter Douglas Data Scientist Interview Questions & Guide 2026

Data scientist interview prep for Hunter Douglas: original practice on experiment design, SQL and metric reasoning, with worked solutions.

The 60-second brief

A Data Scientist at Hunter Douglas operates at the intersection of physical manufacturing excellence and cutting-edge digital transformation. As the global market leader in custom window coverings and architectural products, Hunter Douglas manages a highly complex, configurable product portfolio. Unlike off-the-shelf consumer goods, almost every window treatment is custom-made to order. This introduces unique challenges in demand forecasting, supply chain logistics, pricing elasticity, and manufacturing optimization that require sophisticated, data-driven solutions.

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

Focus areas

Trace an on-time miss to one node
Size safety stock under variable lead times
Price a service-level change in working capital

Overview

Depending on your team alignment, you will either drive operational efficiency within the AI Analytics & Digital Transformation team or shape commercial strategy alongside executive leadership. On the operations side, you will build predictive demand models, optimize inventory, and deploy generative AI applications to streamline supply chain workflows. On the commercial side, you will partner directly with the CFO, Head of Strategy, and sales leaders to design pricing optimization algorithms, analyze product mix profitability, and model customer behavior.

Ultimately, your work as a Data Scientist directly impacts the bottom line of a multi-billion dollar global business. You will not just build models in isolation; you will deploy scalable machine learning pipelines, design custom AI copilots, and translate complex technical insights into actionable strategies that influence executive decision-making.

The Hunter Douglas interview process

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

Foundational Screening

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.

Technical Assessment

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

What to demonstrate

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

How to prepare

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

System Design Interview

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.

Cross-Functional Panel

A day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.

What to demonstrate

Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method

How to prepare

Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times

Where candidates lose points

Treating shipped units as demand

Shipments are censored at available inventory: on a stockout day the recorded quantity is a supply ceiling, not customer intent, and orders that were never placed because the item showed as unavailable leave no row at all. A forecast fitted on that history learns the constraint, under-forecasts the fast movers that stock out most often, and produces the replenishment that causes the next stockout, so the error compounds in one direction rather than averaging out. The fix is to model demand_qty rather than shipped_qty, flag stockout days with stockout_flag and treat them as censored (fit with a censored likelihood, or estimate unconstrained demand from uncensored periods and comparable locations), and to report how much of the history was censored alongside any accuracy number.

Computing average inventory from period-end snapshots

Shipments cluster before period close, so the month-end on-hand position is systematically the lowest point of the month; turns computed against it are biased high and days of supply biased low, frequently by ten to twenty percent, and the bias grows precisely when close-period push is strongest. The same shape of error appears when a daily snapshot is joined to shipment events on date equality: a SKU with several legs on one day fans the snapshot out and multiplies the valued inventory. Average across every daily snapshot in the window for the denominator, and join snapshots to events with an explicit as-of condition and a row-count check at each grain before aggregating.

Comparing periods without accounting for seasonality or day-of-week

Compare whole weeks against whole weeks and check whether the same swing appeared in prior cycles or prior years before attributing it to anything you changed. Weekday and weekend populations often differ enough that a Tuesday-to-Saturday comparison is meaningless.

Reading an observational correlation as a causal effect

Name the confounder you are most worried about and the design that would remove it: an experiment, a difference-in-differences with a checked pre-period trend, an instrument, or a regression discontinuity. When none is available, state which direction the bias likely runs and bound the claim accordingly.

Questions & practice

What are the trade-offs between traditional statistical forecasting methods (like ARIMA) and machine learning approaches (like XGBoost or LSTM) in a supply chain context?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Set a baseline first, so any model has something honest to beat.
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?

How do you balance statistical accuracy with business interpretability when presenting a complex pricing model to non-technical sales directors?

Medium · machine learning and modelling

Approach

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

Follow-up

How would you choose the decision threshold, and who owns that choice?
What would you monitor after launch to know the model is still valid?

Reconstruct replenishment cycles from a daily inventory series

Medium · event sessionisation · state reconstruction · lead time

For one sku-location you have a daily series: inventory_date, on_hand_qty, in_transit_qty, on_order_qty, allocated_qty, shipped_qty, demand_qty, reorder_point_qty, stockout_flag. There is no purchase-order table. Infer the replenishment cycles: a cycle opens on the first day inventory position falls to or below reorder_point_qty, and closes on the first subsequent receipt. Receipts must be inferred from the series itself. Return one row per cycle with trigger date, receipt date, inferred lead time in days, demand during the cycle and stockout days, then report mean and standard deviation of the inferred lead time.

Approach

Derive the receipt signal from the stock balance rather than looking for a column that does not exist: on_hand today equals on_hand yesterday plus receipts minus units shipped, so implied_receipt = on_hand_qty.diff() + shipped_qty, and a positive value means goods arrived.
State what else can make that quantity positive: a cycle-count adjustment, a customer return, or a scrap reversal. Corroborate each candidate receipt with a matching fall in in_transit_qty on the same or adjacent day, and discard the ones with no corroboration.
Debounce arrivals. A load split across two dock shifts produces positive implied receipts on consecutive days; collapse runs of receipt days separated by less than a chosen gap into a single arrival and make the gap an explicit parameter.
Compute inventory position as on_hand + in_transit + on_order - allocated, not on hand alone, or every day after a trigger looks like another trigger and the cycle count explodes.
Walk the series once with an explicit state variable (awaiting-trigger, awaiting-receipt) rather than trying to vectorise the pairing. The state machine is what makes cycles non-overlapping, and it is what a reviewer can check line by line. Be clear that cycles defined this way do not tile the calendar: the stretch from a receipt until the position next falls to the reorder point sits inside no cycle.

Worked solution (35 min)

Sort by inventory_date, assert no duplicate dates and no calendar gaps, and compute position = on_hand_qty + in_transit_qty + on_order_qty - allocated_qty.
Compute implied_receipt = on_hand_qty.diff() + shipped_qty and mark receipt days where it is positive and in_transit_qty fell.
Collapse consecutive receipt days into single arrivals using a gap threshold of one day.
Iterate the series with a two-state machine, emitting a cycle row when a trigger is followed by an arrival, and carrying an open trigger forward across days.
For each cycle sum demand_qty and stockout_flag between trigger and receipt, then compute mean and standard deviation of the lead times across cycles.

Expected result: A cycle table with non-overlapping intervals ordered in time, each with receipt_date strictly after trigger_date, plus two scalars: mean inferred lead time and its standard deviation across cycles. The cycles cover only the trigger-to-receipt stretches, so they will not add up to the length of the series; the post-receipt stretches where stock is still above the reorder point are outside every cycle by definition.

Verify it yourself

Assert no two cycles overlap and that every receipt_date is strictly later than its trigger_date.
Partition the calendar into four exhaustive categories and sum demand_qty over all of them: cycle days (trigger to receipt), post-receipt gap days (receipt to the next trigger), the head before the first trigger, and the tail after the last receipt. The total must equal total demand_qty. Summing cycles plus head and tail alone under-counts by exactly the gaps, which is the arithmetic proof that cycles do not tile the series.
Confirm the day counts of those same four categories sum exactly to the number of days in the series. A shortfall means two cycles overlap or a boundary day is double-counted. Cycle count times mean cycle length is not a substitute for this check, since it ignores the gaps.
Confirm total inferred receipts across the window approximately equals total shipped_qty plus the change in on_hand_qty from first day to last.

Follow-up

Two orders are open at once because the trigger fired again before the first arrived. How does your state machine change?
The policy quotes lead time in working days. What do you need from dim_location to convert, and where does the timezone matter?
You now have mean and standard deviation of lead time. Write the safety stock this implies and say which assumption you are least comfortable with.

How would you design and evaluate a custom LLM-based copilot to help internal teams query complex supply chain documentation?

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
Compute rates by summing numerator and denominator separately, never by averaging rates.
State the window function and its partition and ordering out loud before writing 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?

How do you optimize a query in Google BigQuery when dealing with multi-terabyte datasets to minimize slot usage and cost?

Medium · sql and data manipulation

Approach

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

Follow-up

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

What is your strategy for joining highly unstructured IoT sensor data from manufacturing equipment with structured ERP tables (like SAP or Oracle)?

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.
Compute rates by summing numerator and denominator separately, never by averaging rates.

Follow-up

How does the query change if the join becomes one-to-many?
What breaks if events arrive late or out of order?

Perfect order rate against the original promise in local time

Hard · timezone boundaries · order-grain rollup · semi-join

Compute weekly perfect order rate at order grain. An order qualifies only if every fct_order_line has line_status = 'shipped_full', every line's actual_delivery_at_utc falls on or before original_promised_delivery_date at the ship-from node's local end of day (dim_location.timezone holds IANA names), and no fct_shipment_leg reached through shipment_id carries exception_code in ('damage','address_issue') or leg_status in ('refused','lost'). The denominator is orders whose original_promised_delivery_date lands in the week. Undelivered lines fail. Publish with a fourteen-day lag and declare invoice accuracy as out of scope.

Approach

Convert the event into local time rather than comparing against a UTC-anchored deadline: (actual_delivery_at_utc AT TIME ZONE 'UTC' AT TIME ZONE loc.timezone)::date <= original_promised_delivery_date. Casting the promise date to a timestamp puts the cut-off at UTC midnight starting the promise day, which is up to a full day early and wrong in both directions depending on the node's offset.
Evaluate the three components as booleans at line grain, then roll to the order with BOOL_AND, coercing a NULL actual_delivery_at_utc to FALSE explicitly so an undelivered line fails rather than propagating NULL and dropping the order out of the comparison.
Test the shipment exceptions with EXISTS against fct_shipment_leg, never with a join. One shipment has many legs and one order has many lines, so a join multiplies the order into the denominator and changes the metric.
Aggregate to order grain first, then count qualifying orders over orders promised in the week. The rate is orders over orders; a line-level average is a different and easier metric.
Apply the publication lag by restricting to weeks whose promise dates are at least fourteen days old, and emit the three component pass rates beside the headline so any movement is attributable to completeness, timeliness or exceptions.

Worked solution (40 min)

CTE line_flags: join fct_order_line to dim_location on ship_from_location_id, producing is_full, is_on_time using the AT TIME ZONE conversion with COALESCE to FALSE, and has_exception from an EXISTS over fct_shipment_leg.
CTE order_flags: GROUP BY order_id with BOOL_AND(is_full), BOOL_AND(is_on_time), BOOL_AND(NOT has_exception) and MIN(original_promised_delivery_date) for bucketing.
Aggregate per DATE_TRUNC('week', promise date): COUNT() as denominator, COUNT() FILTER (WHERE all three) as perfect_orders, and the three component counts.
Restrict output to weeks ending at least fourteen days ago.
Re-run the on-time expression once with a naive UTC comparison and record the delta as evidence the conversion is live.

Expected result: One row per week with orders_in_denominator, perfect_orders, rate, and three component pass rates, where the rate is less than or equal to every component rate and weeks inside the fourteen-day lag are absent from the output.

Verify it yourself

perfect_orders is less than or equal to the count passing any single component, for every week.
Switching the deadline to a naive UTC comparison shifts the on-time component by roughly the share of orders at nodes offset from UTC. No change at all means the conversion is not being applied.
The denominator equals COUNT(DISTINCT order_id) over orders promised in the week and does not change when the legs EXISTS clause is removed.
No order_id appears in more than one week.

Follow-up

All three component rates are flat but perfect order fell two points. How is that arithmetically possible, and what does it tell you about where the failures now land?
A node sits at UTC+13 and many orders are promised on the last day of the month. What does that do to your weekly and monthly buckets?
How do you handle an order whose lines ship from two nodes in different timezones, and which node's clock owns the promise?

If the CFO asks you to identify the primary drivers of margin erosion across our North American commercial channels, how would you structure your analysis?

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?

Imagine our logistics team reports a sudden bottleneck in shipping con…

Medium · product sense and metrics

Imagine our logistics team reports a sudden bottleneck in shipping container utilization. What data sources would you gather, and what models would you build to optimize container packing and dispatch schedules?

Approach

Name one primary metric, then the guardrail that stops it being gamed.
Restate the decision this analysis has to support, and who acts on the answer.
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?
How would you detect that the metric is being gamed rather than genuinely improving?

How would you design an analytical framework to measure price elasticity for a premium, highly customizable product line?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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.

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?

A fill rate that improves by cancelling what it misses

Easy · gameable metrics · denominator integrity · audit series

Unit fill rate is defined as SUM(shipped_qty) over lines shipped on or before requested_ship_date, divided by SUM(ordered_qty) over lines with requested_ship_date in the week, excluding lines with line_status = 'cancelled_by_customer'. A node is now paid on it. Using fct_order_line (line_status, short_reason_code, ordered_qty, shipped_qty, cancelled_qty, substituted_sku_id, ordered_at_utc, requested_ship_date, account_id, sku_id, ship_from_location_id), list the routes by which this number rises without service improving, and specify the audit series that detects each.

Approach

Attack the denominator first: removing a line removes a miss, and the only exclusion the definition grants is customer cancellation, a status written by the same operation being scored.
Name re-entry: a short line closed and re-keyed as a fresh order arrives with a new requested_ship_date, so the miss leaves the scoring window entirely instead of being cancelled inside it.
Name intake suppression: a line never created because the item showed unavailable enters neither side of the ratio, which is the only route that leaves no trace in this table and therefore needs demand-side evidence to detect.
Name numerator inflation: substitution counted as a fill without saying so, partial shipments credited at ordered_qty, and shipping early against a requested date that was itself edited.
Attach one audit series to each route from the same table: weekly cancelled_by_customer share by node, share of accounts re-ordering the same sku within seven days of a cancellation, substitution share, and median hours from ordered_at_utc to terminal status.
Close with the structural fix: publish cancellation mix beside the rate, and pair it with perfect order rate, so that cancelling an order cannot improve both numbers at once.

Worked solution (20 min)

Compute the weekly fill rate per node by re-summing shipped_qty and ordered_qty, never by averaging line-level ratios.
Compute cancellation mix beside it: cancelled_qty split by line_status in ('cancelled_by_customer','cancelled_no_supply') as a share of ordered_qty, per node per week.
Compute the re-entry series: lines whose (account_id, sku_id, ship_from_location_id) reappears within seven days after a cancellation or a short ship.
Compute substitution share and median hours from ordered_at_utc to terminal status, both by node and week.
Quantify the exposure algebraically: moving x units of unmet demand out of the denominator takes a rate r to r divided by (1 minus x/D), then read that against normal week-over-week variation.

Expected result: Four named routes, each with its audit series, plus the arithmetic that sizes the exposure: excluding one percent of the denominator lifts a 95.0 percent fill rate to 95.96 percent, which exceeds a typical week-over-week swing, so any monitoring threshold has to sit well below a one-point move. The recommendation is to publish cancellation mix and perfect order rate alongside the headline rather than to tighten the definition alone.

Verify it yourself

The identity holds: denominator units equal shipped plus short plus backordered plus no-supply-cancelled units, with customer cancellations excluded and reported separately.
Recomputing after relabelling a share of cancelled_no_supply units as cancelled_by_customer reproduces the r divided by (1 minus x/D) formula to rounding.
A node improving genuinely shows a falling cancelled_no_supply share; a gamed one shows that share falling while cancelled_by_customer rises by a similar quantity in the same weeks.

Follow-up

Which of these routes still works if you score lines rather than units?
A node's cancelled_by_customer share doubled in one week. What confirms or clears it before you accuse anyone?

OEE fell six points on a constraint work centre

Medium · oee decomposition · mix shift · manufacturing

OEE on a constraint work centre fell from 0.72 to 0.66 over one month and plant leadership has requested maintenance spend. From fct_production_run (planned_production_seconds, run_seconds, unplanned_downtime_seconds, changeover_seconds, ideal_cycle_seconds_per_unit, planned_qty, total_qty, good_qty, scrap_qty, rework_qty, downtime_reason_code, sku_id, run_status) and dim_sku, decompose the six points into availability, performance and quality, then decide whether the performance term is a machine problem or a SKU mix problem. Deliverable: the decomposition and one recommendation.

Approach

Rebuild OEE by re-summing: SUM(good_qty * ideal_cycle_seconds_per_unit) over SUM(planned_production_seconds) for each month. Averaging per-run OEE gives a five-minute run the same weight as an eight-hour run and produces a figure that belongs to no work centre.
Compute the three factors on the identical row set: availability = SUM(run_seconds) / SUM(planned_production_seconds); performance = SUM(total_qty * ideal_cycle_seconds_per_unit) / SUM(run_seconds); quality = SUM(good_qty) / SUM(total_qty). Verify their product equals the re-summed OEE; if it does not, the three calculations are running over different rows.
Attribute the move on the log scale, where the factors are exactly additive: ln(OEE1/OEE0) = ln(A1/A0) + ln(P1/P0) + ln(Q1/Q0). This removes any argument about how to split the cross-terms and gives each factor a defensible share of the six points.
If performance carries the move, separate a slower machine from a harder product mix before recommending anything. ideal_cycle_seconds_per_unit is SKU-specific, so recompute month two's performance holding month one's SKU volume mix fixed. Performance that is flat under a fixed mix means the line runs as fast as ever on slower SKUs.
Interrogate the denominator itself. planned_production_seconds excludes unstaffed hours, so adding or removing a shift changes OEE with nothing physical having changed, and whether changeover_seconds sits inside or outside planned time decides whether a changeover appears as an availability loss at all.

Follow-up

Availability fell and downtime_reason_code is mostly 'material_shortage'. Is that a maintenance problem, and who owns it?
rework_qty is 400 against total_qty of 10,000. Are reworked units counted once or twice in total_qty, and how does each answer change the quality factor?
What guardrail stops a plant from hitting an OEE target by scheduling long runs of its fastest SKU?

Suggested practice checklist

Roughly 90 minutes a night on weekdays with one longer weekend block. The plan deliberately cuts scope rather than compressing everything, on the assumption that finishing one thing a night beats half-starting four.

Day 1 — Fix the scope and set a baseline

Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list for everything else and keep it visible all week.
Take one 20-minute SQL prompt and one 10-minute metric question cold, and write the single sentence that says what blocked each attempt, since that sentence is what decides which two topics get the most evenings.
Set the week's one rule: one problem finished to completion every night, including the night you only have 40 minutes.

Deliverable: A one-page scope with an explicit not-doing list and two cold attempts, each carrying one sentence on what blocked it.

Day 2 — One query pattern, written three times

Choose the single pattern most likely to appear (a cohort retention grid, or a funnel counted by user) and write it three times from a blank file rather than editing the previous attempt.
On the third attempt, write the grain of every CTE as a comment before writing its body.
Stop at 90 minutes even if the third version is imperfect, and write the one thing you would fix with another hour.

Deliverable: Three independent versions of the same query plus a note on what changed between them.

Day 3 — Only the statistics you will be asked to defend

Write, in under 200 words, how you would decide whether a difference between two groups is real: the test, its assumptions, and what you would switch to when an assumption fails.
Compute a 95 percent confidence interval for a difference in proportions by hand on realistic numbers, then write in one sentence what changes if the two samples are paired rather than independent.
Write your answer to "what does a p-value mean", check it against a definition, and delete the version that describes it as the probability the hypothesis is true.

Deliverable: A 200-word written answer and one hand-computed interval you can reproduce under pressure.

Day 4 — One case, and the assumptions holding it up

Answer one product case aloud in 20 minutes with a recording running, then listen back with a pen and mark every claim you asserted without saying what it rested on: an assumed user behaviour, an assumed data source, an assumed baseline rate, an assumed grain.
Pick the three assumptions the recommendation actually depends on, write how you would check each one against data, and say which one being wrong would flip the recommendation rather than merely weaken it.
Write the four-step structure you used onto a card small enough to hold in working memory when you are nervous.

Deliverable: One recording, three load-bearing assumptions each with a written check, and a four-step structure card.

Day 5 — Your own work, timed

Write a 90-second version and a four-minute version of your main project, and time both out loud rather than reading them.
Prepare answers to the two follow-ups that always come: what you would do differently, and how you knew it worked.
Put one number in the first sentence and be able to say exactly where that number came from and what it excludes.

Deliverable: Two timed narratives with one defensible number in the opening line.

Day 6 — The one full rehearsal, in a longer weekend block

Run a 60-minute mock covering query work, a case and a behavioural question in a single sitting with no breaks, because sustained attention is the thing evenings have not trained.
Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.

Deliverable: Mock notes naming three failure moments with a specific fix written under each.

Day 7 — Taper

Write the 20-minute warm-up you will actually do on the morning of the interview: one query you can already write from a blank file, one metric you can define out loud, and nothing you have never seen before.
Re-read only your own notes from this week, and open no new material.
Write down the logistics: the tool you will be asked to work in, whether lookups are allowed, and the sentence you will use when you do not know something.

Deliverable: A one-page card holding the case structure, the project numbers, and the logistics.

Where your data came from

Data people depend on systems owned by other teams, and much of the job is negotiating for instrumentation, access, or a fix to a broken pipeline. Prepare an example of getting something changed upstream that you did not control. Describe what you asked for, what you traded, and how you worked while you waited.

Tell me about a time when you had to convince a senior business stakeholder to adopt an AI-driven tool over their traditional, intuition-based planning process.

Medium · behavioural and stakeholder questions

Approach

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

Follow-up

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

Explain forecast uncertainty to a non-technical general manager

Easy · communicating uncertainty · service level · executive communication

A general manager who does not use statistics must approve a 2.1 million dollar finished-goods build. Your recommendation rests on a demand forecast with a wide error band on the Z-class items and on a 97 percent cycle service target. You get two minutes, no formulas, and the question you will get back is whether the forecast is right. Deliver the explanation as you would say it aloud, including how you describe the error band in physical terms and what the 97 percent number does and does not promise.

Approach

Convert the error band into the units the approver already thinks in: weeks of cover, pallets, or dollars at risk, not a percentage or a confidence interval.
Answer the right-or-wrong question directly rather than deflecting: the forecast will be wrong, the size of the wrongness is what you measured, and the build is sized against that size.
State precisely what the 97 percent buys: it is a cycle service level, the probability of not running out during one replenishment cycle, so roughly three cycles in a hundred see a stockout. It is not the share of units shipped from stock. Unit fill rate also depends on the replenishment quantity, and when that quantity is large relative to the standard deviation of lead-time demand, which is the normal case outside lot-for-lot ordering, the fill rate sits above the cycle service number, often above 99 percent at a 97 percent cycle target.
Give the two-sided consequence in money: what the build costs to carry at the applicable cost of capital plus obsolescence risk on shelf-life items, against the margin at risk from the stockouts it prevents.
Close with the decision you want and the trigger that would reverse it, for example a lag-7 bias check after four weeks that reopens the number.

Follow-up

The GM says just give me one number. What do you give, and what do you refuse to give?
How would your answer change if the items were frozen with a 90-day shelf life?

Prioritise transport, plant and commercial in one week

Medium · prioritisation · stakeholder management · decision value

Three requests land in the same week. Transport wants landed cost per delivered unit split by lane and mode from fct_shipment_leg for a carrier bid that closes in nine days. A plant manager wants OEE on two work centres decomposed into availability, performance and quality from fct_production_run for a capital request due next month. Commercial wants a fill-rate root cause from fct_order_line for an account review on Thursday. You are the only data scientist and have about four working days. Give your order, the rule that produced it, and what you say to the two who wait.

Approach

Rank by the decision each request feeds and by whether your input can still change it, not by seniority or by who asked loudest: a bid closing in nine days is a live decision with a large irreversible spend attached, a capital request due next month has slack, and an account review has a fixed date but a smaller reversible decision.
Check reversibility and blast radius: carrier rates lock for a contract period across every lane, so an error or an absence there is expensive for a year, while the fill-rate story can be revised next week.
Look for the cheap partial that unblocks someone else: the fill-rate cut by node, by short_reason_code and by week is a few hours of work and covers most of what commercial needs on Thursday, so it does not have to wait behind the bid.
Sequence with explicit time boxes: the commercial cut first because it is short and date-locked, the lane and mode cost analysis next with the allocation basis for multi-stop loads stated up front, and the OEE decomposition scheduled into the following week with a date the plant manager can hold you to.
Tell the plant manager directly and early, with a date rather than a maybe, and say what you need from him in the meantime so the delay produces something.
Escalate the trade-off rather than absorbing it silently: your manager should know a capital request slipped a week, because that is a business choice and not yours alone to make.

Follow-up

The plant manager escalates and his director asks you to reorder. What do you do?
Which of the three would you push back on entirely, and what would you offer instead?