As a Data Scientist at ASML, you play a vital role at the cutting edge of the global semiconductor industry. Your primary mission is to transform complex, high-dimensional manufacturing, supply chain, and operational data into actionable insights and robust predictive models. By bridging advanced analytics with extreme engineering, you help optimize lithography systems and planning operations that enable the world's leading chipmakers to build smaller, faster, and more energy-efficient microchips.
The scope of this role spans across multiple critical domains, including business intelligence, machine learning deployment, equipment performance optimization, and supply chain forecasting. You will collaborate directly with software engineers, applications specialists, and cross-functional hardware teams to diagnose operational bottlenecks and deploy scalable data pipelines. Whether you are building regression models to streamline planning or designing automated dashboards to monitor fab efficiency, your work directly influences both internal operations and customer success.
Expect a collaborative, highly intellectually stimulating environment where curiosity and structured problem-solving are valued just as much as technical execution. While the technical demands are rigorous, fosters a respectful, team-oriented culture where you are given the autonomy to drive projects from conception to deployment. Success in this role requires a balance of rigorous statistical foundations, robust coding practices, and the communication skills needed to translate technical findings to upper management.
Recruiter Screening
reportedWhoever runs this call is usually not a practitioner. They take notes, and a hiring manager skims those notes later, so the real question is whether your work survives being written down by someone outside the field. Test every project sentence against that: could a non-specialist repeat it correctly without knowing what a propensity score is? Carry a plain-language version of each project and one reason you want this particular role that you could not copy onto another application. Vagueness at this stage reads as inexperience, even when the underlying work was genuinely deep.
What to demonstrate
- Whether a non-specialist can restate your projects accurately, since their paraphrase is what reaches the hiring manager
- Whether your reason for wanting the role points at the work itself rather than the company's reputation
- Whether your language signals the level being screened for: what you decided yourself versus what you were handed
How to prepare
- Write a two-sentence, jargon-free version of each major project: the question nobody could answer, and the decision your work changed. Read it to someone outside data and have them repeat it back
- Point your 'why this role' answer at something concrete in the job description or the product surface you would be working on, and keep it to two sentences
- Have two questions ready about measurement: which metric the team is held to, and who acts on an analysis once it lands
Technical Screening
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Panel Interviews
reportedWhere a loop includes a partner from outside the data team, that conversation usually carries the same weight as the technical ones and gets the least preparation. The person opposite you will not follow a derivation and does not need to. They are working out whether having you involved would make their decisions better or slower. The failure mode is not being too technical. It is answering a question about a decision with a description of your method, leaving the translation to them. What they carry into the debrief is the sentence you handed them, not the analysis underneath it.
What to demonstrate
- Whether a statistical result arrives as something the partner could act on, with the one caveat that would change their decision kept and the rest left out
- Whether you can state what you need from their side, in their terms: instrumentation that does not exist yet, a definition they own, or a holdout they have to agree to
- Whether uncertainty is given as a range someone can plan against, rather than as hedging that invites them to ignore the result
- Whether you ask what decision is actually on the table before explaining anything
How to prepare
- Take a result you know well and write the version for someone who stops reading after one sentence, then the three-minute version, and check the short one is not the long one with the qualifications stripped out
- For a past project, list everything you asked a non-technical partner for and how you phrased it, then rewrite each ask so it names what goes unmeasured without it
- Practise saying where a result does not apply, out loud, in one sentence that a partner could repeat accurately to someone else
Technical Deep Dive
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
Behavioral Interview
reportedThis round decides whether you owned a decision or watched one happen nearby. Interviewers for data roles listen for the point where the analysis stopped being a report and started changing what someone did, so build each story around that hinge: what was going to happen by default, what you found, and what happened instead. The most common weakness is a story that ends at delivery. If you can name the decision your work changed and the number that moved because of it, most follow-ups become easy.
What to demonstrate
- Whether the decision was yours to influence, or whether you are narrating a team outcome in the first person
- The counterfactual: what would have been done without your analysis, and why that default was worse
- How far your involvement ran past the handoff, and whether you checked that the change did what you predicted
How to prepare
- Pick three projects and write one sentence for each naming the decision-maker, the choice in front of them, and what they chose after seeing your work. If you cannot name a person and a choice, the story is not ready for this round.
- Reconstruct the baseline for your strongest project from the original query or dashboard rather than memory, so the before-number survives a follow-up asking where it came from.
- Prepare an honest version of a project where your recommendation was overruled, including what you did with the analysis afterwards.
PracHub editorial advice for the preparation topics above.
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.
Sizing safety stock as z times sigma_D times the square root of lead time
That form assumes lead time is deterministic. When lead time itself varies, the standard deviation of demand over lead time is sqrt(L_bar * sigma_D^2 + D_bar^2 * sigma_L^2), and the second term dominates whenever supply is unreliable, so the familiar formula can understate the requirement severalfold for a long, variable inbound lane. Two further preconditions are routinely forgotten: demand is assumed independent across periods, which promotions and order batching break, and z maps to cycle service level (the probability of no stockout in a replenishment cycle), not to fill rate, which additionally depends on order quantity through the unit normal loss function. Quoting a z-derived number as a fill rate overstates achieved service, and the gap widens as order quantity shrinks.
Optimising accuracy on a heavily imbalanced target
State the base rate first, then choose the metric from the relative cost of a false positive against a false negative: precision and recall at the operating threshold, PR-AUC, or expected cost. At a 1 percent positive rate, predicting the majority class for everyone scores 99 percent accuracy and is worthless.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
What are the mathematical foundations and trade-offs between gradient …
What are the mathematical foundations and trade-offs between gradient boosting and deep neural networks for time-series forecasting?
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.
- Pick an evaluation metric that matches the cost of each error type, not a default.
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
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.
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.
Cluster bootstrap for landed cost per delivered unit
You have delivered outbound legs from fct_shipment_leg: leg_id, shipment_id, leg_seq, carrier_id, origin_location_id, destination_location_id, shipped_units, cube_m3, linehaul_cost_cents, fuel_surcharge_cents, accessorial_cost_cents, expedite_premium_cents. In this extract the linehaul for a multi-stop load is booked entirely on leg_seq 1, so allocate it across the load's legs by cube first. Then estimate the difference in landed cost per delivered unit between two carriers on lanes both serve, with a 95 percent interval. Write the resampling yourself; no bootstrap library.
Approach
- Allocate the linehaul before anything else, in proportion to each leg's cube over the load's total cube, and keep the other three cost columns where they already sit. State the choice: allocating by weight instead reorders lanes whenever freight is bulky rather than dense.
- Restrict to the set of lanes both carriers actually serve in the window. Comparing over all lanes measures which carrier was assigned the cheap lanes, not which carrier is cheaper.
- Resample shipments, not legs. Legs of one load share a single allocated linehaul and a single dispatch decision, so treating them as independent draws understates the variance of the estimate.
- Recompute the statistic as a ratio of sums on every resample, total allocated cost over total shipped units. Taking the mean of leg-level cost-per-unit values instead gives a different estimand in which a one-unit leg counts as much as a full truckload.
- Report both the raw difference and a lane-standardised difference where lane weights are fixed at the pooled volume share, and say which one you would put in front of a decision maker.
Follow-up
- The interval crosses zero. What would you need in volume or in window length to resolve a difference of 2 cents per unit?
- One carrier's accessorials are rising while its linehaul is flat. What is that signature telling you about execution versus rates?
- How does your interval change if one shipment accounts for 15 percent of the units?
Given a table of customer support tickets, how would you write a query…
Given a table of customer support tickets, how would you write a query to find the top three longest-resolving issues per region?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Say which table is the grain you start from, and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
Follow-up
- How does the query change if the join becomes one-to-many?
- What breaks if events arrive late or out of order?
Write a query to identify duplicate records in a large manufacturing t…
Write a query to identify duplicate records in a large manufacturing telemetry table using common table expressions.
Approach
- State the window function and its partition and ordering out loud before writing it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
How would you use SQL window functions to calculate rolling averages a…
How would you use SQL window functions to calculate rolling averages and running totals for machine sensor logs?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
Follow-up
- How would you verify this result without re-running the same query?
- What breaks if events arrive late or out of order?
Dead stock cells excluding SKUs used as substitutes
Find dead stock: sku_id x location_id cells whose latest fct_inventory_daily row has on_hand_qty > 0 and which have no fct_order_line with that sku_id and ship_from_location_id in the last 90 days. Also exclude any SKU that filled another line as a substitute in that window; fct_order_line.substituted_sku_id is NULL on every line that was not substituted. Value each cell at on_hand_qty * standard_cost_cents taken from the snapshot, not from dim_sku. Return the cell, days since its last demand line, and value, ordered by value. Treat line_status = 'cancelled_no_supply' as demand.
Approach
- Take the latest snapshot per sku-location with ROW_NUMBER() OVER (PARTITION BY sku_id, location_id ORDER BY inventory_date DESC) = 1, or an equivalent MAX(inventory_date) join, and keep rows with on_hand_qty > 0.
- Anti-join demand with NOT EXISTS against fct_order_line on both sku_id and ship_from_location_id over the 90-day window, counting every line status including cancelled_no_supply, because unmet demand is still demand and a cell with it is not dead.
- Write the substitute exclusion as NOT EXISTS, or force substituted_sku_id IS NOT NULL inside the subquery. NOT IN over a column that contains NULLs evaluates to NULL for every candidate that is not an explicit match, so the predicate is never true and the result set is empty.
- Value from the snapshot's standard_cost_cents rather than dim_sku, since the dimension carries only the cost in force today and is revised at period close without back-dating.
- Get days since last demand from a LEFT JOIN LATERAL MAX(ordered_at_utc), and report cells that never had a line in a separate bucket instead of as a very large number.
Worked solution 30 min
- CTE latest_snap: ROW_NUMBER over sku_id, location_id ordered by inventory_date DESC, filtered to rn = 1 and on_hand_qty > 0.
- CTE last_demand: MAX(ordered_at_utc) grouped by sku_id, ship_from_location_id over all of fct_order_line, with no status filter.
- Select from latest_snap LEFT JOIN last_demand, keeping rows where the max is NULL or older than 90 days.
- Add AND NOT EXISTS (SELECT 1 FROM fct_order_line o WHERE o.substituted_sku_id = latest_snap.sku_id AND o.ordered_at_utc >= now() - interval '90 days').
- Project value_cents = on_hand_qty * standard_cost_cents, order by it descending, and run the same query once with NOT IN to see the empty result for yourself.
Follow-up
- The query returns zero rows. Name the two likeliest causes and say which you would test first, with the one-line test.
- How do you separate genuinely dead stock from a seasonal item out of season and from a lifecycle_status = 'pre_launch' SKU?
- The same SKU is dead at one node and short at another. Write the query that finds that pattern and say what it implies about the policy.
How would you design a comprehensive product metric design framework f…
How would you design a comprehensive product metric design framework for a new internal machine-learning dashboard?
Approach
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
- 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?
What key performance indicators would you track to measure the success…
What key performance indicators would you track to measure the success of an automated data pipeline deployment?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- State what result would change your recommendation, so the answer is falsifiable.
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 we notice a sudden metric drop in our supply chain operational effi…
If we notice a sudden metric drop in our supply chain operational efficiency index, how would you structure your diagnosis?
Approach
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Fix the population and the time window before naming any metric.
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?
How do you determine statistical significance when dealing with low-vo…
How do you determine statistical significance when dealing with low-volume, high-value semiconductor equipment trials?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Say whether units interfere with each other, and switch design if they do.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
What steps would you take if your variant experiences an unexpected dr…
What steps would you take if your variant experiences an unexpected drop in conversion midway through an experiment?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
An OEE target that makes the plant run the wrong SKUs
A constraint work centre is put on an OEE target. Using 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, run_status) and fct_inventory_daily (on_hand_qty, demand_qty), define OEE precisely, decide whether planned changeover sits inside or outside the denominator, and name the guardrail that stops the target being met by running long batches of whatever is easiest. Defend the conflict rather than hiding it.
Approach
- Write the metric exactly: SUM(good_qty * ideal_cycle_seconds_per_unit) over SUM(planned_production_seconds), re-summed for every roll-up, and show that it factors into availability times performance times quality so a movement is attributable before anyone argues about it.
- Settle the changeover question in writing, because it decides gameability. Leaving planned changeover inside the denominator makes OEE fall whenever variety rises, which is the behaviour the target would then buy; excluding it protects mix but hides real capacity loss. Exclude it and carry changeover_seconds as its own reported series.
- Use ideal_cycle_seconds_per_unit per SKU and work centre rather than a line average, since a line-average nameplate rate turns performance into a function of which SKU happened to run.
- Set the guardrail on demand-weighted schedule adherence, good_qty against planned_qty at SKU level weighted by what the network is short of, paired with finished-goods days of supply for the SKUs that were skipped.
- State the conflict plainly: a plant maximising OEE lengthens runs and drops small-volume SKUs, which raises the score and lowers service, so the two metrics are published together and the target is set on the pair, not on OEE alone.
- Scope the metric to constraint work centres, because OEE on a non-constraint rewards making inventory nobody is waiting for, and scope is part of the definition rather than a footnote.
Worked solution 25 min
- Compute the factors per work centre per shift: availability = run_seconds / planned_production_seconds, performance = (total_qty * ideal_cycle_seconds_per_unit) / run_seconds, quality = good_qty / total_qty.
- Verify the identity on every row: the product equals good_qty * ideal_cycle_seconds_per_unit / planned_production_seconds.
- Build the mix control as SKU share of run_seconds per period, then split a period-over-period OEE move into within-SKU rate change and mix change.
- Build the guardrail from demand-weighted schedule adherence plus days of supply from fct_inventory_daily for the SKUs on the plan.
- Write the target as a pair with an explicit adherence floor, and state the decision rule for the case where OEE rises while adherence falls.
Follow-up
- Availability rose and performance fell in the same week. Give two explanations and the query that separates them.
- Where does rework_qty enter your definition, and what does that choice reward?
On-time rate improved the week the promise field changed
Customer on-time delivery jumped from 88 percent to 95 percent in one week and has stayed there. That week a release began writing re-confirmed dates into fct_order_line.promised_delivery_date, while original_promised_delivery_date still carries the first promise and both columns are populated for all history. Using fct_order_line (actual_delivery_at_utc, promised_delivery_date, original_promised_delivery_date, line_status) and dim_location.timezone, determine how much of the seven points is real. Deliverable: two numbers that sum to seven and one sentence of recommendation.
Approach
- Recompute the whole series twice on identical rows, once against promised_delivery_date and once against original_promised_delivery_date. Both columns exist for all history, so the counterfactual is free and the split is arithmetic rather than an estimate.
- Read the gap between the two series in the weeks before the release. If it was near zero and opens abruptly in the release week, the jump is the measurement changing. If the original-promise series also rises, that component is real and should be credited.
- Measure the mechanism directly rather than inferring it: by week, the share of lines where promised_delivery_date > original_promised_delivery_date and the mean slip in days. Any metric that can be satisfied by moving its own target is not measuring service.
- Handle the date-versus-timestamp comparison explicitly. actual_delivery_at_utc is a UTC instant while the promise is a local date, so convert using dim_location.timezone and compare against local end of day. Doing the comparison in UTC moves an entire timezone's deliveries across the boundary and invents a second, smaller step.
- Recommend the original-promise definition for the published metric and keep the re-promise series as a separate operational measure, stating that the two answer different questions rather than that one is simply wrong.
Follow-up
- Re-promises are legitimate when a customer agrees to the new date. How would you let those count without reopening the loophole?
- What automated check would catch the next definition change before it reaches a chart?
- Supplier inbound on-time has the same structure. Where is its equivalent loophole, and what does the metric tree already say about it?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Interviewers here are not checking whether you can describe a project. They want the decision you made, why you made it under the information you had, and what changed afterwards that someone else could measure. A story that ends at 'I built a model' has no ending. Say what the model caused, or what you stopped doing because of it.
How do you balance conflicting optimization targets, such as maximizin…
How do you balance conflicting optimization targets, such as maximizing machine uptime while minimizing maintenance overhead?
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.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- How did you know the outcome was caused by your change?
- What would you do differently if you ran that project again?
Tell a sponsor the effect cannot be measured in six weeks
A VP wants a read in six weeks on a forward-stocking policy now live at eight of forty nodes. Replenishment lead time on the affected lanes is five weeks, the eight nodes were chosen by the ops team because they were the worst performers, and the cross-node standard deviation of weekly fill rate is about 3 points. The VP has already told his leadership team that a number is coming. Tell him what you can and cannot deliver in six weeks, and propose the design and timeline you would commit to instead.
Approach
- Do the power arithmetic in front of him rather than asserting the test is underpowered: comparing eight treated against thirty-two untreated node means with a between-node standard deviation of 3 points gives a standard error near 3 times the square root of one eighth plus one thirty-second, about 1.2 points, so the minimum detectable effect at 80 percent power and a two-sided 5 percent test is roughly 3.3 points. Any real effect smaller than that comes back as a null you cannot interpret.
- Name the burn-in problem separately: with a five-week lead time, six weeks covers barely one replenishment cycle, so whatever you measure is the transition rather than the new steady state, and the transition usually looks worse than the policy is.
- Name the selection problem third: nodes picked because they were worst will improve toward the network mean without any policy, so a simple before-and-after at those nodes is biased upward and will over-claim.
- Offer what is genuinely deliverable in six weeks: an implementation read, meaning whether the policy is actually in force at the eight nodes, whether inventory positions moved as designed, and whether any leading indicator such as short_reason_code mix is moving, framed explicitly as operational verification and not an effect estimate.
- Propose the real design with dates: extend to sixteen weeks covering roughly three cycles, use a difference in differences against matched comparison nodes chosen on pre-period fill rate and volume, cluster variance at the node, and pre-register the burn-in window that will be excluded.
- Help him with the commitment he already made: give him the exact wording for what he reports at week six, so the honest answer arrives as something he can say rather than as a refusal.
Follow-up
- He asks you to add the remaining thirty-two nodes to the rollout next month. What does that do to your design?
- If the effect really is 1 point, is the policy worth keeping, and how would you ever know?
Walk through an inventory analysis that turned out wrong
Six months ago you published inventory turns by node using the month-end on_hand_qty snapshot from fct_inventory_daily as the denominator, and two DCs cut cover on the strength of it. A finance review later showed month-end is systematically the lowest on-hand point of the month, because shipments cluster before close, so your turns were overstated and days of supply understated. Tell the story: what you built, how the error surfaced, what it cost, what you corrected, and the control that now prevents this class of mistake rather than this specific instance.
Approach
- Give the facts in order and own the decision, not just the code: you chose the month-end snapshot because it was one row per sku-location and fast, and you did not check whether the sampling point was representative.
- Quantify the error rather than describing it: recompute the same window against the average of every daily snapshot and state the gap in turns and in days of supply, which in a network with close-period push typically runs ten to twenty percent.
- Separate the consequence from the mistake honestly: say what the two DCs did, whether service actually degraded, and if it did not, say so instead of inflating the damage to sound accountable.
- Describe the correction and the notification: who was told, how quickly, and whether the restated number changed the recommendation.
- Close on the generalised control: a denominator convention written into the metric definition, plus a row-count assertion at each join grain, because the same shape of error appears when a daily snapshot is joined to shipment events on date equality and one shipment with several legs fans the snapshot out.
Follow-up
- How did you decide whom to tell first, and how did you phrase it?
- What made you trust the month-end snapshot in the first place, and what would have caught it in review?
- 01
How do you balance conflicting optimization targets, such as maximizing machine uptime while minimizing maintenance overhead?
- 02
A VP wants a read in six weeks on a forward-stocking policy now live at eight of forty nodes. Replenishment lead time on the affected lanes is five weeks, the eight nodes were chosen by the ops team because they were the worst performers, and the cross-node standard deviation of weekly fill rate is about 3 points. The VP has already told his leadership team that a number is coming. Tell him what you can and cannot deliver in six weeks, and propose the design and timeline you would commit to instead.
- 03
Six months ago you published inventory turns by node using the month-end on_hand_qty snapshot from fct_inventory_daily as the denominator, and two DCs cut cover on the strength of it. A finance review later showed month-end is systematically the lowest on-hand point of the month, because shipments cluster before close, so your turns were overstated and days of supply understated. Tell the story: what you built, how the error surfaced, what it cost, what you corrected, and the control that now prevents this class of mistake rather than this specific instance.
Is this an official ASML interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at ASML. Rounds and questions reflect what candidates have reported, not a process ASML has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the interview process for a Data Scientist at ASML?
The interview difficulty is generally rated as moderate and approachable, focusing heavily on foundational concepts, practical scenarios, and cultural alignment rather than grueling algorithmic trick questions.
PracHub interview research ↗How much preparation time should I dedicate?
Most candidates benefit from two to four weeks of focused preparation, ensuring they are comfortable with SQL window functions, A/B testing principles, and communicating past project experiences clearly.
PracHub interview research ↗What is the company culture like for data professionals?
The culture is characterized by a respectful, collaborative atmosphere where curiosity and motivation are prized, and teams work closely together to solve cutting-edge semiconductor challenges.
PracHub interview research ↗Are interviews conducted remotely or on-site?
The initial screening stages are typically conducted online via video or phone calls, followed by subsequent interview rounds that may be virtual or hosted at regional office locations.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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