The role of a Data Scientist at United States Steel is pivotal in driving data-driven decision-making across the organization. As a Data Scientist, you will harness advanced analytical techniques and machine learning algorithms to extract meaningful insights from large datasets. The impact of your work will be felt across various products and operations, enhancing efficiency, improving product quality, and influencing strategic initiatives. By leveraging data, you will contribute to optimizing processes in manufacturing, supply chain management, and customer engagement, ultimately strengthening United States Steel’s position in the competitive steel industry.
This position is not only critical but also offers a unique opportunity to work with complex datasets and innovative technologies. You will engage with cross-functional teams, tackling challenges that range from predictive maintenance in manufacturing to market analysis and demand forecasting. Your contributions will directly influence key business outcomes, making this a highly rewarding role for those passionate about data and its transformative potential in a leading industrial company.
Initial Screening
reportedData Scientist covers at least four different jobs: experimentation, product analytics, causal work on observational data, and applied modelling that ships into a system. A screening call is the cheapest place to find out which of them is being hired for, and doing that diagnosis openly reads as senior rather than fussy. Ask what the last few pieces of work on the team actually were, and roughly how a week splits between querying, modelling and stakeholder time. Then say which parts of that you have done and which you have not. Claiming the whole range is the fastest way to be caught one round later.
What to demonstrate
- Whether you can distinguish the flavours of the role and locate your own experience inside one of them honestly
- Whether you name what you have not done instead of stretching to cover every line of the posting
- Whether your hard constraints (notice period, location, work authorisation, level) surface now rather than at offer stage
How to prepare
- Map the last two years of your time into rough percentages across query writing, experiment design, modelling and stakeholder work, so a question about scope has a real answer
- Mark every responsibility in the posting as done, adjacent or new, and prepare one sentence for each adjacent item naming the closest thing you have actually built
- Decide which logistics are non-negotiable before the call so you can state them in one sentence rather than negotiating live
Technical Assessment
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
Behavioral Interview
reportedMost of the weight in this round sits on the disagreement questions. Data work routinely produces an answer someone senior did not want, and the interviewer is trying to learn what you do in that hour. Both failure modes are common: folding as soon as a director pushes back, and treating the pushback as ignorance to be corrected with a better chart. A strong answer usually contains a specific thing the other person knew that you did not, and describes how you found out whether it changed the conclusion.
What to demonstrate
- Whether you can state the other side's argument accurately before you explain why you disagreed
- What you treated as evidence during the disagreement, such as a rerun under their assumption or a holdout check, rather than persuasion technique
- Whether you distinguish being overruled from being wrong, and can give an example of each
How to prepare
- Write out one disagreement where you turned out to be wrong, and say what in the data misled you. Candidates prepare the story where they were right, and the follow-up asks for the other one.
- For your main disagreement story, be ready to say what result would have made you drop your position. If no such result exists, you were not arguing from the data.
- Practise stating the opposing position out loud in one sentence the stakeholder would accept, then continue the story.
Team Interactions
reportedAn added round often puts you in front of someone outside the core hiring team: a partner engineer, a product owner, a domain expert, sometimes a more senior manager. The question they are really asking is not whether you can do the work but whether they would trust a number that came from you. That changes what a good answer looks like. Lead with what the decision cost and what it changed, keep the method available but not central, and be plain about the limits of your evidence. Overstating a result is the fastest way to lose this round.
What to demonstrate
- Whether you can explain a technical choice to someone who will never read your code, without either flattening it into nothing or hiding inside jargon
- Honesty about evidence strength: what the analysis establishes, what it only suggests, and what it cannot say at all
- How you take disagreement, specifically whether you update on a good objection, hold your position with reasons, or fold on contact
How to prepare
- Write the two-sentence version of your most technical project for a non-specialist, then check that neither sentence needs a method name to make sense.
- For one result you are proud of, write the strongest objection someone could raise and a response that concedes the part of it that is correct.
- Prepare one decision that turned out to be wrong: how you found out, what it cost, and what you changed afterwards. A senior cross-functional interviewer asks for this more often than a technical one does.
PracHub editorial advice for the preparation topics above.
Attributing demand variability to the node where it is observed
Order variance amplifies as it moves upstream: batching to a truckload, minimum order quantities, forecast-driven ordering and promotional pull all convert smooth end demand into lumpy replenishment orders, so a plant can see a coefficient of variation several times that of the underlying consumption. Diagnosing the plant's schedule instability as a plant problem then produces interventions that cannot work, because the generating process sits one or two echelons downstream. Measure the bullwhip ratio explicitly (variance of orders placed by a node over variance of demand it received) at each echelon, and fix the ordering rule at the node where the ratio jumps rather than the node where the pain is felt.
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.
Accepting a metric definition without asking about the denominator
Pin down the denominator, the eligibility filter and the time window before computing anything: conversion rate per session, per user, per eligible user and per new user are four different numbers with different behaviour. Restate the definition in one sentence and get agreement before you analyse.
Solving silently instead of narrating the reasoning
Say which branch you are taking and why you chose it over the alternative, for example checking the denominator first because it changes what the comparison means. A correct answer that arrives with no visible path scores below a rigorous one that needed a hint.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you implement a decision tree from scratch?
How would you implement a decision tree from scratch?
Approach
- 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.
- 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?
Describe a project where you implemented a machine learning model. Wha…
Describe a project where you implemented a machine learning model. What challenges did you face?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Check what information would not exist at prediction time, and exclude it.
- 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?
Weighted forecast error at the lag ordering actually consumes
You are given fct_inventory_daily as a DataFrame with inventory_date, sku_id, location_id, demand_qty and forecast_qty_lag7. Replenishment is decided weekly per SKU per node, so score the forecast at that grain over a trailing 8 weeks. WMAPE is the sum of absolute weekly errors over the sum of weekly demand; weighted mean percentage error is the same ratio with the error left signed. forecast_qty_lag7 is null before a series existed. Return one row per location_id carrying both statistics, the shared denominator, and the count of rows excluded. No forecasting library.
Approach
- Decide the null rule before writing any aggregation: a sku-location-week where any constituent day has a null forecast must be dropped whole. Signed error here is forecast minus demand, so summing 7 days of demand against the 5 days of forecast that survived leaves the numerator short by two days of forecast and drives the ratio negative, which reads as systematic under-forecasting the model never committed.
- Aggregate demand_qty and forecast_qty_lag7 to sku x location x week first, then difference. Differencing daily and summing the absolute values afterwards answers a different question, since intraday timing error cancels inside a week and the ordering decision never sees it.
- Build week buckets from inventory_date with a fixed anchor (a Monday-start ISO week or a rolling 7-day offset), and use the same buckets for both series so no partial week sits at either end of the 8-week window.
- Compute the two statistics by summing numerator and denominator to the location level and dividing once. Never average sku-level ratios: a C-class item with 3 units of weekly demand would otherwise carry the same weight as an A-class item with 3,000.
- Return the denominator and the excluded row count as columns, not as a printed aside, so a reader can tell a genuinely accurate node from one with almost no scored history.
Worked solution 20 min
- Filter to the trailing 8 complete weeks by inventory_date, then drop any sku-location-week containing a null forecast_qty_lag7 and record how many rows that removed.
- Group by sku_id, location_id, week and sum demand_qty and forecast_qty_lag7 into weekly totals.
- Add abs_err = (forecast - demand).abs() and signed_err = (forecast - demand) on the weekly frame.
- Group by location_id and sum abs_err, signed_err and demand_qty; divide the first two by the third to get wmape and wmpe.
- Attach the denominator and the excluded count, and sort by denominator descending so the nodes that matter read first.
Follow-up
- The same series is scored at lag 28 and WMAPE roughly doubles. Is that a model problem or an expected property of the horizon?
- A node shows WMAPE of 0.35 and bias of 0.01. What can you and can you not conclude about its inventory position?
- How would you report accuracy for a SKU whose weekly demand is zero in 40 of the 52 weeks?
Write a function to calculate the mean and median of a given dataset.
Write a function to calculate the mean and median of a given dataset.
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which table is the grain you start from, and join outward from 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?
- How does the query change if the join becomes one-to-many?
Perfect order rate against the original promise in local time
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.
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 tasked with reducing production costs by analyzing operational data…
If tasked with reducing production costs by analyzing operational data, what steps would you take?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Decompose the metric into the rates that drive it, and say which one you would check first.
- Name one primary metric, then the guardrail that stops it being gamed.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How do you prioritize tasks when managing multiple projects?
How do you prioritize tasks when managing multiple projects?
Approach
- 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.
- 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?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you approach a project to improve the accuracy of forecastin…
How would you approach a project to improve the accuracy of forecasting demand for steel products?
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.
- 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?
Given a dataset with customer purchase history, how would you identify…
Given a dataset with customer purchase history, how would you identify patterns in buying behavior?
Approach
- 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.
- State what result would change your recommendation, so the answer is falsifiable.
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?
What tools and programming languages are you proficient in for data an…
What tools and programming languages are you proficient in for data analysis?
Approach
- State your assumptions explicitly before working the problem.
- Work from the decision backwards to the evidence you would need.
- Say what you would check first and why it is the highest-information step.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Size a fill-rate test when only nodes can be randomised
A new allocation rule can only be switched on per node. You have 24 regional DCs, 12 per arm, and eight post-period weeks. The outcome is weekly unit fill rate per node from fct_order_line: SUM(shipped_qty) over SUM(ordered_qty) by requested_ship_date week. Between-node SD of that weekly rate is 4.0 percentage points; within-node week-to-week SD is 1.5 points. Planning wants to detect a 1.5-point lift at 80% power, two-sided 5%. Give the minimum detectable effect for this design and say whether running longer fixes it.
Approach
- Write the variance at the unit that was actually randomised. The node is the unit, so each node contributes one number and the week rows inside it are not independent observations: the variance of one node's eight-week mean is sigma_b^2 + sigma_w^2/m, and the variance of an arm mean is that quantity over n nodes.
- Plug in: 4.0^2 + 1.5^2/8 = 16.28 pp^2. MDE = (1.96 + 0.84) * sqrt(2 * 16.28 / 12) = 2.80 * 1.647 = 4.6 pp.
- Re-run with m taken to infinity to show what duration buys: 2.80 * sqrt(2 * 16 / 12) = 4.57 pp. Between-node variance is 98.6% of the node-mean variance, and extra weeks only shrink the other 1.4%, so doubling the run changes the MDE by under 1%.
- Invert the formula for the nodes needed to reach 1.5 pp: 2 * 16.28 * (2.80/1.5)^2 = about 114 nodes per arm. That does not exist, so state plainly that the design cannot answer the question as posed rather than reporting an underpowered null.
- Offer the levers that attack sigma_b rather than time: matched-pair randomisation on pre-period fill rate with ANCOVA on the pre-period node rate (at a pre-post correlation of 0.8 the residual between-node variance is 5.76 and the MDE falls to about 2.8 pp), or a within-node switchback by week if the rule can be toggled.
Worked solution 25 min
- Compute the node-level variance of an eight-week mean: 4.0^2 + 1.5^2/8 = 16.28 pp^2.
- MDE = 2.80 * sqrt(2 * 16.28 / 12) = 2.80 * 1.647 = 4.6 pp.
- Repeat at 16 weeks: 16 + 2.25/16 = 16.14, giving 4.59 pp, a change of about half a percent.
- Solve for nodes at the 1.5 pp target: n = 2 * 16.28 * (2.80/1.5)^2 = 114 per arm.
- Recompute under ANCOVA at a pre-post correlation of 0.8: residual between-node variance 16 * (1 - 0.64) = 5.76, total 6.04, MDE = 2.80 * sqrt(2 * 6.04 / 12) = 2.8 pp.
Follow-up
- The rule can be toggled weekly. What does a node-level switchback buy you here, and what does it cost you in assumptions?
- If you must ship a decision on 24 nodes, what MDE do you pre-register, and what exactly does a null result license anyone to conclude?
Unit fill rate fell four points in one region
Weekly unit fill rate for one region fell from 96.1 percent to 92.0 percent over four weeks and has not recovered. Network fill rate is flat. You have fct_order_line (ordered_qty, shipped_qty, requested_ship_date, ship_from_location_id, sku_id, line_status, short_reason_code), dim_location (location_type, region_code, echelon) and dim_sku (abc_class, xyz_class, lifecycle_status). Produce the first four query cuts, say what each one rules in or out, and name the single most likely cause together with the evidence that would confirm it. Thirty minutes, SQL plus a short written argument.
Approach
- Rebuild the metric by summing shipped_qty and ordered_qty to region level and dividing once, rather than averaging per-SKU-location rates. An unweighted mean of cell rates moves with portfolio mix on its own, so if the drop does not survive re-summing there is nothing physical to diagnose.
- Cut by ship_from_location_id first. If one node carries the whole drop the mix of demand across nodes is not the story and the problem is local; if every node fell a little, the cause sits upstream in supply or forecast rather than in any warehouse.
- Decompose the region change over abc_class by xyz_class cells into within-cell, mix and interaction terms: delta R = SUM(w0 * (r1 - r0)) + SUM(r0 * (w1 - w0)) + SUM((w1 - w0) * (r1 - r0)), where w is a cell's share of the denominator and r is its rate. A dominant mix term means demand moved toward hard-to-serve intermittent items and no cell actually got worse.
- Group the lost units (ordered_qty minus shipped_qty) by short_reason_code. 'no_stock' points at planning, 'pick_shortage' and 'damage_on_pick' mean the stock was physically present and execution lost it, 'allocation_rule' means the units existed and were promised to someone else, and 'credit_hold' is not a supply chain failure at all.
- Only then cross the weekly series against the promotion calendar, against SKUs with lifecycle_status = 'pre_launch' cutting in, and against accounts placing their first order in the window. A step change starting in the same week as a promotion or a new-item onboarding is a demand-side event, not a degradation.
Follow-up
- Fill rate is flat nationally but down in this region. What does that tell you about where the stock went, and how would you test it?
- Most of the failed units carry short_reason_code = 'no_stock', yet on_hand_qty at the node stayed above reorder_point_qty all month. How is that possible?
- Would you publish this rate weighted by units or by revenue, and which team gets a different answer from each?
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.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Design 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.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Power 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.
Practice prompt ↗Practice prompt ↗03Variance 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.
Practice prompt ↗Practice prompt ↗04Validity 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗05When 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.
Practice prompt ↗Practice prompt ↗06The 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.
Practice prompt ↗Practice prompt ↗07Present 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.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
Give an example of a situation where you had to collaborate with other…
Give an example of a situation where you had to collaborate with others to achieve a goal.
Approach
- Quantify the outcome, including what you would not claim credit for.
- State the situation in two sentences and spend the rest on your reasoning.
- 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?
Disagree with a planning manager about a safety stock cut
A planning product manager proposes cutting safety stock 30 percent on every C and Z class sku-location cell, citing a MAPE improvement from 41 to 28 percent over two quarters. Checking the query, you find MAPE is computed over fct_inventory_daily rows where the actual is greater than zero, and the actual used is shipped_qty rather than demand_qty; stockout_flag is true on 14 percent of the rows that were kept. You have fifteen minutes in his planning review. Make the disagreement, and propose what you would do instead of the flat cut.
Approach
- Lead with the decision at risk, not the metric error: a 30 percent cut on intermittent items is the cheapest way to convert a measurement artefact into stockouts eight weeks later, by which time nobody will connect the two.
- Name the two defects precisely. Dropping zero-actual rows is not a rounding choice, it removes most of the history for a C-class item at a forward-stocking location and it removes the rows where over-forecasting is penalised, so the surviving MAPE is biased toward whichever series stocked out. Using shipped_qty as the actual scores the forecast against a supply ceiling, so a series looks more accurate the more often it ran out.
- Offer the replacement metric in the same breath: WMAPE, sum of absolute errors over sum of actuals against demand_qty, computed at the sku-location-week grain the ordering decision uses and at the lag it uses, reported next to signed bias so a persistently short series cannot hide inside an accuracy number.
- Propose the smaller action that is defensible now: recompute on the corrected basis, rank cells by bias rather than accuracy, and cut cover only where the corrected series is unbiased or over-forecasting, in a staged rollout with a service guardrail.
- Give him the win he actually wants: if the corrected numbers still support cuts on a subset, say so in advance, so the disagreement is about evidence rather than about territory.
Follow-up
- He says demand_qty is itself incomplete because customers stop ordering what shows out of stock. Is he right, and what do you do about it?
- Which items would you leave alone regardless of what the corrected metric says?
Quantify your own impact on a safety stock change honestly
In March you re-sized safety stock at twelve regional DCs using the variable lead time formula. Unit fill rate at those nodes rose from 94.1 to 96.5 percent by June. In the same window a supplier that had been running 71 percent inbound on-time recovered to 93 percent, and Q2 volume is seasonally about 8 percent below Q1. Your manager asks for one sentence on impact for a performance review. Write the claim you are willing to defend under challenge, and explain how you bounded the portion attributable to your change.
Approach
- Refuse the naive claim first: the 2.4 point gross movement contains at least three generating processes, and attributing all of it is the sort of claim that collapses the moment someone plots the supplier's on-time series next to it.
- Build a comparison group from nodes that did not get the change, check that their pre-period fill rate moved in parallel with the treated nodes for at least two quarters before March, and estimate the difference in differences with variance clustered at the node, since twelve treated clusters is the effective sample size regardless of how many node-weeks there are.
- Strip the confounders you can measure directly rather than trusting the design alone: restrict to sku-location cells sourced from other suppliers to remove the recovery, and hold the sku mix fixed across periods so the seasonal volume drop does not enter through mix.
- Sanity-check the mechanism, not just the coefficient: if your change worked, the improvement should concentrate in cells whose reorder point rose and in short_reason_code = 'no_stock' lines, and should be absent where the policy did not move. If the gain is spread evenly, something else caused it.
- State the result as an interval with the confounders named, and state the cost side too: extra units held at the applicable cost of capital, so the claim is a net one rather than a service headline.
- Write the sentence so that it survives someone else re-running it, which usually means a smaller number than the gross movement and a named method.
Follow-up
- The comparison nodes are not parallel in the pre-period. What do you do next?
- Your bounded estimate is roughly half the gross movement. How do you present that to a manager who already told his boss the larger number?
- 01
Give an example of a situation where you had to collaborate with others to achieve a goal.
- 02
A planning product manager proposes cutting safety stock 30 percent on every C and Z class sku-location cell, citing a MAPE improvement from 41 to 28 percent over two quarters. Checking the query, you find MAPE is computed over fct_inventory_daily rows where the actual is greater than zero, and the actual used is shipped_qty rather than demand_qty; stockout_flag is true on 14 percent of the rows that were kept. You have fifteen minutes in his planning review. Make the disagreement, and propose what you would do instead of the flat cut.
- 03
In March you re-sized safety stock at twelve regional DCs using the variable lead time formula. Unit fill rate at those nodes rose from 94.1 to 96.5 percent by June. In the same window a supplier that had been running 71 percent inbound on-time recovered to 93 percent, and Q2 volume is seasonally about 8 percent below Q1. Your manager asks for one sentence on impact for a performance review. Write the claim you are willing to defend under challenge, and explain how you bounded the portion attributable to your change.
Is this an official United States Steel interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at United States Steel. Rounds and questions reflect what candidates have reported, not a process United States Steel 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?
The interview process can be challenging, requiring a solid understanding of data science concepts and practical applications. Candidates typically spend several weeks preparing, which can significantly enhance their performance.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate not only technical proficiency but also strong problem-solving abilities and effective communication skills. They can articulate their thought processes clearly and show a genuine passion for data science.
PracHub interview research ↗What is the culture like at United States Steel?
The culture at United States Steel emphasizes safety, innovation, and collaboration. Employees are encouraged to contribute ideas and work together to address challenges.
PracHub interview research ↗How long does the interview process usually take?
The timeline from initial screening to offer can vary, but candidates can expect it to take several weeks, depending on the scheduling of interviews and assessments.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Waymo Data Scientist Interview Questions & Guide 2026
SQL sets for data scientist prep ahead of Waymo, covering window functions, funnel and retention queries, and the joins that quietly drop the rows you needed.
ZF Group Data Scientist Interview Questions & Guide 2026
Causal-inference exercises for ZF Group data scientist prep: difference-in-differences, matching, instrumental variables, and knowing when observational data simply cannot answer the question asked.
XPO Data Scientist Interview Questions & Guide 2026
Data-quality drills for XPO data scientist prep: survivorship and selection bias, leakage through time-ordered features, and extreme values where provenance decides the call. A logging fault or unit slip gets corrected or dropped; a genuine
Xometry Data Scientist Interview Questions & Guide 2026
A prep plan for Xometry data scientist candidates built on spaced review: write a full answer before opening any solution, keep a running list of the assumptions you failed to state, and repeat every missed exercise three days later instead