As a Data Scientist at Navistar, you are at the intersection of heavy-duty vehicle manufacturing, supply chain optimization, and cutting-edge predictive analytics. Beyond building models, you solve complex, real-world problems that directly affect the efficiency of Navistar's logistics, the reliability of its fleet, and the future of transportation technology. Your work transforms raw telemetry, manufacturing, and supply chain data into actionable intelligence that drives strategic business decisions.
This role requires a blend of rigorous statistical modeling and a pragmatic, business-oriented mindset. Whether you are working on Supply Chain Analytics or broader enterprise-level data initiatives, you will collaborate with cross-functional teams to identify bottlenecks, forecast demand, and optimize operational workflows. You will be expected to own your projects from data ingestion and cleansing to model deployment, making this an ideal role for candidates who thrive on end-to-end ownership and high-impact problem solving.
HR Screen
reportedMost candidates lose this call inside the first two minutes, during the walkthrough of their own background. The account runs chronologically, sits at the level of tools and titles, and never arrives at a decision anyone could have disagreed with. Anchor on a problem instead of a timeline: what the team could not answer, what you did about it, what happened next. Ninety seconds is enough, and stopping on time leaves room for the half of the call that belongs to you. What you ask about how work gets prioritised signals your level more reliably than the walkthrough does.
What to demonstrate
- Whether your background summary has a shape (problem, decision, consequence) or is a chronological list of tools and employers
- Whether you can account for gaps, short stints and the reason you are looking, unprompted and without hedging
- The substance of the questions you ask back, which an experienced screener reads as a level signal
How to prepare
- Time your opening walkthrough against a clock. If it runs past two minutes, compress the earliest role into a single clause and spend the recovered time on the most recent one
- Write one honest sentence for every gap or short stint visible on your resume and offer it before being asked about it
- Prepare questions about how work arrives and gets prioritised: who writes the request, how often priorities change, and what happens to an analysis after it is delivered
Take-Home Assignment
reportedThe clock is part of the test. Three to six hours is not enough to do everything the dataset supports, so the submission mostly reveals how you spend a fixed budget against an open question. A reviewer sees which paths you took and, by absence, which you abandoned. Work that runs out of time inside the analysis ships a thin conclusion, while work that cuts scope early protects the last hour for writing. The most reliable way to lose here is to leave the scoping decision implicit, so it reads as something you missed rather than something you chose.
What to demonstrate
- Whether the scope you settled on is presented as a decision with a reason, rather than left for the reader to infer from what is missing
- Whether the depth of the work is consistent with the stated time budget, instead of several half-finished directions left open
- Whether the closing section reads as something written on purpose rather than assembled from whichever cells survived
How to prepare
- Run a timed rehearsal on a public dataset with a hard stop, holding the final sixty minutes for writing no matter where the analysis has got to
- Before opening the data, list the questions it could plausibly answer, pick one, and keep the discarded ones as a short note on what you did not attempt and why
- Commit a one-line finding after each analysis step so the writeup is assembled from recorded results rather than from memory at midnight
Technical Interviews
reportedThis round decides whether someone can hand you a schema and a question and trust the number that comes back. Correctness under a clock is the bar, not clever syntax. The habit that separates strong from weak answers is checking the grain: after every join, know how many rows you expect and whether the count moved. Most wrong answers in this format are not wrong logic, they are a fan-out from a key that turned out not to be unique, or a filter applied before an aggregate when it belonged after. Say what you expect before you run it.
What to demonstrate
- Whether your row counts survive each join, and whether you notice on your own when they do not
- Deliberate handling of rows that fail to match, including whether the question needs an inner join or a left join with the non-matches kept and counted
- Whether NULLs are treated on purpose, given that a NULL compares equal to nothing and that COUNT of a column skips it
- Reaching a defensible answer inside the window instead of a refined one after it
How to prepare
- Take a two-table schema, write a join that fans out on purpose, then fix it by collapsing the many-side to one row per key before joining. Repeat until the fix is reflex rather than recall.
- Write a funnel as one query and print the distinct user count at each stage, then confirm each stage is a subset of the one above it rather than assuming it
- Do a few timed runs in a plain text box with no autocomplete and no formatter, since assessment editors often have neither
Behavioral Interviews
reportedRounds of this kind usually include one question about work that did not go well, and it is the part that carries the most information. Anyone can narrate a shipped win. What the interviewer learns from a project that stalled is how you behave without a result to hide behind: whether you noticed the problem yourself, how long it took, and who you told. Answers that route the failure onto a data pipeline or a reorganisation close the topic without answering it, and the follow-up comes back to your own part.
What to demonstrate
- Whether you found the error yourself or someone else found it, and how long it sat before anyone knew
- What you changed afterwards, stated as a check you now run rather than a lesson you now believe
- Whether the mistake you choose has real cost attached, such as a quarter of misdirected roadmap or a metric that was reported upward, instead of one that flatters you
How to prepare
- Choose a failure you caught yourself and be ready to say what tipped you off. A story where someone else caught it is still usable, but you will be asked why you missed it.
- Write down the check you added afterwards and where it lives now, so the correction is a concrete artefact rather than a resolution.
- Rehearse saying the cost out loud. Candidates shrink the number by instinct once the interviewer is in the room.
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.
Scoring intermittent demand with MAPE
MAPE is undefined whenever the actual is zero, which is most days for a C-class item at a forward-stocking location, and it is asymmetric even where it is defined: under-forecasting is bounded at 100 percent error while over-forecasting is unbounded. Optimising it therefore drives systematic under-forecasting on exactly the items whose stockouts are most expensive, and the bias is invisible in the headline because the zero-actual rows were dropped before averaging. Use WMAPE (sum of absolute errors over sum of actuals), a scaled error such as RMSSE, or a pinball loss at the service quantile the policy targets, and always state the aggregation level and forecast lag, because the same series scores very differently at daily SKU-store level and weekly SKU-region level.
Explaining an aggregate move without decomposing the mix shift
Split the change in the aggregate into within-segment movement and movement in segment weights before you explain it. Every segment's rate can fall while the overall rate rises, purely because volume shifted toward segments that already had higher rates.
Reaching for a model before the target metric exists
Before naming an algorithm, write down the label, the prediction time, and the action that changes when the score crosses a threshold. If you cannot say what decision the output drives, any modelling choice is guesswork dressed up as method.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you rank your proficiency in Python or R, and what libraries…
How would you rank your proficiency in Python or R, and what libraries do you rely on most?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
- Set a baseline first, so any model has something honest to beat.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Describe your process for handling missing data or outliers in a large…
Describe your process for handling missing data or outliers in a large dataset.
Approach
- Set a baseline first, so any model has something honest to beat.
- Say how the offline result would be validated online before it is trusted.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Tell us about a product or model you developed in a previous role or i…
Tell us about a product or model you developed in a previous role or internship. What was the impact?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Set a baseline first, so any model has something honest to beat.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- 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?
How do you validate your models to ensure they perform well on unseen …
How do you validate your models to ensure they perform well on unseen data?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
- Pick an evaluation metric that matches the cost of each error type, not a default.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
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?
Weekly unit fill rate by ship-from node
fct_order_line holds one row per customer order line per ship-from node in its terminal state, with ordered_qty, shipped_qty, requested_ship_date, actual_ship_at_utc, line_status and ship_from_location_id; dim_location carries location_code. Produce weekly first-pass unit fill rate by node. Numerator is shipped_qty on lines shipped on or before requested_ship_date; denominator is ordered_qty on every line requested that week except line_status = 'cancelled_by_customer'. Return node, week, numerator, denominator and rate, plus one network total row. State in the output how substituted lines are counted.
Approach
- Anchor the query at order-line grain filtered on requested_ship_date, never at shipments: a line that was never filled has no shipment row, and starting from shipments deletes exactly the failures the metric exists to count.
- Build the numerator as a conditional SUM over the same row set, SUM(CASE WHEN actual_ship_at_utc IS NOT NULL AND its ship date <= requested_ship_date THEN shipped_qty ELSE 0 END), so short and backordered lines stay in the denominator instead of vanishing behind a WHERE clause.
- Exclude only line_status = 'cancelled_by_customer'. A line cancelled for lack of supply is a service failure and belongs in the denominator at full ordered_qty.
- Group by node and by DATE_TRUNC('week', requested_ship_date), then build the network row by re-summing numerator and denominator across nodes, not by averaging the node rates.
- Declare the substitution rule explicitly in one column or one comment: lines with substituted_sku_id NOT NULL count toward the numerator only if substitution is an accepted fill in the service definition.
Worked solution 20 min
- Filter fct_order_line to requested_ship_date inside the window and line_status <> 'cancelled_by_customer'; count the rows removed by that filter and keep the count.
- Compute num = SUM(CASE WHEN actual_ship_at_utc::date <= requested_ship_date THEN shipped_qty ELSE 0 END) and den = SUM(ordered_qty) in the same SELECT.
- GROUP BY ship_from_location_id, DATE_TRUNC('week', requested_ship_date) and join dim_location for location_code.
- Add the network row from a second aggregate over the same base set: SUM(num) / SUM(den).
- Report the excluded-line count and the substitution rule beside the rate so the number is auditable.
Follow-up
- actual_ship_at_utc is UTC but requested_ship_date is a local calendar date at the node. How does the comparison change for a node at UTC+9, and which direction does the error run?
- Fill rate rose two points while a node's short_reason_code mix moved from 'no_stock' toward 'credit_hold'. Is that a supply improvement?
- How would you hold SKU mix fixed so the network number is comparable month over month?
Stockout streaks of three days or more per cell
fct_inventory_daily carries one row per sku_id x location_id x inventory_date for every active pair, including days with nothing on hand, plus stockout_flag marking days where available_qty reached zero. Return every run of consecutive stockout days lasting three days or more: sku, location, start date, end date, run length, and demand_qty summed across the run. Return alongside it, per sku-location, the count of such runs in the window. Verify that the daily row series is contiguous for each pair before relying on date arithmetic, and say what you do about pairs that fail.
Approach
- Filter to stockout_flag = TRUE, then assign ROW_NUMBER() OVER (PARTITION BY sku_id, location_id ORDER BY inventory_date).
- Form the island key as inventory_date - rn * INTERVAL '1 day'. Two rows share a key only when their dates are exactly consecutive, so the key cannot weld separate runs together; the exposure runs the other way. A day with no snapshot row inside a true stockout breaks that run into fragments, and any fragment under three days is deleted by the threshold, so the run leaves the output rather than appearing with a wrong length.
- Prove contiguity before trusting the key: per sku-location compare COUNT(DISTINCT inventory_date) against (MAX(inventory_date) - MIN(inventory_date) + 1), and separately confirm no pair carries two rows on one date, since a duplicate date advances rn without advancing the calendar and shifts every key after it. Pairs that fail get a generated date spine: LEFT JOIN generate_series over the window so each day is labelled stockout, in stock, or no snapshot, then apply a stated rule for the no-snapshot days instead of letting the key make that call silently. The conservative rule is to break the run at the hole and mark both fragments gap_adjacent.
- Group by sku, location and island key for MIN and MAX date, COUNT(*) as length and SUM(demand_qty) as the demand exposed to the stockout, keeping runs of length >= 3.
- Aggregate once more per sku-location for the run count, and mark runs touching either window edge as censored, since their true length is unknown from this window alone.
Follow-up
- The longest runs cluster on X-class A items. What does that combination suggest to look at before touching the safety stock policy?
- How would you estimate lost demand across a run, given that demand_qty records the orders that were placed but not the orders nobody bothered to place?
- The snapshot is taken at each location's local end of day and the pairs span several timezones. What breaks, and what stays true?
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?
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.
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?
OEE fell six points on a constraint work centre
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?
For someone who has spent the last year in notebooks, dashboards or modelling work and has not written raw SQL under time pressure. The first four days rebuild query fluency against a fixture you control and can verify by hand; the last three attach that fluency to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Build a fixture you can check answers against
- Create a local Postgres or SQLite database with four tables (users, sessions, events, orders) holding roughly 200 rows you generated yourself, so you know the contents well enough to predict every result.
- Deliberately seed the cases that break queries: a user with no sessions, a session with no events, two orders sharing a timestamp, a NULL in one join key, and one duplicated user row.
- Before writing any SQL, hand-compute five answers on paper (how many users placed at least one order, median orders per ordering user, and three others) and save them as the ground truth for the week.
Deliverable: A one-command seed script plus a text file of five hand-computed answers to grade every later query against.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Joins, filters and NULL semantics
- Answer "which users have no orders" three ways (LEFT JOIN with IS NULL, NOT EXISTS, NOT IN) and confirm that the NOT IN version returns zero rows once the subquery contains a NULL, because the comparison is never TRUE.
- Reproduce the LEFT JOIN that silently collapses to an inner join by putting a right-table predicate in WHERE, then fix it by moving the predicate into the ON clause, and record both row counts.
- Create a fan-out bug on purpose by joining orders to order_items and summing the order total, then correct it with a pre-aggregated subquery and explain in one line which table changed the grain.
Deliverable: One annotated .sql file holding the three join traps, each with the wrong result and the corrected result side by side.
Practice prompt ↗Practice prompt ↗03Window functions and frames
- Write three window queries against the fixture: a running order total per user, the rank of each order within its user by value, and the day gap to that user's previous order, then check each against the day-one ground truth.
- Run ROW_NUMBER, RANK and DENSE_RANK over a column containing ties, print all three side by side, and write one sentence on when each is the correct choice.
- Switch one query from the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is what you get when ORDER BY is present and no frame is written) to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and explain why the output differs only when the ORDER BY column has duplicates.
Deliverable: Three verified window queries plus a short note explaining the RANGE versus ROWS difference in your own words.
Practice prompt ↗Practice prompt ↗04The four analytical query patterns
- Write a monthly retention grid: first order month per user, then months-since-first as the column, and verify that month zero equals the cohort size exactly.
- Sessionize the events table under a 30-minute inactivity rule using LAG plus a cumulative sum over a new-session flag.
- Build a four-step funnel that counts distinct users rather than events at each step, and state the rule you applied to a user who reaches step three without ever logging step two.
Deliverable: One file with the retention, sessionization and funnel patterns, each carrying a one-line note on the assumption it bakes in.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Write SQL the way you will have to write it live
- Set a 12-minute timer and solve three medium prompts in a plain editor with no execution and no autocomplete, then run them and tally syntax errors separately from logic errors.
- Narrate one solution aloud while writing it, stating the grain of each intermediate result (one row per user, one row per user-day) before you type its body.
- Rewrite your slowest solution as a CTE chain where every CTE name states its grain, and time yourself re-solving it from blank.
Deliverable: A recording of one narrated solution plus an error tally that separates syntax from logic.
Practice prompt ↗Practice prompt ↗06One day for everything that is not SQL
- Write the preconditions of the two-sample t-test from memory, then check them: independent observations, and a difference in means whose sampling distribution is approximately normal, which at large sample sizes follows from the central limit theorem rather than from normality of the raw values.
- Write the difference between an odds ratio from logistic regression and a relative risk, and state the condition under which the two are close (low outcome prevalence).
- Prepare a 90-second answer to "how would you know this model is any good" that names the metric, the baseline you would beat, and the cost of the errors you care about.
Deliverable: One page of notes covering test preconditions, the odds-ratio caveat and the model-quality answer.
Practice prompt ↗Practice prompt ↗07Full loop rehearsal
- Run a 45-minute mock with someone willing to interrupt: 20 minutes of SQL, 15 minutes defining a metric, 10 minutes on a past project.
- Re-solve from blank the two queries you were slowest on this week and compare the times against day five.
- Write a five-line answer to "walk me through a project" that puts a number in the first sentence and names the decision the work changed.
Deliverable: Mock feedback notes plus a timed project narrative you can deliver without reading it.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Most of the questions in this section reduce to one thing: can you be handed a vague request and come back with something useful? Prepare an example where the ask was underspecified, you chose an interpretation, and you said out loud which interpretation you chose. Describing how you narrowed the question matters more than the technique you eventually used.
How do you handle situations where your model results differ from the …
How do you handle situations where your model results differ from the expectations of business stakeholders?
Approach
- Close with what you would do differently, concretely.
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
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 us about a difficult technical challenge you faced and how you ov…
Tell us about a difficult technical challenge you faced and how you overcame it.
Approach
- Pick a story where you drove the decision, not one where you observed it.
- State the situation in two sentences and spend the rest on your reasoning.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- What would you do differently if you ran that project again?
- How did you know the outcome was caused by your change?
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 handle situations where your model results differ from the expectations of business stakeholders?
- 02
Tell us about a difficult technical challenge you faced and how you overcame it.
- 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 Navistar interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Navistar. Rounds and questions reflect what candidates have reported, not a process Navistar has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long should I spend preparing for the take-home assignment?
While the assignment can be completed in a few hours, set aside enough time to make sure your code is clean and your analysis is thorough. Quality of documentation is often as important as the model accuracy itself.
PracHub interview research ↗How difficult is the technical interview?
Expect an average level of difficulty. The focus is on your practical application of skills rather than obscure theoretical knowledge. If you can clearly explain your past work and the logic behind your technical choices, you will be well-positioned.
PracHub interview research ↗What is the company culture like?
Navistar values pragmatism, collaboration, and a drive for continuous improvement. Strong candidates are experts in their field who also enjoy working in a team-oriented environment where data is used to solve real-world industrial challenges.
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
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