A Data Scientist at Locus operates at the intersection of complex logistics optimization and high-stakes decision-making. You will be tasked with transforming raw operational data into actionable insights that drive efficiency in supply chain management, delivery routing, and fleet performance. This role is highly product-focused; you are not just building models in a silo, but actively shaping how the company’s software platform solves real-world logistics challenges for global clients.
The work is intellectually demanding, requiring a balance of rigorous statistical analysis and practical product intuition. You will collaborate closely with engineering and product management teams to define success metrics, diagnose performance drops in live systems, and design experiments that validate the impact of new features. Because Locus deals with large-scale, time-sensitive data, you must be comfortable managing complexity and communicating technical findings to non-technical stakeholders.
Introductory Round
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Technical Assessment
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
Deep-Dive Interviews
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.
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.
Averaging rates across SKU-locations instead of re-summing
Fill rate, turns, OEE and on-time rate are all ratios whose denominators differ by orders of magnitude between cells, so an unweighted mean gives a slow-moving C item at a small node the same vote as a high-volume A item at a national node. The blended figure then moves whenever the portfolio mix moves, and it can improve in every cell while the company-level ratio worsens, or the reverse, which is Simpson's paradox with a warehouse attached. Always sum numerator and denominator to the reporting level and divide once, and when a rate must be compared across nodes, standardise on a fixed SKU mix before reading anything into the difference.
Reading a dozen metrics with no multiplicity control
Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.
Generalising beyond the population the sample actually supports
State the frame the sample was drawn from and where it diverges from the population you want to talk about: time window, platform, geography, opt-in. If a group is excluded from the frame, either weight to known margins or narrow the claim rather than quietly extending it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you balance trade-offs between delivery speed and operational c…
How do you balance trade-offs between delivery speed and operational cost in a model?
Approach
- 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.
- 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?
- How would you choose the decision threshold, and who owns that choice?
Measure demand amplification hop by hop up the network
Using dim_location (location_id, parent_location_id, echelon, location_type) and fct_shipment_leg (leg_id, shipment_id, origin_location_id, destination_location_id, direction, tendered_at_utc, shipped_units), quantify how order variability grows upstream. fct_shipment_leg carries no sku_id and no link back to order lines, so shipped_units is the node's whole mixed-SKU flow and the measurement is node-level by construction. For each node compute weekly units flowing out (what it served downstream) and weekly units flowing in (what it ordered up), then the ratio of their variances on detrended, deseasonalised residuals. Walk parent_location_id from a store to echelon 0 and report the ratio at each hop, naming the hop where amplification is introduced. dim_location has no path column; build the chain yourself.
Approach
- Date orders by tendered_at_utc, not delivered_at_utc. Tender is the closest observable proxy for the moment the ordering decision was made; dating by delivery shifts the series by transit time and smears the variance you are trying to measure.
- Build both series on the same calendar weeks and in the same units. Variance scales with the aggregation window, so a ratio formed from daily inbound against weekly outbound measures the calendar, not the ordering rule. The units are mixed-SKU counts because the leg table carries no sku_id, so a node whose product mix drifts toward smaller or larger pack sizes moves both series for reasons unrelated to its ordering rule; print that caveat next to the number rather than implying a per-SKU result.
- Detrend and deseasonalise before taking variances, or compute the ratio on residuals from a simple weekly seasonal baseline. A growing node otherwise scores as amplifying, when all you have measured is its trend.
- Walk the parent chain iteratively: start from the store rows, join dim_location to itself on parent_location_id, and repeat until parent is null, capping the loop at the known echelon depth and asserting the path length matches the echelon difference so a data cycle raises rather than hangs.
- Read the output as a sequence, not a set of numbers. A pass-through node such as a cross-dock should sit near 1.0, and the hop where the ratio jumps is where a batching rule, a minimum order quantity or truckload rounding lives. That is the node to fix, not the node that is complaining.
Follow-up
- The ratio at one hop is 4.2 and the node insists it orders exactly to forecast. What ordering rules would produce that number anyway?
- What would it take to run this for a single SKU family rather than total units, given fct_shipment_leg carries neither a sku_id nor any link to order lines, and which hops would still be unmeasurable after you added that link?
- What would you expect this measurement to look like during a promotion, and does that change your conclusion?
As-of join snapshots to order lines without merge_asof
You have fct_order_line (order_line_id, sku_id, ship_from_location_id, actual_ship_at_utc, shipped_qty, shipment_id) and fct_inventory_daily (inventory_date, sku_id, location_id, available_qty). For every shipped line, attach available_qty from the latest snapshot strictly before the ship date for that exact sku and node, plus the shipment's delivery timestamp from fct_shipment_leg (leg_id, shipment_id, delivered_at_utc, leg_status). The output must have exactly one row per input order line. pandas.merge_asof is not available; implement the as-of match yourself.
Approach
- Collapse fct_shipment_leg to one row per shipment_id before joining anything. A shipment is one or more legs, so a direct line-to-leg join multiplies every line by its leg count and quietly inflates every sum computed afterwards.
- Define the shipment-level delivery as the maximum delivered_at_utc across its legs, and set it null if any leg is not delivered, since the customer has the order only when the last leg lands.
- Sort the snapshot frame by (sku_id, location_id, inventory_date) and the line frame by the same key plus ship date, then use np.searchsorted with side='left' on the sorted dates within each key group to find the insertion point; the matching snapshot is the row at position minus one.
- Guard the boundary explicitly: an insertion point of 0 means no snapshot exists before that ship date, and the result must be null rather than the first available future snapshot. Count those lines and report them.
- Assert the output row count equals the input line count immediately after each merge, before any aggregation. Row-count drift is the only symptom a silent fan-out produces, and it is invisible once the frame has been grouped.
Worked solution 30 min
- Aggregate legs by shipment_id to produce delivered_at_utc as the max, leg_count, and a delivered_all flag from leg_status.
- Left join that one-row-per-shipment frame onto fct_order_line by shipment_id and assert the row count is unchanged.
- Sort snapshots and lines by the composite key, then compute per-group insertion points with np.searchsorted and subtract one.
- Mask positions below zero to null, take available_qty at the remaining positions, and attach it as available_qty_prior.
- Validate on a single sku-location pair by printing its snapshot dates and one line's ship date and confirming the chosen date by eye.
Follow-up
- The snapshot is taken at the node's local end of day and actual_ship_at_utc is UTC. Where does that break the strictly-before rule and what do you need from dim_location to fix it?
- How would you extend this to allocate each leg's linehaul cost back to the order lines it carried?
- What changes if a line ships in two partial shipments?
Write a query using SQL window functions to calculate a rolling 7-day …
Write a query using SQL window functions to calculate a rolling 7-day average of delivery times.
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 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 handle missing or null values in a large-scale delivery …
How would you handle missing or null values in a large-scale delivery dataset?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- 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 would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
Explain how you would join multiple tables to track the lifecycle of a…
Explain how you would join multiple tables to track the lifecycle of a single package from warehouse to doorstep.
Approach
- Say which table is the grain you start from, and join outward from it.
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
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?
Landed cost per delivered unit by lane without fan-out
fct_shipment_leg holds one row per leg: shipment_id, leg_seq, origin_location_id, destination_location_id (NULL when the stop is a customer address rather than a network node), direction, mode, shipped_units, stop_count, leg_status, and linehaul_cost_cents, fuel_surcharge_cents, accessorial_cost_cents and expedite_premium_cents. fct_order_line carries shipment_id, NULL until shipped. Compute monthly landed cost per delivered unit by lane for direction in ('outbound','interfacility'), keying the lane on origin and destination with customer stops bucketed. Do not route through fct_order_line to get units. State the multi-stop allocation basis and what shipped_units means on a leg.
Approach
- Keep the entire computation at leg grain. Numerator is the sum of the four cost columns, denominator is SUM(shipped_units), both over legs with leg_status = 'delivered' and the two directions. The order table is not needed and joining it can only damage the number.
- Bucket destination_location_id IS NULL into an explicit customer_stop lane key with COALESCE, because a NULL never equals anything and will otherwise drop the row from a join or split it silently in a GROUP BY comparison.
- Say out loud whether shipped_units is units on board or units dropped at that stop. Under units on board, summing the legs of a multi-stop load counts a unit once per leg it rides and understates cost per unit; under units dropped, the leg sum is a true shipment total.
- Declare whether multi-stop cost is allocated by weight or by cube, since dense and bulky freight rank lanes in opposite orders under the two bases and the lane league table is not readable without it.
- Roll months up by re-summing cost and units and dividing once. Averaging leg-level cost per unit weights a one-pallet LTL leg the same as a full truckload.
Worked solution 25 min
- Filter fct_shipment_leg to leg_status = 'delivered', direction IN ('outbound','interfacility'), and the month range.
- Build lane_key from origin_location_id and COALESCE(destination_location_id::text, 'customer_stop').
- Aggregate per month and lane_key: total_cost_cents as the sum of the four columns, total_units as SUM(shipped_units), leg_count as COUNT(*).
- Divide once: cost_per_unit_cents = total_cost_cents / NULLIF(total_units, 0).
- Cross-check the grand total against an unaggregated SUM over the same filter, and record the allocation basis and shipped_units semantics in the output header.
Follow-up
- Cost per unit rose 8 percent while contracted linehaul rates were flat. Which columns do you cut first, and what does a rise in accessorials rather than linehaul point to?
- How do you separate a rate change from a mix change across mode and service_level?
- If cost genuinely had to be attributed to an order line, how would you allocate a leg's cost, and what does your rule do to a line that shipped one heavy item on an otherwise full truck?
If we introduce a new "priority delivery" service, what product metric…
If we introduce a new "priority delivery" service, what product metrics should we track?
Approach
- Fix the population and the time window before naming any metric.
- 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.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
A key performance metric dropped by 10% overnight; walk me through you…
A key performance metric dropped by 10% overnight; walk me through your diagnostic process.
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.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How would you define success metrics for a new delivery route optimiza…
How would you define success metrics for a new delivery route optimization feature?
Approach
- Fix the population and the time window before naming any metric.
- 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?
Explain the concept of statistical significance in the context of a lo…
Explain the concept of statistical significance in the context of a low-traffic experiment.
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- State the primary metric and the minimum effect worth shipping, then size the test.
- 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?
What are the most common experimentation pitfalls that lead to false p…
What are the most common experimentation pitfalls that lead to false positives?
Approach
- 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.
- Name the guardrails that would stop a launch even on a positive primary result.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Set a stopping rule and error budget for a daily-read test
A carrier allocation change is randomised at the shipment leg within lane. The primary metric is landed cost per delivered unit from fct_shipment_leg; the team also watches 14 guardrails, including on-time rate, accessorial share and refused legs. The dashboard refreshes daily and the team plans to stop as soon as the primary is significant. The run is planned at 20 business days. Give the type I error of that plan, specify a stopping rule that fixes it, and say how you treat the 14 guardrails.
Approach
- Quantify the peeking cost rather than describing it. Testing accumulating data at nominal two-sided 0.05 on each of 20 equally spaced looks carries a type I error of roughly 25%, against the 5% the team believes it is running; the inflation comes from taking the maximum of a correlated sequence of statistics instead of reading one.
- Match the correction to the behaviour they actually want. To stop early on a fixed schedule, use a group-sequential plan with an alpha-spending function: at five interim looks the O'Brien-Fleming boundaries are z = 4.56, 3.23, 2.63, 2.28, 2.04, which spends almost nothing early and leaves the final look near full alpha for about 3% more maximum sample; Pocock's constant z = 2.41 stops earlier but pays for it at the end. To look whenever they like with no schedule, use an anytime-valid confidence sequence, which is honest under continuous monitoring but needs more data for the same interval width.
- Do not adjust the primary for the guardrails. It is one hypothesis, and its alpha is spent entirely by the stopping rule.
- Treat the guardrails by what a miss costs. For the few that would actually block a launch, pre-register non-inferiority margins and test each at unadjusted 0.05, because a Bonferroni here buys a lower false-alarm rate by blinding the guardrail to the regression it exists to catch. Report the rest with Benjamini-Hochberg q-values at FDR 0.1 as triage, and state up front that 14 independent guardrails at 0.05 give a 1 - 0.95^14 = 51% chance of at least one spurious flag.
- Cluster the variance to match the dependence: legs on the same lane and day share weather, carrier capacity and fuel shocks, so leg-level iid standard errors are too small and make every boundary easier to cross than the design intends.
Worked solution 30 min
- State the uncorrected error: about 25% at 20 looks, against a believed 5%.
- Fix five interim analyses at equal information fractions with an O'Brien-Fleming spending function and boundaries z = 4.56, 3.23, 2.63, 2.28, 2.04.
- Re-size for the plan: maximum sample rises about 3% over a fixed-horizon test at the same power, while expected sample falls when a real effect is present.
- Write the guardrail policy: three or four blocking guardrails with pre-registered non-inferiority margins at unadjusted 0.05, the remainder Benjamini-Hochberg at FDR 0.1 for triage only.
- Fit the primary with standard errors clustered at lane by day and re-check the boundary crossings under the clustered SEs.
Follow-up
- Legs of one shipment share a truck and an allocated cost. Is the leg a legitimate randomisation unit here, and what breaks if it is not?
- The primary crosses the boundary on day 4. What do you need to see before you recommend stopping, and what would make you keep running anyway?
Forecast accuracy collapsed at nodes missing snapshot rows
WMAPE at lag 7 for one region jumped from 31 percent to 58 percent over three weeks with no model change and no retraining. fct_inventory_daily is specified to carry one row per active sku-location-date even when on_hand_qty is zero, and a release changed how the end-of-day cut-off is derived from dim_location.timezone. Using fct_inventory_daily (inventory_date, sku_id, location_id, demand_qty, forecast_qty_lag7, stockout_flag) and dim_location, decide whether demand changed or the table did, and quantify the contaminated share before anyone retrains.
Approach
- Run a completeness check before touching any accuracy number: expected rows per location per day from the active sku-location grid, against actual rows present. If the table is incomplete the accuracy question is not well posed, and this check costs one query.
- Separate the two failure modes a cut-off change produces. Rows missing entirely shrink the denominator, and the sku-days that vanish are not a random sample. Rows present but with demand pushed onto the adjacent date create a one-day shift that inflates absolute error on both days while leaving the weekly total intact.
- Test the shift hypothesis directly by comparing SUM(demand_qty) per ISO week per node before and after the release. If weekly totals match and only the daily split moved, the model is fine and the grain is broken.
- Recompute WMAPE on clean node-days only and report both figures with the excluded row count and the share of demand those rows represent, since the metric definition already requires the excluded count to travel with the number.
- Check whether the affected nodes share a timezone that crosses a daylight-saving boundary or sits far from the offset the pipeline assumed. That grouping is the discriminating evidence between a code defect and genuine demand movement.
Follow-up
- Weekly totals match and only the daily split moved. Does the ordering decision care? At what replenishment frequency does it stop mattering?
- How would you backfill the missing days, and which of them would you refuse to backfill?
- What assertion would you add to the snapshot job so this fails loudly instead of surfacing three weeks later as model drift?
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.
Nearly every data role forces a trade between the analysis you want and the one that fits the decision window. Prepare a case where you deliberately shipped something less rigorous, named the weakness to the person relying on it, and said what would change your answer. The naming is the part interviewers listen for.
Tell me about a time you disagreed with a product manager regarding a …
Tell me about a time you disagreed with a product manager regarding a feature's performance metrics.
Approach
- Close with what you would do differently, concretely.
- 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?
Explain forecast uncertainty to a non-technical general manager
A general manager who does not use statistics must approve a 2.1 million dollar finished-goods build. Your recommendation rests on a demand forecast with a wide error band on the Z-class items and on a 97 percent cycle service target. You get two minutes, no formulas, and the question you will get back is whether the forecast is right. Deliver the explanation as you would say it aloud, including how you describe the error band in physical terms and what the 97 percent number does and does not promise.
Approach
- Convert the error band into the units the approver already thinks in: weeks of cover, pallets, or dollars at risk, not a percentage or a confidence interval.
- Answer the right-or-wrong question directly rather than deflecting: the forecast will be wrong, the size of the wrongness is what you measured, and the build is sized against that size.
- State precisely what the 97 percent buys: it is a cycle service level, the probability of not running out during one replenishment cycle, so roughly three cycles in a hundred see a stockout. It is not the share of units shipped from stock. Unit fill rate also depends on the replenishment quantity, and when that quantity is large relative to the standard deviation of lead-time demand, which is the normal case outside lot-for-lot ordering, the fill rate sits above the cycle service number, often above 99 percent at a 97 percent cycle target.
- Give the two-sided consequence in money: what the build costs to carry at the applicable cost of capital plus obsolescence risk on shelf-life items, against the margin at risk from the stockouts it prevents.
- Close with the decision you want and the trigger that would reverse it, for example a lag-7 bias check after four weeks that reopens the number.
Follow-up
- The GM says just give me one number. What do you give, and what do you refuse to give?
- How would your answer change if the items were frozen with a 90-day shelf life?
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
Tell me about a time you disagreed with a product manager regarding a feature's performance metrics.
- 02
A general manager who does not use statistics must approve a 2.1 million dollar finished-goods build. Your recommendation rests on a demand forecast with a wide error band on the Z-class items and on a 97 percent cycle service target. You get two minutes, no formulas, and the question you will get back is whether the forecast is right. Deliver the explanation as you would say it aloud, including how you describe the error band in physical terms and what the 97 percent number does and does not promise.
- 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 Locus interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Locus. Rounds and questions reflect what candidates have reported, not a process Locus 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 technical rounds?
Most successful candidates dedicate at least 2–4 weeks of focused practice, particularly on SQL and statistical problem-solving. Consistency is more important than cramming, so aim for daily practice with common interview scenarios.
PracHub interview research ↗What is the most common reason candidates fail the technical interview?
Candidates often struggle when they jump straight to a complex solution without first clarifying the problem or discussing the business trade-offs. Always think out loud and ensure you understand the "why" behind the data before writing code.
PracHub interview research ↗Does Locus conduct remote interviews?
Yes, most interviews are conducted via video conferencing, but be prepared for a screen-sharing session where you will be asked to solve problems in real-time.
PracHub interview research ↗How does the company handle feedback?
While the process can be intensive, candidates should always ask for feedback at the end of each round. If you do not hear back within the expected timeframe, it is professional to send one polite follow-up to your recruiter.
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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