As a Data Scientist at Uber Freight, you are stepping into a role that fundamentally transforms the logistics and supply chain industry. Uber Freight connects shippers with carriers in a massive, dynamic B2B marketplace, replacing fragmented, manual processes with seamless, algorithmic matching and pricing. In this role, you will tackle some of the most complex supply and demand challenges in the world, directly influencing how billions of dollars of freight move across the country.
Your impact will be immediate and highly visible. Depending on your specific team—such as the Carrier Pricing organization—you will design and deploy models that determine real-time spot rates, optimize contract pricing strategies, and balance marketplace liquidity. You will work closely with product managers, engineers, and operations teams to build data-driven products that empower truck drivers to keep their wheels turning while helping shippers move goods efficiently.
This position, particularly at the Data Scientist III level, requires a blend of deep technical rigor and sharp business acumen. You will not just be pulling data or building isolated models; you will be architecting scalable solutions that drive core business metrics. The scale of data, the complexity of freight economics (such as deadhead miles, market seasonality, and carrier preferences), and the strategic influence you hold make this one of the most exciting data science roles in the tech industry today.
Recruiter 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
Technical Phone Screen
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
Virtual Onsite Loop
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
PracHub editorial advice for the preparation topics above.
Sizing safety stock as z times sigma_D times the square root of lead time
That form assumes lead time is deterministic. When lead time itself varies, the standard deviation of demand over lead time is sqrt(L_bar * sigma_D^2 + D_bar^2 * sigma_L^2), and the second term dominates whenever supply is unreliable, so the familiar formula can understate the requirement severalfold for a long, variable inbound lane. Two further preconditions are routinely forgotten: demand is assumed independent across periods, which promotions and order batching break, and z maps to cycle service level (the probability of no stockout in a replenishment cycle), not to fill rate, which additionally depends on order quantity through the unit normal loss function. Quoting a z-derived number as a fill rate overstates achieved service, and the gap widens as order quantity shrinks.
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.
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.
Defining the cohort on a post-treatment condition
Ask how rows entered the table. Filtering on something that treatment itself influences, such as users who finished onboarding or accounts still active at ninety days, breaks comparability between arms; define the population at an entry point that precedes exposure and keep everyone in it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you handle a situation where a new pricing algorithm increas…
How would you handle a situation where a new pricing algorithm increases our margin but decreases the total volume of loads booked?
Approach
- Check what information would not exist at prediction time, and exclude it.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- 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.
Worked solution 45 min
- Assign an ISO week from tendered_at_utc and build two weekly series per node from fct_shipment_leg alone: outbound units summed where origin_location_id is the node, inbound units summed where destination_location_id is the node. No SKU filter is applied because the table has no sku_id, so both series are total unit flow.
- Regress each series on a linear trend plus week-of-year dummies, or subtract a centred moving average, and keep the residuals.
- Compute the amplification ratio per node as var(inbound residuals) / var(outbound residuals) over the shared weeks, requiring a minimum of 26 weeks before reporting a ratio.
- Build the parent chain from a chosen store by iteratively joining dim_location on parent_location_id until parent_location_id is null, asserting the loop terminates within the echelon depth.
- Emit the chain in order with echelon, location_type, both variances, the ratio, the week count and the mixed-SKU caveat, and mark the hop with the largest increase.
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?
Reconstruct replenishment cycles from a daily inventory series
For one sku-location you have a daily series: inventory_date, on_hand_qty, in_transit_qty, on_order_qty, allocated_qty, shipped_qty, demand_qty, reorder_point_qty, stockout_flag. There is no purchase-order table. Infer the replenishment cycles: a cycle opens on the first day inventory position falls to or below reorder_point_qty, and closes on the first subsequent receipt. Receipts must be inferred from the series itself. Return one row per cycle with trigger date, receipt date, inferred lead time in days, demand during the cycle and stockout days, then report mean and standard deviation of the inferred lead time.
Approach
- Derive the receipt signal from the stock balance rather than looking for a column that does not exist: on_hand today equals on_hand yesterday plus receipts minus units shipped, so implied_receipt = on_hand_qty.diff() + shipped_qty, and a positive value means goods arrived.
- State what else can make that quantity positive: a cycle-count adjustment, a customer return, or a scrap reversal. Corroborate each candidate receipt with a matching fall in in_transit_qty on the same or adjacent day, and discard the ones with no corroboration.
- Debounce arrivals. A load split across two dock shifts produces positive implied receipts on consecutive days; collapse runs of receipt days separated by less than a chosen gap into a single arrival and make the gap an explicit parameter.
- Compute inventory position as on_hand + in_transit + on_order - allocated, not on hand alone, or every day after a trigger looks like another trigger and the cycle count explodes.
- Walk the series once with an explicit state variable (awaiting-trigger, awaiting-receipt) rather than trying to vectorise the pairing. The state machine is what makes cycles non-overlapping, and it is what a reviewer can check line by line. Be clear that cycles defined this way do not tile the calendar: the stretch from a receipt until the position next falls to the reorder point sits inside no cycle.
Follow-up
- Two orders are open at once because the trigger fired again before the first arrived. How does your state machine change?
- The policy quotes lead time in working days. What do you need from dim_location to convert, and where does the timezone matter?
- You now have mean and standard deviation of lead time. Write the safety stock this implies and say which assumption you are least comfortable with.
Write a SQL query to calculate the week-over-week growth rate of activ…
Write a SQL query to calculate the week-over-week growth rate of active carriers by region.
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?
Given a table of load postings and a table of carrier bids, write a qu…
Given a table of load postings and a table of carrier bids, write a query to find the average time between a load being posted and the first bid being received.
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
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.
Worked solution 30 min
- CTE completeness: per sku_id, location_id compute COUNT(DISTINCT inventory_date) and the date span, and flag pairs where they disagree or where COUNT(*) exceeds COUNT(DISTINCT inventory_date).
- CTE flagged: SELECT the stockout rows with ROW_NUMBER partitioned by sku_id, location_id ordered by inventory_date.
- CTE islands: add grp = inventory_date - rn * INTERVAL '1 day'.
- GROUP BY sku_id, location_id, grp producing MIN, MAX, COUNT() and SUM(demand_qty); filter COUNT() >= 3.
- Second aggregate over that result for runs per sku-location, plus a censored flag where start equals the window start or end equals the window end.
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?
How do you balance the trade-off between maximizing short-term revenue…
How do you balance the trade-off between maximizing short-term revenue and maintaining long-term carrier retention?
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.
- Fix the population and the time window before naming any metric.
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?
How would you evaluate the success of a new feature that shows carrier…
How would you evaluate the success of a new feature that shows carriers the historical average price of a route?
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.
- 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?
If the number of completed loads drops by 15% in one week, what data w…
If the number of completed loads drops by 15% in one week, what data would you look at to diagnose the issue?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
What metrics would you use to define a "healthy" marketplace in the co…
What metrics would you use to define a "healthy" marketplace in the context of freight logistics?
Approach
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- 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 design an A/B test for a new matching algorithm, knowing…
How would you design an A/B test for a new matching algorithm, knowing that carriers in the treatment group might take loads away from carriers in the control group?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- State the primary metric and the minimum effect worth shipping, then size the test.
- Name the randomisation unit first; it decides the variance and what the test can detect.
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?
If an experiment shows a statistically significant result on day 3 of …
If an experiment shows a statistically significant result on day 3 of a 14-day test, should we stop the experiment early? Why or why not?
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
- What would you do if you could not randomise at all?
- What would you conclude if the result is positive but the test is underpowered?
Diagnose a sample ratio mismatch in an order-line test
A promise-date algorithm was randomised 50/50 with the hash taken on order_id. Your analysis table has 180,000 fct_order_line rows in the window: 91,540 control and 88,460 treatment. Lines are excluded when line_status = 'cancelled_by_customer' or when the join to fct_shipment_leg on shipment_id returns no delivered leg. The readout shows treatment up 0.9 points on on-time delivery. Test the split, state what the result implies about the readout, and list the three most likely causes given how this table was built.
Approach
- Run the chi-square goodness-of-fit test against the designed 50/50 split: expected 90,000 per arm, deviation 1,540, so chi-square = 2 * 1540^2 / 90000 = 52.7 on 1 degree of freedom, p around 4e-13. That is not sampling noise, and the readout is not interpretable until it is explained.
- Check the grain mismatch first, because it is the cheapest explanation: assignment is on order_id but rows are lines. A perfectly balanced order split still yields unequal line counts whenever lines per order differ by arm, and a promise-date change that splits or consolidates orders does exactly that. Recount distinct order_id per arm before touching lines.
- Walk the filter chain and recount the ratio at every step: raw assignment log, then all lines, then after the cancelled_by_customer filter, then after the delivered-leg join. The step where the ratio breaks names the cause without any further argument.
- Recognise that both exclusions are post-treatment. Requiring a delivered leg conditions on an outcome the treatment moves, which opens a collider path: whichever arm ships more of the difficult lines retains more slow lines and is penalised for succeeding.
- Do not report the 0.9-point lift. Rebuild the metric with all assigned orders in the denominator and never-shipped orders scored as failures, then re-run and compare.
Worked solution 20 min
- Compute the test: expected 90,000 per arm, deviation 1,540, chi-square = 2 * (1540^2 / 90000) = 52.7, p about 4e-13.
- Recount distinct order_id per arm on the raw assignment log, before any filter or join touches the data.
- Recount after each filter in the order the pipeline applies them, recording the arm ratio at each stage.
- If the raw order split is balanced, rebuild the readout at order grain with every assigned order in the denominator and unshipped orders counted as not on time.
Follow-up
- The split is clean at order_id but broken at line level. Is the readout salvageable, and at which grain would you report it?
- How would you monitor for this automatically on a test that runs for six weeks, and at what threshold would you halt?
Plant schedule instability generated two echelons downstream
A plant reports that the replenishment orders it receives swing far more than end-customer demand, and asks for a larger finished-goods buffer. You have fct_order_line for customer demand at store and forward-stock nodes, fct_inventory_daily (on_order_qty, demand_qty per sku-location-date), dim_location (echelon, parent_location_id) and dim_sku (units_per_case, cases_per_pallet). Measure amplification at each echelon, identify the node whose ordering rule generates it, and price the buffer request against fixing that rule. Deliverable: a per-echelon amplification table and a recommendation.
Approach
- Build two series per node at a fixed weekly bucket per SKU: the quantity the node ordered from its parent_location_id, and the demand it received from its children or from customers. Both series must cover the same SKU, the same weeks and the same node set, or the ratio between them means nothing.
- Compute amplification per node as Var(orders placed) / Var(demand received), and report the squared coefficient-of-variation form alongside it. Mean volumes differ by an order of magnitude between echelons, so a raw variance ratio is not comparable across nodes of different size while the CV-squared ratio is scale-free.
- Read the table echelon by echelon and find where the ratio steps up. The generating node is where it first exceeds one by a wide margin, not the node that feels the pain: amplification observed at the plant is the product of every ratio below it along that chain.
- Name the mechanism at that node rather than asserting bullwhip generically. Minimum order quantity and lot-size multiple, the review period, batching up to a full truckload, and forecast-driven ordering that reacts to its own safety stock recalculation each leave a distinct signature in the order-size histogram.
- Price both options rather than choosing one. The buffer request converts to units, then to cost at standard_cost_cents and to carrying cost at the applicable cost of capital. The rule change converts to more frequent, smaller replenishment and therefore more freight per unit. Present the trade with both numbers attached.
Follow-up
- Orders cluster on exact truckload multiples. Would you break the batching rule, and what does that cost in freight per unit?
- How would you test an ordering-rule change when there are twelve nodes, they draw on the same stock and the same outbound loads, and one replenishment cycle is three weeks?
- Part of the plant's variance is its own production batching. How do you separate inbound amplification from what the plant creates for itself?
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 ↗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 ↗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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Sometimes the honest read is that the initiative did not work, and the person who commissioned the analysis was hoping otherwise. Interviewers want to know whether you softened it. Prepare the case where you delivered an unwelcome result, how you presented the uncertainty without hiding behind it, and what the team did next.
How do you handle novelty effects when analyzing the results of a prod…
How do you handle novelty effects when analyzing the results of a product launch?
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Quantify the outcome, including what you would not claim credit for.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
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?
Prioritise transport, plant and commercial in one week
Three requests land in the same week. Transport wants landed cost per delivered unit split by lane and mode from fct_shipment_leg for a carrier bid that closes in nine days. A plant manager wants OEE on two work centres decomposed into availability, performance and quality from fct_production_run for a capital request due next month. Commercial wants a fill-rate root cause from fct_order_line for an account review on Thursday. You are the only data scientist and have about four working days. Give your order, the rule that produced it, and what you say to the two who wait.
Approach
- Rank by the decision each request feeds and by whether your input can still change it, not by seniority or by who asked loudest: a bid closing in nine days is a live decision with a large irreversible spend attached, a capital request due next month has slack, and an account review has a fixed date but a smaller reversible decision.
- Check reversibility and blast radius: carrier rates lock for a contract period across every lane, so an error or an absence there is expensive for a year, while the fill-rate story can be revised next week.
- Look for the cheap partial that unblocks someone else: the fill-rate cut by node, by short_reason_code and by week is a few hours of work and covers most of what commercial needs on Thursday, so it does not have to wait behind the bid.
- Sequence with explicit time boxes: the commercial cut first because it is short and date-locked, the lane and mode cost analysis next with the allocation basis for multi-stop loads stated up front, and the OEE decomposition scheduled into the following week with a date the plant manager can hold you to.
- Tell the plant manager directly and early, with a date rather than a maybe, and say what you need from him in the meantime so the delay produces something.
- Escalate the trade-off rather than absorbing it silently: your manager should know a capital request slipped a week, because that is a business choice and not yours alone to make.
Follow-up
- The plant manager escalates and his director asks you to reorder. What do you do?
- Which of the three would you push back on entirely, and what would you offer instead?
- 01
How do you handle novelty effects when analyzing the results of a product launch?
- 02
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.
- 03
Three requests land in the same week. Transport wants landed cost per delivered unit split by lane and mode from fct_shipment_leg for a carrier bid that closes in nine days. A plant manager wants OEE on two work centres decomposed into availability, performance and quality from fct_production_run for a capital request due next month. Commercial wants a fill-rate root cause from fct_order_line for an account review on Thursday. You are the only data scientist and have about four working days. Give your order, the rule that produced it, and what you say to the two who wait.
Is this an official Uber Freight interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Uber Freight. Rounds and questions reflect what candidates have reported, not a process Uber Freight has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult is the technical screen, and how much should I prepare for SQL?
The technical screen is rigorous but fair. You should be highly comfortable writing complex SQL queries (including window functions and CTEs) without relying on Google. Aim to spend at least 30% of your preparation time refining your speed and accuracy in SQL and Pandas.
PracHub interview research ↗Do I need prior experience in logistics or supply chain to be hired?
While prior logistics experience is a strong advantage—especially for understanding concepts like deadhead miles or lane imbalances—it is not strictly required. However, you are expected to quickly grasp the dynamics of a two-sided marketplace and apply your data science skills to that context during the interview.
PracHub interview research ↗What is the typical timeline from the initial screen to an offer?
The process usually takes between 3 to 5 weeks. After the recruiter screen, the technical screen is typically scheduled within a week. If you pass, the onsite rounds are usually scheduled 1 to 2 weeks later, with a final decision coming a few days after the onsite.
PracHub interview research ↗What is the working style like for a Data Scientist at Uber Freight in Chicago?
Uber Freight operates with a highly collaborative, fast-paced culture. The Chicago office is a major hub, meaning you will have significant in-person interaction with engineering, product, and operations teams. Expect a hybrid work environment that values both independent deep work and cross-functional brainstorming.
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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