The Data Scientist role at Flexport is positioned at the intersection of complex global logistics and high-scale data engineering. As a Data Scientist, you are tasked with transforming raw, messy shipment data into actionable insights that optimize the flow of goods across borders. You will work on critical challenges such as supply chain visibility, predictive modeling for freight transit, and the automation of manual logistics operations.
This role requires a blend of rigorous statistical analysis and a product-focused mindset. You will not only build models but also design the experiments and metrics that define success for Flexport’s digital platform. Because the logistics industry involves high degrees of real-world volatility, your work will directly impact how the company manages operational efficiency and delivers value to its customers. You will collaborate closely with product managers, engineers, and operations teams to solve problems that are both technically demanding and strategically vital to the global economy.
Initial Screening
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
Technical Assessments
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
Deeper-Dive Interviews
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.
2 candidate reports. Individual accounts describe a particular role and hiring cycle.
Flexport Software Engineer Interview Experience — Onsite Word-Guessing Algorithm Question, No Offer
This year, all the onsite questions were new ones -- job hunting is really tough this year. Hoping this helps other people who are also job hunting. Phone screen: an algorithm question. Given a bunch of meeting start and end times, output the total time spent, counting any overlaps only once. Follow-up: if there's overlap, you'd need multiple meeting rooms -- find the minimum number of meeting ro…
Read full experienceFlexport Software Engineer Interview Experience — Rejected Half an Hour After a Screen With Broken Example Answers
View report detailsPracHub editorial advice for the preparation topics above.
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.
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.
Building features from data that postdates the prediction time
Check every feature against the timestamp at which the model would actually score, and drop anything computed from a window that includes or follows the label event. For a forecasting use case, split train and test by time rather than at random, and split by entity when the same entity recurs.
Treating a non-significant result as proof of no effect
Say whether the confidence interval excludes the effect sizes you would have cared about. If it does not, the honest reading is that the test was underpowered, so report the minimum detectable effect the design could have found and what sample size would resolve it.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Audit a daily inventory snapshot for silent corruption
You receive fct_inventory_daily with inventory_date, sku_id, location_id, on_hand_qty, allocated_qty, blocked_qty, available_qty, in_transit_qty, demand_qty, shipped_qty, stockout_flag and standard_cost_cents. The table is supposed to carry one row per active sku-location per date, including days with no movement. Write a check suite that returns one row per named check with the number of sku-location-days affected, the share of the scope, and the on-hand value at standard cost sitting behind the failures. Do not repair anything; the deliverable is the evidence.
Approach
- Start with the arithmetic identity the table declares: available_qty must equal on_hand_qty minus allocated_qty minus blocked_qty. Report exact row counts rather than a boolean, because a handful of violations is a feed bug and a uniform offset is a definition change upstream.
- Check sign constraints separately from the identity. Negative on_hand_qty usually means receipts posted out of order, while negative available_qty is legitimate at some sites when allocation is allowed to over-commit, so flag it and ask rather than assuming corruption.
- Check calendar completeness per sku-location by comparing the observed row count against the number of dates between that pair's first and last appearance. Count the missing dates without reindexing the frame, because filling them with zeros converts a feed gap into a plausible run of zero-demand days.
- Check stockout_flag in one direction only: available_qty of 0 at the cut-off with stockout_flag false is a contradiction, while flag true with positive available_qty is not, since the flag records an intraday touch of zero that a late receipt can recover.
- Weight every failure by on_hand_qty times standard_cost_cents so the summary orders checks by money at risk rather than by row count, and state the scope denominator on each row.
Follow-up
- Someone proposes asserting shipped_qty is at most demand_qty on the same row. Why does that fire on thousands of healthy rows?
- Half the missing dates fall on Sundays at one set of nodes. What is the most likely explanation and does it change the severity?
- Which of these checks would you run as a blocking gate before a nightly planning job, and which as a monitored report?
Cluster bootstrap for landed cost per delivered unit
You have delivered outbound legs from fct_shipment_leg: leg_id, shipment_id, leg_seq, carrier_id, origin_location_id, destination_location_id, shipped_units, cube_m3, linehaul_cost_cents, fuel_surcharge_cents, accessorial_cost_cents, expedite_premium_cents. In this extract the linehaul for a multi-stop load is booked entirely on leg_seq 1, so allocate it across the load's legs by cube first. Then estimate the difference in landed cost per delivered unit between two carriers on lanes both serve, with a 95 percent interval. Write the resampling yourself; no bootstrap library.
Approach
- Allocate the linehaul before anything else, in proportion to each leg's cube over the load's total cube, and keep the other three cost columns where they already sit. State the choice: allocating by weight instead reorders lanes whenever freight is bulky rather than dense.
- Restrict to the set of lanes both carriers actually serve in the window. Comparing over all lanes measures which carrier was assigned the cheap lanes, not which carrier is cheaper.
- Resample shipments, not legs. Legs of one load share a single allocated linehaul and a single dispatch decision, so treating them as independent draws understates the variance of the estimate.
- Recompute the statistic as a ratio of sums on every resample, total allocated cost over total shipped units. Taking the mean of leg-level cost-per-unit values instead gives a different estimand in which a one-unit leg counts as much as a full truckload.
- Report both the raw difference and a lane-standardised difference where lane weights are fixed at the pooled volume share, and say which one you would put in front of a decision maker.
Follow-up
- The interval crosses zero. What would you need in volume or in window length to resolve a difference of 2 cents per unit?
- One carrier's accessorials are rising while its linehaul is flat. What is that signature telling you about execution versus rates?
- How does your interval change if one shipment accounts for 15 percent of the units?
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?
How would you identify the top three most delayed shipments per region…
How would you identify the top three most delayed shipments per region using a common table expression?
Approach
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Describe your approach to optimizing a slow-running query that joins m…
Describe your approach to optimizing a slow-running query that joins multiple large logistics tables.
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- 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.
Follow-up
- What breaks if events arrive late or out of order?
- How does the query change if the join becomes one-to-many?
As-of inventory position behind each short-shipped line
For every fct_order_line with line_status in ('shipped_short','backordered','cancelled_no_supply') and requested_ship_date in the last quarter, attach the inventory state that governed it: the fct_inventory_daily row for the same sku_id and ship_from_location_id with the greatest inventory_date strictly before requested_ship_date. Snapshots are taken at the location-local end of day. Return the line, ordered_qty, shipped_qty, and that snapshot's available_qty, blocked_qty, on_order_qty and in_transit_qty, plus the snapshot's age in days. Lines with no prior snapshot must appear with NULLs rather than disappearing from the result.
Approach
- Recognise the join is an inequality, not an equality. The snapshot is an end-of-day state, so the position that governed allocation is the last close strictly before the requested ship date, and the same-date row reflects the outcome rather than the cause.
- Implement it with LEFT JOIN LATERAL (SELECT ... WHERE sku_id and location_id match AND inventory_date < requested_ship_date ORDER BY inventory_date DESC LIMIT 1) ON TRUE, or equivalently ROW_NUMBER() OVER (PARTITION BY order_line_id ORDER BY inventory_date DESC) = 1 over a bounded candidate set.
- Bound the lateral with a floor such as inventory_date >= requested_ship_date - 14 so the scan does not walk the whole history for each line, and let that floor produce NULLs rather than silently dropping the line.
- Keep the join LEFT. Lines at a node with no prior snapshot are a data-quality finding worth counting, and an INNER join converts them into an invisible bias toward nodes with clean snapshot coverage.
- Check the row count before and after the join: the output must have exactly one row per order_line_id, and any excess means the as-of match is returning more than one candidate.
Worked solution 30 min
- Filter fct_order_line to the three statuses and the quarter, and record the row count as the target.
- Attach LEFT JOIN LATERAL over fct_inventory_daily with the strict inequality, the fourteen-day floor, ORDER BY inventory_date DESC and LIMIT 1.
- Project the four quantity columns plus snapshot_age_days = requested_ship_date - inventory_date.
- Compare the result row count with the target; investigate any difference before aggregating anything.
- Count and group the NULL-snapshot rows by ship_from_location_id to see whether the gap is a node or a SKU launch artefact.
Follow-up
- Split the output by short_reason_code. Which codes should show available_qty at or near zero, and what does it mean when 'no_stock' lines show healthy availability?
- blocked_qty is large on many of these lines. Is that a planning problem, and who owns it?
- How would you extend this to the position at the moment of allocation rather than the previous close, and what would you need that the daily snapshot does not give you?
How would you design a metric to track the quality of carrier performa…
How would you design a metric to track the quality of carrier performance?
Approach
- 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.
- Restate the decision this analysis has to support, and who acts on the answer.
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?
A key logistics metric has suddenly dropped by 10%; how would you inve…
A key logistics metric has suddenly dropped by 10%; how would you investigate the root cause?
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.
- 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?
- Which segment would you cut first, and what would that rule out?
If we introduce a new shipment notification system, how would you dete…
If we introduce a new shipment notification system, how would you determine if it improves user retention?
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
- 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 prioritize between a feature that increases speed and on…
How would you prioritize between a feature that increases speed and one that increases cost transparency?
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?
- Which segment would you cut first, and what would that rule out?
If you observe a Simpson’s Paradox effect in your A/B test results, ho…
If you observe a Simpson’s Paradox effect in your A/B test results, how do you diagnose the cause?
Approach
- Decide the analysis before seeing data, including how long it runs and when you look.
- 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
- How would you handle interference between treated and control units?
- What would you do if you could not randomise at all?
Explain the concept of statistical significance and why it matters in …
Explain the concept of statistical significance and why it matters in our experimentation framework.
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.
- Decide the analysis before seeing data, including how long it runs and when you look.
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?
Price a three-point service target against inventory turns
Commercial wants the cycle service level on C/Z items raised from 95 to 98 percent at every regional DC. Finance holds you to inventory turns. Using fct_inventory_daily (on_hand_qty, standard_cost_cents, demand_qty, safety_stock_target_qty, reorder_point_qty, stockout_flag), dim_sku (abc_class, xyz_class, shelf_life_days, lifecycle_status) and receipt history for lead times, state the primary metric, the guardrail, and the arithmetic that turns the request into units, cash and a payback claim. Say what the request does not buy.
Approach
- Fix the vocabulary before any number, because the two candidate definitions are not even ordered the same way. Cycle service level is the probability of no stockout within a replenishment cycle, Phi(z). Unit fill rate under continuous review with backordering and normal lead-time demand is 1 - G(z) * sigma_dl / Q, where sigma_dl is the standard deviation of demand over the lead time, Q the replenishment quantity and G the standard normal loss function. The two are equal only at Q = G(z) * sigma_dl / (1 - Phi(z)), which is 0.418 * sigma_dl at z = 1.645 and 0.367 * sigma_dl at z = 2.054; above that Q the fill rate is the larger number.
- Convert the z change: the 95th and 98th percentiles of the standard normal are 1.645 and 2.054, so safety stock scales by 1.249 at unchanged variability. That is a 24.9 percent rise in the safety component only; cycle stock is untouched, so total inventory rises by less.
- Size the variability correctly with the variable-lead-time form, sigma_dl = sqrt(L_bar * sigma_D^2 + D_bar^2 * sigma_L^2), where sigma_L is the standard deviation of the lead time itself. On the long inbound lanes that feed C items the second term usually dominates, and the deterministic-lead-time form understates the requirement.
- Cost it: incremental units times standard_cost_cents times an annual holding rate built from cost of capital, storage and an obsolescence term that is materially larger where shelf_life_days is non-null or lifecycle_status is 'phase_out'.
- Value it honestly, and size the benefit in the unit the request actually moves. Expected units short per cycle fall from G(1.645) * sigma_dl = 0.0209 * sigma_dl to G(2.054) * sigma_dl = 0.0073 * sigma_dl, a 64.9 percent cut in shortage units. Convert that to contribution only with the margin and repeat-purchase assumptions written on the page, rather than assuming three points of service convert wholly into sales.
- Give the conflict its own line rather than smoothing it: turns fall by construction because the denominator rises against unchanged COGS, so propose a differentiated policy by ABC/XYZ cell instead of claiming both numbers improve.
Worked solution 35 min
- Compute D_bar and sigma_D per sku-location at the review-period grain the policy actually uses, and L_bar and sigma_L from receipt history in the same time unit.
- Compute sigma_dl with the variable-lead-time form, then safety stock at z = 1.645 and z = 2.054; the difference is the incremental units.
- Value those units at standard_cost_cents and apply the annual holding rate, showing the obsolescence component separately for shelf-life and phase-out SKUs.
- Read the fill rate both policies actually deliver by evaluating 1 - G(z) * sigma_dl / Q per cell at the Q in force, and report the Q/sigma_dl distribution so the cells near the crossover are visible rather than averaged away.
- Recompute inventory turns: trailing-52-week COGS over the average of daily on-hand at cost, averaged across every daily snapshot rather than month-end positions.
- Write the payback line: incremental annual carrying cost against incremental contribution from avoided shortage, with the shortage-to-lost-sale conversion stated as an assumption rather than buried in a spreadsheet.
Follow-up
- The customer contract specifies fill rate, not cycle service level. How does the answer change, and in which direction?
- The same cash could shorten the inbound lane instead. How do you compare the two options on one page?
Landed cost per unit rose with every lane flat
Landed cost per delivered unit rose from 4.12 dollars to 4.49 dollars month over month, yet cost per unit is flat or lower in every individual lane and no contract rate changed. From fct_shipment_leg (linehaul_cost_cents, fuel_surcharge_cents, accessorial_cost_cents, expedite_premium_cents, shipped_units, mode, service_level, origin_location_id, destination_location_id, leg_status, direction), quantify how much of the 37-cent move is mix and how much is rate, then state what you would do about it. Multi-stop loads are allocated across legs by weight.
Approach
- Reproduce the headline by summing the four cost columns and shipped_units over legs with leg_status = 'delivered' and direction IN ('outbound','interfacility'), dividing once. Confirm both facts hold on the same row set; if the lane figures came from an unweighted average of lane rates, the paradox is an artefact of the averaging and the work stops here.
- Write cost per unit as a weighted mean of lane costs, C = SUM(w_i * c_i), where w_i is the lane's share of shipped_units. Compute within = SUM(w0 * (c1 - c0)), mix = SUM(c0 * (w1 - w0)) and the interaction term, and verify they sum to 37 cents.
- If mix dominates, find which weights moved. Cut w by mode, by service_level and by distance band: parcel and ltl cost multiples of ftl per unit for the same goods, and 'expedite' carries expedite_premium_cents, so a migration of units across those categories raises the blend with no rate moving anywhere.
- Separate a rate change from a surcharge change by decomposing the numerator as well. Fuel surcharge and accessorials move independently of contract linehaul, so report all four cost components per unit side by side for both months before accepting that rates held.
- State the action in the units the decomposition produced. A mix problem is solved by load consolidation, order cut-off changes or minimum order quantities. A carrier negotiation only touches the within-lane term, which by hypothesis is the small one.
Follow-up
- Units migrated from truckload to parcel. Is that a transport failure, an order-profile change, or an inventory-positioning failure, and how would you tell them apart?
- You allocated multi-stop cost by weight. Which lanes change rank if you allocate by cube instead, and for what kind of freight?
- How would you build a mix-adjusted cost index that a monthly review can read without redoing this decomposition every time?
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.
Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.
Tell me about a time you identified a flaw in a model or analysis that…
Tell me about a time you identified a flaw in a model or analysis that was already in production.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Quantify the outcome, including what you would not claim credit for.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
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?
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 identified a flaw in a model or analysis that was already in production.
- 02
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.
- 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 Flexport interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Flexport. Rounds and questions reflect what candidates have reported, not a process Flexport has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How much time should I spend preparing for the SQL assessment?
Dedicate significant time to practicing complex queries, specifically those involving window functions and multi-table joins. The assessments are timed, so practicing in a simulated environment is highly recommended.
PracHub interview research ↗Is the interview process mostly technical or behavioral?
It is a balanced mix. While the early stages are heavily technical, the later rounds place a strong emphasis on your ability to work with product managers and engineers to solve business problems.
PracHub interview research ↗What differentiates successful candidates at Flexport?
Successful candidates are those who demonstrate "product-sense"—the ability to look at a data problem and immediately ask how the solution will improve the user experience or business efficiency.
PracHub interview research ↗What is the typical timeline for the hiring process?
Timelines can vary based on the specific team and seniority, but you should expect the process to span several weeks from the initial recruiter screen to the final round.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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