Whatnot Data Scientist
Interview Questions & Guide 2026

You are given pings: provider_id, market_id, event_at_utc, status in ('idle','en_route','engaged','offline'), one row per app heartbeat, nominally every 30 seconds but with gaps. Reconstruct the supply-session table. A session starts at the first non-offline ping and ends at an explicit offline ping, at a market change, or when the gap to the next ping exceeds 10 minutes. Produce session_id, provider_id, market_id, online_at, offline_at, online_seconds, engaged_seconds, en_route_seconds, idle_seconds and end_reason, with the three state components summing to online_seconds exactly in integer seconds.

You have orders (order_id, market_id, completed_at_utc, order_status, gross_booking_cents, provider_payout_cents, consumer_incentive_cents, provider_incentive_cents, tip_cents) and ledger (ledger_id, order_id, entry_type in charge, refund, chargeback, incentive, payout, adjustment, processing_fee; amount_cents signed positive into the platform; posted_at_utc; settlement_status). For completed orders, produce a market-month frame with net take, margin before support cost, and the order count. Attribute refunds, chargebacks and processing fees to the order's completion month, not the posting month, and drop ledger rows whose settlement_status is 'pending' or 'failed'.

You are given orders: order_id, consumer_id, completed_at_utc (tz-aware UTC), about two million rows, one row per completed order. For every order, compute how many completed orders the same consumer had in the 30 days before that order, counting the window as [t - 30 days, t) so the order itself and any exact-timestamp twin are excluded. You may not use groupby().rolling, merge_asof, or apply over groups. Return the input frame, in its original row order and index, with one added integer column prior_30d.

Optimize a slow-running query that joins multi-terabyte event logs for user streaming sessions and checkout funnels.

Extract and aggregate daily buyer conversion rates across multiple geographic regions, handling null values and edge cases gracefully.

dim_user has user_id, side, provider_status, provider_approved_at_utc. fct_request has request_id, provider_id and matched_at_utc, where provider_id is NULL on every request that never matched. fct_supply_session has session_id, provider_id, online_at_utc. List providers whose provider_status is 'approved' and whose provider_approved_at_utc falls in a given calendar month, who have never appeared as fct_request.provider_id and who have no row in fct_supply_session. Return user_id, provider_approved_at_utc and days since approval, oldest first.

How do you manage competing priorities when multiple product teams request custom analytics support simultaneously?

Whatnot just launched a feature allowing buyers to tip sellers during live auctions; how would you evaluate its success?

How do you balance conflicting marketplace metrics, such as buyer acquisition growth versus seller retention and churn?

How would you design a core metric framework to measure the health and engagement of a new live-shopping category on the platform?

How do you determine statistical significance when dealing with highly skewed transaction value distributions and low-frequency buyers?

Explain how you would approach causal inference and impact measurement when a randomized controlled trial is practically impossible to execute.

Regulation forces one market to cut its service fee on a known date. Randomisation is impossible and only that market is affected. You have 52 weeks of pre-period weekly series for every market: completed orders, active consumers, SLA fill rate, provider online hours and net take rate. Estimate the effect on completed orders per active consumer over the 12 weeks after the change. Specify the estimator, the donor pool and its exclusions, the pre-period fit criterion you would accept, and how you would produce a p-value with exactly one treated unit.

Daily requests in one market fell 4% against the previous day and recovered the day after. The dashboard groups fct_request by DATE(requested_at_local), and dim_market.timezone holds the market's IANA zone. Before anyone opens an incident, rule out calendar and clock causes. Deliverable: the ordered checks you run, the arithmetic for the largest mechanical cause you find, and the residual threshold at which you would still escalate.