DoorDash·Data Scientist·Take-home Assignment
Apr 2026
DoorDash data science take-home with a pretty dense SQL prompt built around their core domain: orders, dashers, merchants, promos. Six sub-questions covering window functions, anomaly detection, uplift estimation, and even a bootstrap CI question at the end. Felt more like a mini-project than a screening filter.
- Given a multi-table delivery schema, compute the daily on-time delivery rate per city over a rolling 7-day window. Define on-time as delivery within 45 minutes of order creation, treating null delivered_at as late and excluding canceled orders. Return city, date, on-time count, total eligible, and rate.
- For a given month, rank the top 3 merchants per city by GMV, where GMV accounts for subtotal, delivery fee, tip, and any applied promo discount. Break ties by cancellation count ascending, then by merchant ID. Exclude canceled orders from GMV but still track cancellations per merchant.
- Using an order events log table, identify orders that have a 'delivered' event but no 'picked_up' event that strictly precedes it in time. Return the order ID and a minimal event timeline showing the anomaly.
- Calculate a weekly dasher acceptance rate: for orders created in a given ISO week and assigned to a dasher, what share had an accepted_at timestamp within 3 minutes of created_at? Exclude orders canceled before acceptance. Output dasher_id, ISO week start, assigned order count, accepted-within-3m count, and rate.
- Find customers who have placed exactly two lifetime orders and both were canceled. Return the customer ID and both canceled_at timestamps in ascending order.
- For each merchant in a given month, estimate the uplift in average order value when a promo is applied versus not. Output merchant ID, promo and non-promo order counts, both AOVs, and the difference. Then explain how you would compute a 95% confidence interval for that difference using either a SQL-based bootstrap or a Python snippet.
“The null delivered_at edge case is the part I almost missed.” The rest of the author's notes on Data Scientist interview at DoorDash, Take-home Assignment round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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