DoorDash interview questions

Target: delivered later than the promised time plus a tolerance agreed with the business. Features may only use what's known at order creation: store prep history, current queue, nearby courier supply, distance, weather, time. Anything later, like actual pickup time, is leakage.

Split by time: train on earlier weeks, validate on later ones, with a gap so labels and aggregates don't overlap, and compute aggregates point-in-time. Metrics: PR-AUC for the rare class, calibration if the probability feeds ETAs, and recall at the alert volume ops can handle.

A cascade, cheap to expensive. Retrieval: stores that deliver to the user's location, from a geohash index, merged with two-tower candidates and popular-nearby lists. Pre-ranking trims them with a light model. Ranking runs a richer multi-task model over user, store and context features.

Cache store lists and slow-changing features per geohash cell; ETA and store busyness come from a low-latency feature store. Give each stage a latency budget with timeouts, and fall back to the cached popular list.

The estimand is the conditional average treatment effect: the extra orders free delivery causes for a customer, divided by its cost.

Collect data with a randomised experiment, treating a random slice, so treatment is independent of features. Fit a T-learner, X-learner or uplift trees. You never see one customer's effect, so evaluate offline with Qini or uplift curves: rank holdout customers by predicted uplift and compare treated against control order rates in the top 20%, per dollar spent. Confirm with a live test of the targeted policy against random targeting.