最近在地里看到不少同学想要参加 DoorDash 的面试。我们团队刚帮一位统计学转码出身的学员完成了数据分析岗位的真实面试，并成功拿到 offer。以下是他的面试实录与核心思路分享。
Background:The trainee has three years of experience in data analysis in China and has a solid foundation in SQL, but he was nervous about the fast-paced interviews in Silicon Valley. He used ProgramHelp's remote assistance service one week before the interview, and was guided by ProgramHelp's real-time guidance, and finally played steadily.
Question 1: Experimental design
Topic:How would you design an experiment to test the impact of a new driver incentive program on delivery times?
Answer Ideas:
- Define the goal of the experiment: to reduce the average delivery time or to increase the order-taking rate?
- Bucket strategy: Randomly divide drivers into groups A/B, with group A receiving the new incentive and group B remaining unchanged.
- Core indicators: average delivery time, standard deviation; order-taking rate; cancellation rate, etc.
- Sample size calculation: based on historical data variability, expected lift and statistical efficacy.
- Significance testing: choosing appropriate confidence intervals and hypothesis testing methods.
Question 2: SQL Query Optimization
Topic:Given a table of delivery orders with columns order_id, driver_id, order_time, pickup_time, dropoff_time, and city_id, write a SQL query to find the average delivery time for each city. Then restrict to last month's orders and drivers with ≥100 deliveries.
Answer Ideas:
- Base Query:
SELECT city_id, AVG(TIMESTAMPDIFF(MINUTE, pickup_time, dropoff_time)) AS avg_delivery_minutes FROM delivery_orders GROUP BY city_id; - Add a time window with activity:
WITH recent AS ( SELECT * FROM delivery_orders WHERE order_time >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH) ), active_drivers AS ( SELECT driver_id FROM recent GROUP BY driver_id HAVING COUNT(*) >= 100 ) SELECT r.city_id, AVG(TIMESTAMPDIFF(MINUTE, r.pickup_time, r.dropoff_time)) AS avg_time FROM recent r JOIN active_drivers d ON r.driver_id = d.driver_id GROUP BY r.city_id; - Optional optimizations: creating time and driver indexes for large tables, or using window functions, etc.
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