Recently, I have seen a number of students in the field who want to participate in the DoorDash The following is the actual interview and the core idea of the interview. Our team just helped a student who came from a statistics transcoding background to complete a real interview for a data analytics position and successfully got an offer.Below is his interview transcript and core thoughts shared.
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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