meta | Data Scientist | Interview Experience
Interview Date: Not specified
Result: Not specified
Difficulty: Not specified
Interview Process
I had three interviews: DoorDash, Netflix, and one other that I still remember.
DoorDash:
The interview focused on product sense related to restaurants. Key topics included metrics to consider when launching restaurants, data segmentation, visualization techniques, and reasons for declines in restaurant revenue. A specific focus was placed on improving user loyalty, rather than engagement or volume.
The modeling portion involved a classic orders model, including two tables: orders and order_items. There were follow-up questions about adding a new metric to measure market share and handling cases where a restaurant has multiple menus. The SQL questions were straightforward, focusing on the orders and restaurant tables, such as calculating the ratio of pickup to delivery and identifying the top 3 types of restaurants.
Python questions were more challenging, particularly with input structured as follows:
ordersteps: [{driver, action, location_id}, {driver, action, order_id}]
When action=travel, there is no order_id; when action=pickup/dropoff, there is no location_id.
Netflix:
The product sense questions revolved around content and engagement. Key topics included measuring the success of newly launched programs, identifying drivers behind user binge-watching behavior, and troubleshooting drops in click-through rates for the recommender system.
The modeling questions favored the relationship between users and content, typically involving tables for user viewing behavior and program content. Follow-up questions included defining and counting “active users,” modeling shows with multiple seasons or episodes, and designing metrics to measure content diversity.
SQL questions were practical, such as calculating daily active users (DAU) on a given day, ranking viewing time by content type (movie, series, documentary), and identifying churn users (subscribers who haven’t watched anything in the last 30 days).
Python questions focused on data processing and algorithmic challenges, such as determining a user’s longest consecutive binge session from their viewing time series and simulating recommendation logic based on user profiles and program tags.
Technical Questions
- User loyalty
- Content assessment
Tips & Insights
Focusing on product sense and understanding user behavior is crucial. Be prepared to discuss metrics and modeling techniques relevant to the specific company and its industry.