Company Knowledge & Culture Topics
Topics covering understanding a company's business model, product portfolio, strategy, culture, values, leadership, and organizational dynamics for interview preparation and market research.
Meta Applied Science Context
Familiarity with how applied research and product engineering intersect at Meta and how scientific investigations translate into product impact. Candidates should understand core product domains such as recommendation and ranking systems for Feed, Reels and Stories, content understanding and moderation, integrity and safety systems, and personalization across messaging and social graphs. Topics include how applied scientists choose high impact problems, define offline and online success metrics, design experiments and evaluation pipelines, manage production constraints such as latency and scale, and collaborate cross functionally with product and engineering teams. Knowledge of recent public research or engineering posts and an ability to connect those works to concrete product challenges is useful.
Motivation for DoorDash and Data Science Role
Topic covers motivation for applying to DoorDash and specifically to a Data Science role, including alignment with DoorDash's mission, product strategy, and data-driven decision making, as well as demonstrating cultural fit and value you bring to the team.
Meta Products & Data Culture
Overview of Meta's product ecosystem (e.g., Facebook, Instagram, Messenger, WhatsApp, Oculus/Reality Labs) and the company's data-driven culture. Covers Meta's product portfolio, business strategy, leadership approach, data governance, analytics practices, experimentation culture (A/B testing), data platform considerations, privacy requirements, and cross-functional decision-making within the organization.
Meta Product Portfolio and Use Cases
Comprehensive understanding of Meta product portfolio, how each product serves specific user segments, and the concrete value those products deliver to users and partners. Interviewers assess the candidate s ability to articulate product use cases across consumer and business segments, explain product differentiators and competitive advantages, and demonstrate where partnerships can augment or accelerate product value. Candidates should be able to identify integration points and technical constraints, recommend partner enablement models, define success metrics for product and partnership initiatives, and explain trade offs between building capabilities in product versus enabling partner solutions. This topic also covers understanding monetization levers, network effects, and measurement approaches used to evaluate product and partnership outcomes.