Apply reinforcement learning to product decisions
Company: Meta
Role: Data Scientist
Category: Machine Learning
Difficulty: Medium
Interview Round: Onsite
Session‑level recommendations have stateful effects and feedback loops affecting long‑term retention. a) Formulate the problem as an MDP (state, action, reward, horizon) and contrast with contextual bandits. b) Outline offline policy evaluation using doubly‑robust inverse propensity scoring and describe diagnostics for support violations. c) Propose safe exploration under business constraints (e.g., conservative policy improvement). d) Address network effects and interference during evaluation and rollout.
Quick Answer: This question evaluates expertise in reinforcement learning and sequential decision-making for product optimization, covering MDP formulation, contrasts with contextual bandits, offline policy evaluation, safe exploration under constraints, and interference due to network effects; it is in the Machine Learning domain and tests both conceptual understanding and practical application. It is commonly asked to assess reasoning about long-term retention trade-offs, validation of policies from logged data under business constraints, and management of feedback loops and interference during evaluation and rollout.