Start by clarifying the problem scope and requirements, then walk through the system architecture from query understanding to candidate generation, ranking, and evaluation. Emphasize the data science aspects: features, models, metrics, and experimentation, while also addressing failure modes and mitigation strategies.
Pro tip: Frame your answer around the user's intent and the trade-offs between relevance, latency, and coverage. Show awareness of Uber's specific context, such as integrating with ride-hailing and using geospatial data.
Ask clarifying questions to understand the scale, latency requirements, and specific use cases (e.g., pickup/dropoff, business listings). Define success metrics and constraints.
Outline the end-to-end pipeline: query understanding (parsing, normalization, typo correction), candidate generation (inverted index, geospatial indexing), ranking (ML model with features), and post-processing (diversity, business rules).
Explain feature engineering (textual, geospatial, popularity, user context), model choices (e.g., learning to rank, neural networks), and how to handle fuzzy inputs and location-awareness.
Propose offline metrics (NDCG, MRR) and online metrics (CTR, success rate, time to selection). Describe A/B testing setup, guardrail metrics, and how to iterate.
Identify potential failures (e.g., no results, poor ranking, latency spikes) and propose solutions like fallback strategies, monitoring, and graceful degradation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.