This was basically the entire interview crammed into one question.
Structure your answer as an end-to-end ML system design, starting with business objectives and data sources, then moving through data pipeline, feature engineering, model selection, evaluation, deployment, and experimentation. Emphasize how each component ties back to Spokeo's core product of people search and how you'd measure success through online metrics and A/B tests.
Pro tip: Anchor your design in Spokeo's unique data assets (public records, social profiles) and business model (subscription-based), and discuss how you'd handle privacy and legal constraints—this shows you understand the domain beyond generic ML.
Define the recommendation goal (e.g., increase user engagement or conversion) and identify available data: user behavior logs, search queries, profile views, and Spokeo's proprietary records. Discuss data collection mechanisms and privacy compliance.
Outline batch and real-time data ingestion, storage, and processing. Describe features: user features (demographics, past behavior), item features (profile attributes), and interaction features (query-profile similarity, co-visitation). Mention handling sparse data and cold start.
Choose candidate models (e.g., matrix factorization, two-tower neural networks, gradient boosted trees) based on data scale and latency needs. Explain training process, including negative sampling and loss functions.
Define offline metrics (precision@k, recall@k, NDCG) and online metrics (CTR, conversion). Describe serving architecture: precomputed recommendations, real-time scoring via feature store, and fallback strategies.
Design an A/B test with control (current system) and treatment (new model), define primary and guardrail metrics, determine sample size and duration, and outline analysis plan. Discuss monitoring and retraining cadence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.