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Babylist is seeking a Staff Product Manager to own personalization and discovery across its consumer experience, including the homepage feed, product recommendations, and ML-powered systems that support registry building. The role involves leveraging Babylist's editorial foundation and rich first-party dataset to build personalized, ML-powered recommendations across every consumer decision point.
Registry building is central to Babylist's product—parents curate lists of dozens of items with real stakes, creating one of the most interesting personalization problems in consumer e-commerce. The role combines latent intent signals, life-stage progression, multi-stakeholder gift dynamics, and declarative data from millions of completed registries.
As the authority on recommendations and discovery, you will set the quality bar, define the one-year horizon, and shape how the company thinks about personalization. You'll partner closely with the ML Engineering team, discussing retrieval, ranking, candidate generation, and evaluation with fluency and opinion.
You bring demonstrated experience shipping recommendations, search, ranking, or personalization systems in consumer-facing products. You understand the full ML lifecycle—data pipelines, feature engineering, model training, deployment, monitoring, and iteration—and can read model design docs and push back on architectural choices as a true peer to senior ML engineers. You know when rules beat models, when models need guardrails, and when hard-coded baselines are the right first step.
You have strategic foresight about personalization maturity curves and hold strong, opinionated views on product direction. Deep customer expertise is essential; you talk to customers regularly and bring concrete qualitative and quantitative evidence into decisions. You understand the business—how recommendations drive registry completion, GMV, ad revenue, and retention—and own impact rather than celebrating launches. You communicate with clarity that moves conversations forward and actively use LLMs and AI tools to prototype, analyze, and move faster.
