Staff Product Manager (Recommendations & Discovery)

Join Babylist, a leading parenting resource, as a Staff Product Manager. In this role, you will own personalization and discovery across the consumer experience, leveraging one of the richest first-party datasets in parenting. You will set the strategy, quality bar, and impact for recommendations and discovery, and partner closely with the ML Engineering team. This is an opportunity to shape the future of personalization at Babylist and make a meaningful impact on the lives of millions of families.

• Ownership of personalization and discovery across Babylist's consumer experience, including the homepage feed and product recommendations.
• Defining the strategy, KPIs, quality bar, and impact for recommendations and discovery at Babylist, and setting the one-year horizon for product personalization.
• Partnering closely with the ML Engineering team to shape the modeling, data, and evaluation infrastructure that supports the company's goals.
• Real B2C ML product depth. You have shipped recommendations, search, ranking, or personalization systems in a consumer-facing product. You can speak fluently about candidate generation vs. ranking, online vs. offline evaluation, cold start, exploration vs. exploitation, novelty effects, and the tradeoffs between business objectives and user-perceived relevance. You know the failure modes and the diligence required to ship ML responsibly
• Commercial ownership. You are fluent in the business. You understand how recommendations and feed surfaces drive registry completion, GMV, ad revenue, and retention. You can defend a unit economics model and partner with finance and data without needing them to translate. You don't celebrate launches — you own impact
• Real technical fluency with ML systems. You don't write production model code, but you understand the full ML lifecycle — data pipelines, feature engineering, model training, deployment, monitoring, and iteration. You're comfortable reading a model design doc, pushing back on architectural choices when the product reality demands it, and being a true peer to a senior ML EM rather than a translator
• Adaptability to change. You select for change, not against it. You jump in where needed, working across team boundaries without waiting for permission. You are humble, low-ego, and biased toward action
• A builder's instinct for early-stage ML. You know that early ML investment is about getting the right reps on a small number of bets, not shipping breadth. You understand when a rule beats a model, when a model needs a guardrail, and when a hard-coded baseline is the right first step. You'd rather ship one excellent recommender and learn from it than launch six mediocre ones
• Strongly preferred: Background in e-commerce or marketplaces; experience helping build or scale an ML personalization function from scratch
• AI-native daily practice. You actively use LLMs and AI coding tools to prototype, analyze, query data, and move faster than you could without them. You have intuition for what current models are good and bad at. This is table stakes at Babylist — every team uses AI daily — and we expect you to model what AI-native PM craft looks like for the team around you
• Clarity of thought. You communicate with extreme clarity that moves conversations forward fast. You don't mistake collaboration for consensus
• Deep customer expertise. This is the irreplaceable PM contribution in a builder world, and it has to be a genuine strength. You talk to customers directly with regularity and bring concrete evidence (qualitative and quantitative) into every decision
• Strategic foresight. You can articulate the maturity curve of personalization and discovery at Babylist — where we are, what's next, and the effort behind each step. You hold a strong, opinionated view of the product and you know when to update your priors