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 Babylist's consumer experience, leveraging one of the richest first-party datasets in parenting. You will set the one-year horizon for product personalization, shape the company's approach to personalization, and partner closely with the ML Engineering team. This is an opportunity to make a significant impact in a company that is early in its journey of leveraging machine learning for personalization.

• Posséder la responsabilité de la personnalisation et de la découverte dans l'expérience client de Babylist, y compris le fil d'accueil, les recommandations de produits et les systèmes alimentés par l'IA.
• Définir la stratégie, les indicateurs clés de performance et la qualité des expériences alimentées par l'IA, en prenant des décisions éclairées sur les compromis difficiles.
• Collaborer étroitement avec l'équipe d'ingénierie ML pour façonner la modélisation, les données et l'infrastructure d'évaluation qui rendent le travail futur possible.
• You are a demonstrated product leader who has spent meaningful time inside ML-powered consumer products. You have owned a recommendation, personalization, and/or discovery surface end-to-end at scale — and you have the scar tissue to prove it
• You have held Senior PM, Staff PM, GPM, or comparable Lead roles. You're motivated by the chance to bring what you've learned to a company that's earlier in this journey than you've been before, and you see that as an asset, not a downgrade
• 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
• 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
• 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
• 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
• Clarity of thought. You communicate with extreme clarity that moves conversations forward fast. You don't mistake collaboration for consensus
• 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
• 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
• 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
• Strongly preferred: Background in e-commerce or marketplaces; experience helping build or scale an ML personalization function from scratch
• 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