Staff Product Manager, Recommendations & Discovery

📋 Description

• • Own end-to-end strategy, execution, and quality bar for personalization and discovery across Babylist’s consumer experience, including homepage feed, product recommendations, and ML-powered systems that enhance registry building.
• • Set the one-year product horizon for personalization at Babylist, defining the sequence of bets, defending prioritization based on evidence, and updating direction with conviction as data evolves.
• • Partner as a true peer with the ML Engineering team to shape model behavior, data pipelines, feature engineering, candidate generation, ranking systems, and evaluation frameworks — translating ambiguous business goals into clear technical direction.
• • Define what "good" and "unacceptable" look like for ML-powered recommendations, making hard tradeoffs between business objectives (GMV, retention, ad revenue) and user-perceived relevance, novelty, and trust.
• • Raise the company’s collective judgment on ML investment by educating leadership on where personalization compounds, what is table stakes, what is a real lever, and what to avoid — building belief and securing funding for high-impact initiatives.
• • Operate as an AI-native product builder: actively use LLMs and AI tools to prototype, analyze data, query datasets, and ship experiments faster — modeling AI-integrated workflows for the broader product team.
• • Bring deep customer expertise to every decision by regularly engaging with users directly, grounding product choices in both qualitative insights and quantitative signals from millions of completed registries.
• • Drive commercial ownership by understanding how recommendations impact registry completion, GMV, retention, and ad monetization — defending unit economics models and collaborating with finance and data teams without translation.
• • Establish and maintain the quality bar for all discovery surfaces, ensuring ML systems are responsible, transparent, and aligned with user trust — recognizing failure modes like cold start, exploration-exploitation imbalance, and bias.
• • Mentor and develop other product managers, providing specific, timely feedback to elevate the team’s output and contributing to hiring as the product organization scales.
• • Lead early-stage ML product development with a builder’s instinct: prioritize depth over breadth, favoring one excellent recommender over six mediocre ones; know when a rule-based system or hard-coded baseline is the right first step.
• • Navigate complex consumer e-commerce dynamics including latent intent, life-stage progression, multi-stakeholder gift-giving, and declarative signals from millions of user-curated registries — treating registry building as one of the most compelling personalization problems in consumer tech.
• • Act as the company’s definitive voice on recommendations and discovery, ensuring all teams look to you for authoritative answers on personalization strategy, system behavior, and user impact.
• • Embrace change and operate across team boundaries without waiting for permission — demonstrating humility, low ego, and bias toward action in fast-moving, ambiguous environments.
• • Maintain fluency in the full ML lifecycle: data pipelines, model training, deployment, monitoring, and iteration — capable of reading model design docs, challenging architectural choices, and holding technical conversations at the level of senior ML engineers.