Staff Product Manager, Recommendations & Discovery
RemoteUnited States or Canada
United States or CanadaRemoteFull TimeSenior LevelE-commerceMedium
Full TimeSenior LevelMediumE-commerce
Job Summary
Own recommendations and discovery at Babylist end-to-end, setting the one-year horizon for personalization and defining the quality bar for ML-powered experiences. Partner closely with ML Engineering as a peer to shape modeling, data, and evaluation infrastructure, translating ambiguous business problems into clear technical direction. Operate as an AI-enabled builder using LLMs to prototype, analyze, and ship decisions quickly while raising the company's judgment on where ML investment compounds. Mentor PMs around you, contribute to hiring, and defend unit economics models to drive registry completion, GMV, and retention.
Required Qualifications
- demonstrated product leader
- spent meaningful time inside ML-powered consumer products
- owned a recommendation, personalization, and/or discovery surface end-to-end at scale
- held Senior PM, Staff PM, GPM, or comparable Lead roles
- Real B2C ML product depth
- shipped recommendations, search, ranking, or personalization systems in a consumer-facing product
- speak fluently about candidate generation vs. ranking
- speak fluently about online vs. offline evaluation
- speak fluently about cold start
- speak fluently about exploration vs. exploitation
- speak fluently about novelty effects
- speak fluently about the tradeoffs between business objectives and user-perceived relevance
- know the failure modes
- know the diligence required to ship ML responsibly
- Real technical fluency with ML systems
- understand the full ML lifecycle
- understand data pipelines
- understand feature engineering
- understand model training
- understand deployment
- understand monitoring
- understand iteration
- comfortable reading a model design doc
- push back on architectural choices when the product reality demands it
- be a true peer to a senior ML EM
- A builder's instinct for early-stage ML
- know that early ML investment is about getting the right reps on a small number of bets, not shipping breadth
- understand when a rule beats a model
- understand when a model needs a guardrail
- understand when a hard-coded baseline is the right first step
- Strategic foresight
- can articulate the maturity curve of personalization and discovery at Babylist
- hold a strong, opinionated view of the product
- know when to update your priors
- Deep customer expertise
- talk to customers directly with regularity
- bring concrete evidence (qualitative and quantitative) into every decision
- Commercial ownership
- fluent in the business
- understand how recommendations and feed surfaces drive registry completion, GMV, ad revenue, and retention
- can defend a unit economics model
- partner with finance and data without needing them to translate
- don't celebrate launches — you own impact
- Clarity of thought
- communicate with extreme clarity that moves conversations forward fast
- don't mistake collaboration for consensus
- AI-native daily practice
- actively use LLMs and AI coding tools to prototype, analyze, query data, and move faster than you could without them
- have intuition for what current models are good and bad at
- Adaptability to change
- select for change, not against it
- jump in where needed, working across team boundaries without waiting for permission
- humble, low-ego, and biased toward action
- Must be able to lift 50 lbs
Desired Qualifications
- Background in e-commerce or marketplaces
- experience helping build or scale an ML personalization function from scratch
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