Senior Product Manager II - Commerce and Personalization

Own personalization platform strategy and roadmap: Drive three parallel workstreams (model improvements, data expansion, surface experiments) to maximize subscriber lifetime value across key Commerce touchpoints Define and socialize North Star metrics: Establish success criteria for all personalization experiments; ensure consistent measurement across surfaces Partner with ML/Data Science to build better models: Translate business problems into model requirements and success criteria Build new personalization capabilities: Spec and launch propensity models, cross-surface offer orchestration (decide where to show offers, not just what), and unauthenticated personalization Design and ship high-impact experiments: Run A/B tests across surfaces with clear LTV success criteria and guardrails (retention, revenue, and engagement) Ensure model quality and rigor: Establish randomization infrastructure, LTV-native model training, unbiased training data pipelines, and holdout groups Coordinate cross-functionally without direct authority: Align with product, data science, analytics, and lifecycle/marketing teams on shared goals and experimentation frameworks Prioritize investment based on ROI: Determine trade-offs between model improvements vs. surface expansion using LTV impact data Communicate strategy and results to leadership: Present regular updates and business reviews on portfolio impact; write strategy memos with hypothesis-driven framing and validation gates Requirements 7+ years of product management experience shipping consumer products at scale (millions of users) Proven track record partnering with Data Science/ML Engineering to build and ship production ML models (recommender systems, propensity models, ranking algorithms, personalization platforms) Deep understanding of A/B testing, experimentation frameworks, holdout design, statistical significance, and measuring incrementality Experience with subscription businesses, pricing, promotions, lifecycle optimization, growth, or monetization Data-driven decision-making: comfortable defining success metrics, interpreting experiment results, making go/no-go decisions based on data Cross-functional leadership: ability to influence ML Engineering, Data Science, and surface PMs without direct authority; skilled at building consensus across competing priorities Strong stakeholder management Clear communicator: translates complex ML concepts into business language and vice versa; writes crisp strategy documents and presents effectively to leadership Experience working in fast-paced, high-growth environments with ambiguous problem spaces. #J-18808-Ljbffr Jobtailor