Senior Product Manager II- Commerce and Personalization
$155,700–$228,600 year
On-site · New York City, New York, United States or San Francisco, California, United States
Job Summary
Senior Product Manager II, Commerce and Personalization owning the personalization platform strategy and roadmap across Disney’s Commerce touchpoints (Disney+, Hulu, ESPN). Drive three parallel workstreams (model improvements, data expansion, surface experiments) to maximize subscriber lifetime value, define North Star metrics for personalization experiments, partner with ML/Data Science to build better models (propensity, recommender, ranking), design and ship high-impact experiments with clear success criteria, ensure model quality with robust experimentation infrastructure, and coordinate cross-functionally with product, data science, analytics, and lifecycle/marketing. Responsibilities include defining and socializing strategy, building new personalization capabilities, and communicating results to leadership. Required and preferred qualifications emphasize extensive PM experience, ML/data-science collaboration, experimentation rigor, subscription monetization, cross-functional leadership, and ability to influence without direct authority; education requires a bachelor’s degree in CS/Engineering/Business/Economics/Statistics or equivalent.
Required Qualifications
- 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: define success metrics, interpret experiment results, make go/no-go decisions based on data
- Cross-functional leadership: influence ML Engineering, Data Science, and surface PMs without direct authority; build consensus across competing priorities
- Strong stakeholder management
- Clear communicator: translates complex ML concepts into business language; writes strategy documents and presents to leadership
- Experience working in fast-paced, high-growth environments with ambiguous problem spaces
- Experience with ML serving infrastructure and personalization platforms (preferred)
- SQL proficiency: write queries to pull experiment data, validate model outputs, and debug attribution issues
- Understanding of ML model constraints: online vs. offline models, training data bias, feature engineering
- Track record of leading cross-functional initiatives in matrixed organizations
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