Senior Product Manager II- Commerce and Personalization

About the role

Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally.

The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world.

Responsibilities

• 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

Preferred Qualifications

• Experience with ML serving infrastructure and personalization platforms (Metaflow, Kubeflow, or similar)

• Built recommendation or ranking systems at scale for complex, multi-SKU product surfaces

• Worked with prediction models to evaluate experiments or prioritize product investments

• Experience coordinating personalization across multiple surfaces to ensure consistent messaging and avoid signal cannibalization

• Background at companies with mature personalization and experimentation cultures (streaming, subscription, e-commerce, or similar)

• SQL proficiency: can 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, model architecture trade-offs, feature engineering

• Experience leading cross-functional initiatives in matrixed organizations that required influencing without direct authority

• Track record of defining and shipping challenger models or model improvements that drove measurable business impact