Staff Product Manager - Growth AI & ML

Walmart•Bellevue, WA

68d

About The Position

We’re looking for a Staff Product Manager to lead the development of HBO Max’s Growth Lifecycle Machine Learning initiatives. This person will be responsible for shaping the end-to-end strategy for initiatives that drive subscriber acquisition, retention, and lifetime value leveraging Machine Learning. You’ll work closely with ML Engineers, Data Science, and growth product teams to turn predictive models into scalable decisioning systems that surface the right offer, to the right user, at the right time. This is a highly cross-functional and technically complex role that will require comfort working across machine learning infrastructure, experimentation, pricing, platform limitations, and global business needs. You’ll own the Machine learning and personalization strategy that powers aspects of customer lifecycle journeys. With your deep commerce background, you’ll write technical requirements directly for Commerce Engineering to ensure offers are implemented smoothly across product surfaces. You’ll play a key role in shaping roadmaps, informing monetization strategy, and delivering a cohesive experience across the subscriber journey.

Requirements

8+ years of product management experience in subscription, growth, monetization, or ML/AI-driven products.
Proven track record of partnering with Data Science and Engineering to launch ML-powered features at scale.
Hands-on experience with experimentation frameworks (e.g., A/B testing, multi-armed bandits, holdouts), especially for lifecycle and offer optimization.
Strong product intuition with the ability to balance data, model performance, business goals, and user experience.
Ability to write clear end-to-end product requirements—from backend offer logic to front-end presentation and messaging—ensuring ML-personalized offers are delivered with clarity, consistency, and impact.
Comfortable working with backend systems, data infrastructure, and multi-platform environments (web, mobile, CTV).
Strong communicator who can influence without authority, align cross-functional teams, and bring clarity to ambiguity.
Deep interest in personalization, behavioral science, and maximizing customer lifetime value.

Responsibilities

Strategy and Vision: Define and own the product strategy and roadmap for ML-driven personalization across the subscriber lifecycle. Translate HBO Max’s monetization and growth objectives into scalable, testable, and personalized experiences—such as upgrade prompts, discount offers, or time-bound sampling experiences of higher-tier plans. Partner with Pricing & Promotions and Commerce leadership to ensure our ML-powered offers align with broader plan and pricing strategy.
Execution and Delivery: Partner with Data Science and ML Engineers to design and deploy predictive models (e.g., take rate models, contextual bandits) that inform not only messaging, but offer type, duration, discount level, and plan selection. Operationalize model outputs into product features through configurable offer logic, eligibility criteria, and business guardrails. Ensure smooth integration of personalized offers into existing user flows (e.g., signup, cancel, resubscribe), in partnership with PMs who own those surfaces.
Measurement and Optimization Partner with Data Science, Experimentation, and Analytics teams to define KPIs, set up experiments (A/B tests, Multi-Armed Bandits), and interpret results to assess offer effectiveness and model accuracy. Iterate on models and offer strategies based on experimental results, behavioral insights, and business context. Maintain an always-on learning agenda to evolve offer personalization strategies over time.
Infrastructure and Enablement: Define product requirements for offer experiences, as well as the systems that support ML-powered offer delivery—including feature pipelines, targeting infrastructure, and override controls. Partner with Engineering and Data Platform teams to ensure infrastructure is scalable, performant, and adaptable across global markets. Advocate for tools and systems that enable non-technical teams to configure, test, and manage offers safely and effectively.