Staff Product Manager - Personalization

StubHub•Aliso Viejo, CA

6h•$270,000 - $325,000•Hybrid

About The Position

StubHub is seeking a Staff Product Manager to lead personalization for its consumer product. This senior individual-contributor role owns the product strategy and roadmap for shared ranking, fan-understanding, and predictive capabilities that power personalization across StubHub. The role partners with Search, Discovery, CRM, Growth, Design, Research, Data Infrastructure, machine learning engineers, data scientists, and backend engineers to deliver relevant recommendations and measure their incremental impact on repeat purchase, purchase frequency, and lifetime value. The role owns three connected systems: personalization and ranking capabilities; a shared understanding of fan taste and intent; and predictive lifecycle signals for purchase intent, retention risk, and likely incremental response to outreach or offers. It defines model objectives, signals, evaluation criteria, and product principles, while balancing relevance, freshness, price, availability, and business requirements. It also ensures that shared capabilities are adopted across product surfaces and channels. The Staff Product Manager will set a multi-year strategy, make the case for required models, data, and infrastructure, and define how personalized experiences should work for both known and first-time fans. The role will lead experimentation and holdouts to demonstrate incremental effects, communicate strategy and results to executives and partner teams, and mentor other product managers on experimentation, ML collaboration, and data-informed prioritization. The position is hybrid, with three days in the office and two days remote, based in New York, NY; Century City, CA; or Aliso Viejo, CA. StubHub offers a competitive base salary, equity, flexible time off, and comprehensive benefits.

Requirements

8 or more years of product management experience on consumer web or native mobile products.
At least 3 years owning recommendation, ranking, or personalization systems.
Experience shipping machine-learning-powered experiences at consumer scale and explaining their incremental business results.
A track record of improving repeat purchase, retention, or frequency in a consumer business and explaining which levers worked and how results were measured.
Experience using holdouts and experiments to isolate incremental effects, and making decisions based on evidence even when it contradicts prior assumptions.
Working knowledge of recommendation and ranking systems, user modeling, and the data infrastructure behind them.
Ability to interpret model evaluations, question offline-to-online gaps, and discuss trade-offs with ML engineers and data scientists.
Ability to balance multiple product teams’ needs and identify when shared capabilities create more value than surface-specific solutions.
Strong user instincts about what makes recommendations feel right, and a point of view on personalization risks such as filter bubbles, cold start, staleness, and creepiness.
Excellent written and verbal communication, including presenting technical results and strategy to executives and non-technical audiences.
Comfort with ambiguity, strong organization, and ability to manage multiple workstreams across teams with competing priorities.

Nice To Haves

Prior ownership of a platform or shared system with internal customers.
Experience in live events, ticketing, travel, marketplaces, or other commerce involving time-limited inventory.
Familiarity with lifecycle prediction, purchase propensity, retention risk, or incremental response modeling.

Responsibilities

Own the personalization strategy and multi-year roadmap for ranking, taste modeling, and prediction.
Define model objectives, signals, evaluation criteria, and the balance among relevance, freshness, price, availability, and business requirements.
Set product principles for personalized recommendations, including experiences for first-time visitors, changing event availability, and avoiding overly narrow recommendations.
Build shared personalization capabilities for Search, Discovery, and CRM rather than duplicative surface-specific systems.
Own the fan taste and intent model, including its input signals, freshness, user coverage, and access for partner teams.
Own shared predictive lifecycle signals for purchase intent, retention risk, and incremental response to outreach or offers.
Partner with CRM and Growth to make predictive signals actionable and evaluate whether interventions change behavior; CRM and Growth retain ownership of campaign execution, messaging, and incentive strategy.
Partner with surface teams to design experiments and holdouts that measure incremental impact on repeat purchase, frequency, and lifetime value.
Connect near-term metrics to long-term outcomes, report results in business terms, and communicate candidly about what is not working.
Develop fluency in the models, features, data pipelines, and serving architecture behind personalization, and lead technical discussions with engineering and data science partners.
Drive adoption of shared ranking systems and audience capabilities by reducing integration friction and earning the trust of partner product managers.
Present strategy, results, and trade-offs to executives, product leadership, and partner teams; explain model behavior to non-technical audiences.
Mentor other product managers on experiment design, working with ML teams, and data-informed prioritization, and help shape consumer product team practices.