Staff Product Manager - Personalization

StubHub•Los Angeles, CA

1d•$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 will own the product strategy and roadmap for shared ranking, fan-understanding, and predictive capabilities used across Search, Discovery, and CRM. The role aims to make each fan’s experience feel tailored to their interests and build lasting customer relationships, while improving repeat purchase, purchase frequency, and lifetime value. The position works closely with machine learning engineers, data scientists, backend engineers, Design, Research, Data Infrastructure, and partner product teams. The Product Manager will set direction for shared personalization systems, define model objectives and product principles, guide adoption across surfaces, and measure incremental business impact through experiments and holdouts. The role also communicates strategy and results to executives and partner teams and mentors other product managers. The role is hybrid, with three days in the office and two days remote, based in New York, NY or Century City, CA. The anticipated annual base salary range is $270,000–$325,000, with compensation varying based on qualifications, skills, experience, and competencies. The broader compensation package includes equity, 401(k), paid time off, paid parental leave, and comprehensive health benefits.

Requirements

At least 8 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.
Experience improving repeat purchase, retention, or purchase frequency in a consumer business and explaining which approaches worked and how results were measured.
A track record of using holdouts and experiments to isolate incremental effects and making evidence-based decisions, including when results contradict prior assumptions.
Working knowledge of recommendation and ranking systems, user modeling, and supporting data infrastructure.
Ability to interpret model evaluations, investigate offline-to-online performance gaps, and discuss trade-offs with machine-learning engineers and data scientists.
Strong instincts for user-centered recommendations and awareness of personalization failure modes 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.
Ability to work through ambiguity, stay organized, and 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 multi-year personalization strategy and roadmap for ranking, taste modeling, and prediction, and make the case for the models, data, and infrastructure required.
Define product principles for personalized recommendations, including balancing familiar and new content, serving first-time visitors, adapting to changing event availability, and avoiding overly narrow recommendations.
Build shared personalization capabilities for Search, Discovery, and CRM so teams use a common understanding of each fan.
Own the taste and intent model as a product, including its input signals, freshness, user coverage, and availability to partner teams.
Define model objectives, signals, and evaluation criteria; work with surface product managers to balance relevance, freshness, price, availability, and business needs.
Own shared predictive signals and models for purchase intent, retention risk, and likely incremental response to outreach or offers; partner with CRM and Growth to make them actionable and evaluate outcomes.
Partner with surface teams to design experiments and holdouts that measure incremental effects on repeat purchase, frequency, and lifetime value; report results in business terms.
Build fluency in and lead discussions about personalization models, features, data pipelines, and serving architecture.
Increase adoption of shared ranking systems and audience capabilities by reducing integration friction and earning partner teams’ trust.
Present strategy, results, and trade-offs to executives, product leadership, and partner teams, explaining model behavior to non-technical audiences.
Mentor other product managers on experiment design, working with machine-learning teams, and data-informed prioritization, and help shape consumer product team practices.