Staff Product Manager - Personalization

StubHub • New York, NY

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 owns the strategy and roadmap for shared ranking, fan-understanding, and predictive capabilities that power personalization across Search, Discovery, and CRM. The role partners with surface product managers, machine learning engineers, data scientists, backend engineers, Design, Research, Data Infrastructure, CRM, and Growth to turn fan signals into relevant recommendations and actionable lifecycle insights. The goal is to increase repeat purchase, purchase frequency, and customer lifetime value while helping fans discover live events they will love.

The role owns three connected systems: personalization and ranking capabilities; taste and intent understanding, including the signals, weighting, freshness, and coverage of fan profiles; and predictive lifecycle signals for purchase intent, retention risk, and likely incremental response. It defines product principles and model objectives, helps teams adopt shared capabilities, and ensures experiences account for relevance, freshness, price, availability, cold starts, and the risk of narrowing what fans see.

The Staff Product Manager will establish a multi-year strategy, make the case for required models, data, and infrastructure, lead experimentation and holdouts to measure incremental impact, and communicate results and trade-offs to executives and partner teams. The role also mentors other product managers and helps improve the consumer product organization’s practices. The position is hybrid, with three days in the office and two days remote, in New York, NY or Century City, CA.

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 frequency in a consumer business, with the ability to explain what worked and how impact was measured.
• Track record using holdouts and experiments to isolate incremental effects and make 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 gaps, and discuss trade-offs with ML engineers and data scientists.
• Ability to balance the needs of multiple product teams and determine when shared capabilities are more valuable than surface-specific solutions.
• Strong instincts for what makes recommendations feel right, and a point of view on personalization failure modes such as filter bubbles, cold starts, 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 run 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 predictive capabilities.
• Define model objectives, signals, and evaluation criteria for shared recommendation and ranking systems, balancing relevance, freshness, price, availability, and business requirements.
• Own fan taste and intent modeling, including which signals are used, how they are weighted and decay, how profiles form, and how they stay current.
• Own shared predictive models and signals for purchase intent, retention risk, and likely incremental response to outreach or offers.
• Define product principles for personalized experiences, including cold-start approaches, freshness, discovery, and avoiding overly narrow recommendations.
• Build shared personalization capabilities used by Search, Discovery, and CRM rather than duplicative surface-specific systems.
• 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 effects on repeat purchase, frequency, and lifetime value.
• Connect near-term metrics to longer-term business outcomes, report results in business terms, and communicate candidly about what is not working.
• Develop fluency in the models, features, pipelines, and serving architecture behind personalization, and lead technical discussions with engineering and data science partners.
• Drive adoption of shared systems 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 product managers on experiment design, collaboration with ML teams, and data-informed prioritization, and help shape consumer product team practices.

Benefits

• Unlimited Flex Time Off.
• 401(k).
• Health insurance.
• Vision insurance.
• Dental insurance.
• Equity.
• Paid parental leave.
• Competitive base compensation and upside that tracks with impact.
• Accelerated growth environment with autonomy to lead experiments and tests at scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed