Staff Product Manager - Personalization

StubHub•Aliso Viejo, CA

2d•Hybrid

About The Position

StubHub is seeking a Staff Product Manager to lead personalization for its consumer product. The role owns the product strategy and roadmap for shared ranking, fan-understanding, and predictive capabilities used across Search, Discovery, and CRM. It aims to make the experience feel tailored to each fan and to improve repeat purchasing, purchase frequency, and lifetime value. The role leads three connected systems: personalization and ranking; taste and intent understanding; and predictive lifecycle signals. It defines model objectives, signals, evaluation criteria, and product principles, while partnering with surface product managers to balance relevance, freshness, price, availability, and business requirements. It also owns the fan taste and intent model as a product, including the signals it uses, their weighting and decay, profile freshness, coverage, and access for partner teams. As a senior individual contributor, the Staff Product Manager works with machine learning engineers, data scientists, backend engineers, Design, Research, Data Infrastructure, Search, Discovery, CRM, and Growth. The role sets direction for shared capabilities, earns adoption across teams, and leads experiments and holdouts to measure incremental impact. It communicates strategy, results, and trade-offs to executives and other audiences, and mentors product managers on experimentation, working with ML teams, and data-informed prioritization. The position is hybrid, with three days in the office and two days remote, and is based in New York, NY; Century City, CA; or Aliso Viejo, CA. The anticipated base salary range is $270,000–$325,000 USD. StubHub also describes equity and a competitive benefits package, including 401(k), paid time off, paid parental leave, and health benefits.

Requirements

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 ML-powered experiences at consumer scale and explaining their incremental business results.
Experience moving repeat purchase, retention, or frequency in a consumer business, and explaining which levers worked and how impact was measured.
Track record using holdouts and experiments to isolate incremental effect and making decisions based on evidence, including when it contradicts prior assumptions.
Working knowledge of recommendation and ranking systems, user modeling, and related data infrastructure.
Ability to interpret model evaluations, assess 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 a shared capability is preferable to a surface-specific solution.
Strong instincts for what makes recommendations feel appropriate, with a point of view on 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 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 with time-limited inventory.
Familiarity with lifecycle prediction, purchase propensity, retention risk, or incremental response modeling.

Responsibilities

Own the personalization strategy and multi-year plan for ranking, taste modeling, and prediction, and make the case for the models, data, and infrastructure required.
Own shared recommendation and ranking capabilities, defining model objectives, signals, and evaluation criteria in partnership with surface product managers.
Set product principles for personalized experiences, including balancing familiar and new recommendations, handling visitors with no history, keeping recommendations current, and avoiding overly narrow recommendations.
Design shared personalization capabilities for Search, Discovery, and CRM rather than separate, surface-specific systems.
Own the fan taste and intent model, including its input signals, freshness, user coverage, and availability to partner teams.
Own shared predictive lifecycle signals for purchase intent, retention risk, and likely incremental response to outreach or offers; partner with CRM and Growth to make them actionable and evaluate interventions.
Partner with surface teams to design experiments and holdouts measuring incremental impact on repeat purchase, frequency, and lifetime value.
Connect near-term metrics to longer-term outcomes, report results in business terms, and communicate candidly about what is not working.
Lead technical discussions about personalization models, features, pipelines, and serving architecture.
Drive adoption of shared personalization capabilities by reducing integration friction and building trust with partner teams.
Present strategy, results, and trade-offs to executives, product leadership, and partner teams, explaining model behavior to non-technical audiences.
Mentor product managers on experiment design, working with ML teams, and data-informed prioritization, and help shape consumer product team practices.