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. The role owns the product strategy and roadmap for shared ranking, fan understanding, and predictive capabilities that power personalization across StubHub. The goal is to help fans discover relevant live events, return more often, and build lasting relationships with the platform, while driving repeat purchase, frequency, and lifetime value.

As a senior individual contributor on the Direct Demand Consumer Product team, this person will work with machine learning engineers, data scientists, backend engineers, Design, Research, Data Infrastructure, and partner product teams. They will set direction for personalization systems used across Search, Discovery, and CRM, define model objectives and capabilities, and work with teams to integrate those capabilities into their experiences. Partner PMs own their individual surfaces and channels; this role coordinates shared investments, experiments, and success measures.

The position covers three connected areas: shared recommendation and ranking capabilities; a unified taste and intent model based on fan signals such as purchases, follows, searches, browsing behavior, and skips; and predictive lifecycle signals for purchase intent, retention risk, and likely incremental response to outreach or offers. CRM and Growth own campaign execution, messaging, and incentive strategy, while this role owns the shared product capabilities and partners with those teams to make signals actionable.

The role will develop a multi-year personalization plan, make the case for its required models, data, and infrastructure, and establish principles for relevant, fresh, and appropriately varied recommendations—including for first-time visitors and changing event inventory. It will measure incremental impact through experiments and holdouts, communicate results and trade-offs to executives and partner teams, drive adoption of shared systems, and mentor other product managers. The position is hybrid, with three days in the office and two days remote, in New York, NY or Century City, CA.

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 machine-learning-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 results were measured.
• A track record of using holdouts and experiments to isolate incremental effects and making decisions based on evidence, including when it contradicts 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 technical trade-offs with machine learning engineers and data scientists.
• Ability to balance the needs of multiple product teams and recognize when shared capabilities are more valuable than surface-specific solutions.
• Strong instincts for what makes a recommendation feel right, and an understanding 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.
• Comfort with ambiguity, strong organizational skills, and ability to manage several 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 create a multi-year plan for ranking, taste modeling, and prediction.
• Set product principles for personalized recommendations, including balancing familiar and new content, serving first-time visitors, keeping recommendations current, and avoiding overly narrow recommendations.
• Own shared recommendation and ranking capabilities, defining model objectives, signals, and evaluation criteria while balancing relevance, freshness, price, availability, and business requirements.
• Own the fan taste and intent model, including its input signals, weighting, freshness, coverage, and availability to internal teams.
• Own shared predictive lifecycle signals for purchase intent, retention risk, and likely incremental response to outreach or offers.
• Partner with Search, Discovery, CRM, Growth, and other surface teams to integrate shared capabilities into consumer experiences.
• Partner with CRM and Growth to make predictive signals actionable and evaluate whether interventions change behavior; campaign execution, messaging, and incentive strategy remain with those teams.
• Make the case to leadership for the models, data, and infrastructure required by the personalization strategy.
• Design experiments and holdouts with surface teams to measure personalization’s incremental contribution to 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.
• Build fluency in the models, features, pipelines, and serving architecture behind personalization, and lead technical discussions with engineering and data partners.
• Drive adoption of shared personalization systems by making them easy to integrate and addressing friction for internal teams.
• Present strategy, results, model behavior, and trade-offs to executives, product leadership, partner teams, and non-technical audiences.
• Mentor other product managers on experiment design, working with machine learning teams, and data-informed prioritization; help shape consumer product team practices.

Benefits

• Competitive base compensation, equity, and upside tied to impact.
• Unlimited Flex Time Off.
• 401(k).
• Health insurance.
• Vision insurance.
• Dental insurance.
• Paid parental leave.
• Comprehensive health benefits.
• Autonomy to lead experiments and tests at scale in an accelerated growth environment.

What This Job Offers

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