Marketing Growth Product Lead

Gametime United

11d

About The Position

We’re looking for a Marketing Growth Product Lead to drive innovation across our marketing ecosystem, with a focus on paid channels and lifecycle marketing. This role will define the strategy, roadmap, and execution plan for how Gametime leverages technology, AI and machine learning to deliver personalization, scale and efficiency. This is a key cross-functional lead position that requires a strong understanding of marketing growth strategy, data-driven experimentation, and how ML, data science and technology can be applied to real-world marketing challenges. You will work closely with Growth, Creative, Analytics, and Engineering to identify opportunities, prioritize initiatives, and ensure successful development of new scalable solutions. This is a broad and far-reaching position looking across marketing end-to-end to see where that are innovation opportunities. From developing new ML models for lifecycle campaigns to shaping bidding strategies in paid search, you’ll set the strategy and roadmap to develop a new marketing platform leveraging ML and broader AI in marketing.

Requirements

8+ years of experience in product growth marketing roles, with strong understanding and curiosity for how technology and AI can drive marketing growth.
Strong product management skills: ability to define strategy, prioritize initiatives, and lead cross-functional teams.
Demonstrated success applying automation or ML-driven solutions to marketing use cases (lifecycle marketing, paid media, personalization).
Familiarity with ad tech ecosystems (Google Ads, Meta, programmatic) and CRM/marketing automation platforms (Braze).
Excellent communication and stakeholder management skills, with a track record of working across marketing, analytics, and engineering.
A passion for live events, growth marketing, and innovating at the intersection of customer experience and technology.

Nice To Haves

Experience with experimentation platforms (eg; Google Ads Experiments) and causal inference methods.
Background in marketplace, ticketing, travel, or e-commerce industries.
Knowledge of SKAN, incrementality testing, and app-based measurement.

Responsibilities

Own the vision and roadmap for technology, ML and AI for marketing at Gametime, aligning with broader growth and company priorities.
Serve as the bridge between marketing stakeholders and technical teams—translating business challenges into technical requirements and ensuring solutions deliver measurable outcomes.
Evangelize personalization and technology best practices across the marketing team and share results with executives and stakeholders.
Partner with marketing teams such as CRM to define opportunities for advanced audience data models, personalization, and automated journeys and workflow.
Enable predictive marketing use cases (e.g., churn prevention, LTV forecasting, event affinity scoring) by translating business needs into ML requirements.
Leverage a combination of Braze out the box solutions, custom development by 3rd parties and our internal ML teams to build the underlying data models to enable advanced orchestration and personalization
Drive adoption of dynamic content optimization, send-time optimization, and multi-channel orchestration.
Support cross-sell strategies and promotion optimization through automated decisioning and experimentation frameworks.
Develop close working relationships with marketing partners such as Google, TikTok and Meta to develop joint technology solutions to improve marketing performance
Collaborate with SEM marketers to integrate ML-driven bidding, keyword optimization, and automated campaign adjustments.
Operationalize advanced automation with ad platforms (Google, Meta, programmatic) and evaluate opportunities to pilot new betas.
Guide the automation of targeting, creative testing, and budget allocation to maximize efficiency and ROI.
Partner with Engineering and Analytics to ensure the right data pipelines, tooling, and measurement frameworks exist to power automation.
Champion experimentation frameworks and incrementality testing to measure the true impact of ML-driven marketing programs.
Define requirements for scalable, reliable infrastructure that supports continuous optimization.