Senior Product Manager, Paid

• 6+ years of product management experience, owning a product area end to end
• Experience setting a north star metric, with secondary and counter metrics, and using it to make prioritization and go/no-go calls
• A history of owning roadmaps tied to commercial outcomes, such as NDR, churn reduction, or ARPU growth
• Comfort with ambiguity: you structure problems, form hypotheses, and find evidence even when data is thin
• Experience balancing the needs of different user groups, and working with design from the start to resolve the tradeoffs
• 2+ years on data, ML, or analytics products, including shipping features such as recommendations, ranking, prediction, or scoring end to end
• The technical fluency to go deep with data and ML engineers, including reading SQL and working in a modern warehouse such as BigQuery or Snowflake
• The ability to turn model outputs and data insights into clear narratives for non-technical stakeholders
• You see data and ML as a source of lasting advantage, not a feature list. You're comfortable in a SQL query and in a strategy conversation with leadership. You define the problem before you're handed one, and you ask "why" until the real motivation is clear, for brands and creators alike
• You build trust with engineers by respecting their expertise and bringing your own, and you bring design in at the start, not the end. You care about measurable impact, and you understand that good product work connects a possibility to a user benefit to a business outcome. You take ownership of your results and your mistakes, and when user needs conflict with team goals, you put users first

Desirable

• As Senior Product Manager for our Data & ML squad, you'll own the roadmap for how data, signals, and machine learning power the marketplace
• Your first focus is predictive performance infrastructure, starting with creator–brand match quality: helping brands work with the right creators, and proving that it keeps them with us. You'll build it as the foundation for Forge AI, our AI agent for creators, and for campaign automation
• Own the product roadmap for the Data & ML squad, starting with predictive performance infrastructure and creator–brand match quality
• Find the signal before building: use data and conversations with brands and creators to learn what actually drives brand retention
• Turn ML and data capabilities into clear product value for brands and creators, and build them so other products, including Forge AI, can reuse them
• Partner with Data Product Engineering leads to tie data product strategy to company OKRs and commercial outcomes such as net dollar retention, retention, and contribution margin
• Define a north star metric for each product or feature, with secondary and counter metrics that signal when something is going wrong
• Decide when to run experiments, design them, and analyze the results with engineering and data partners
• Balance brand and creator needs when they compete, and explain the tradeoffs you make
• Sequence how ML-powered features earn brands' trust, from predictions to recommendations to automated actions
• Work with design from the start of every project, and make clear go/no-go and deprioritization calls
• Study competitors and market trends to find opportunities and anticipate constraints
• Report results, learnings, and next steps clearly