About Us
At #paid, we’re on a mission to empower creators to do what they love—create. Our marketplace connects vetted creators with some of the world’s most iconic brands, like McDonald’s, Samsung, and Disney, fostering authentic collaborations that drive real business results. We’ve built a marketplace that solves big challenges in the creator ecosystem, from fair pricing to algorithmic matching and content usage rights, ensuring every partnership is seamless and impactful.
With our proprietary technology and an unwavering commitment to trust and transparency, we’re revolutionizing the way brands and creators come together to make magic. Rated #1 for customer support and managed services, #paid is leading the creator marketing space. Through innovative technology and a team of ambitious humans, we're transforming the future of the creator economy.
The RoleYou'll lead the product strategy for #paid's Data & ML organization, owning how data, signals, and ML-powered features drive measurable outcomes for brands and creators. You'll translate complex technical capabilities into user-facing product value while partnering closely with engineering, analytics, and business stakeholders to shape a defensible competitive advantage through AI-native, insight-driven capabilities. This is a net-new, foundational investment in our predictable performance roadmap and the Forge AI creator agent.
Key Responsibilities
Own the end-to-end product vision for the Data & ML squad, including the Forge AI creator agent and predictive performance infrastructure
Translate complex ML/data capabilities into user-facing product value for both brands and creators
Partner with Data Product Engineering leads to align data product strategy with OKRs and drive commercial outcomes (NDR, retention, contribution margin)
Define the metrics layer: determine what we measure, how we surface insights, and connect metrics to business impact
Identify and prioritize ML-powered bets (creator-brand match quality, content performance prediction, churn signals) and drive them from conception to launch
To Be Successful, You'll Need
Technical & Data Expertise
6+ years of product management experience, with 2–3 years working directly with data, ML, or analytics products
Strong technical fluency with a prior engineering background (ex-engineer-turned-PM is the ideal archetype)
Hands-on experience writing or reviewing SQL; comfort navigating modern data warehouses (BigQuery, Snowflake, or similar)
Demonstrated track record shipping ML-adjacent features end-to-end (recommendations, ranking, prediction, scoring systems)
Working experience with LLMs or AI-native product workflows (prompting, agent design, RAG pipelines, etc.)
Product & Business Acumen
Clear ability to translate model outputs and data insights into compelling product narratives for non-technical stakeholders
Proven experience owning roadmaps tied to commercial outcomes (NDR, churn reduction, ARPU growth)
Familiarity with creator economy dynamics, marketplace mechanics, or two-sided platform product challenges is a strong plus
Experience building internal data tools and dashboards that empower ops and customer success teams
Mindset & Collaboration
Deeply curious—the kind of person who explores the data warehouse for insights, not just on demand
Comfortable with ambiguity; you define the problem before being handed one
Can engage at multiple levels: hold technical depth conversations with engineers while presenting strategy to leadership
Build trust with engineering teams quickly; they want to build with you, not just have you manage them
High ownership mentality; thrive in lean, founder-adjacent environments
About YouYou see data and ML as the operating system of competitive advantage. You're equally at home in a SQL query and a strategic conversation with founders. You approach ambiguity as an opportunity to shape what gets built next. You build genuine relationships with engineers and stakeholders because you respect their expertise and bring yours to the table. You care deeply about measurable impact and understand that great product work connects possibility → user value → business outcome. You celebrate learning from what doesn't work and stay curious about what does.
