Company
Technology
PrincipalProductManager
Neural analysis suggests this role is
optimal for Senior candidates.
“Principal Product Manager. Skills: Product strategy, Data & ML, AI-native capabilities. Lead product strategy for Data & ML organization. Translate technical capabilities into user-facing product value”
What You'll Achieve.
Drive measurable outcomes for brands and creators; Drive commercial outcomes (NDR, retention, contribution margin); Connect metrics to business impact; Drive creator-brand match quality; Drive content performance prediction; Drive churn signals; Connect possibility to user value to business outcome
Industry & Context.
Explore data warehouse for insights; Define the problem
What They're Looking For.
Must Have
6+ years of product management experience, 2-3 years working with data, ML, or analytics products, Technical fluency with prior engineering background, Hands-on experience writing or reviewing SQL, Comfort navigating modern data warehouses, Demonstrated track record shipping ML-adjacent features end-to-end, Working experience with LLMs or AI-native product workflows
Nice to Have
Prior engineering background is ideal archetype, Familiarity with creator economy dynamics, Familiarity with marketplace mechanics, Familiarity with two-sided platform product challenges, Experience building internal data tools and dashboards
What You'll Do.
Lead product strategy for Data & ML organization
Translate technical capabilities into user-facing product value
Partner with engineering
and business stakeholders
Shape competitive advantage through AI-native capabilities
Own end-to-end product vision for Data & ML
Own Forge AI creator agent
Own predictive performance infrastructure
Translate ML/data capabilities into product value
Partner with Data Product Engineering leads
Align data product strategy with OKRs
Drive commercial outcomes
Define the metrics layer
Determine what we measure
Connect metrics to business impact
Identify ML-powered bets
Prioritize ML-powered bets
Drive ML-powered bets from conception to launch
How You'll Work.
Team & Collaboration
Cross-functional collaboration; Partnering with engineering; Partnering with analytics; Partnering with business stakeholders; Build trust with engineering teams
Communication Scope
Strategic conversation; Presenting strategy to leadership
Process & Methodology
Roadmap planning
Full Job Description
## The Role You'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-tech
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