Principal Product Manager
#paid · New York, United States
- Hybrid
- Full-time
- $150,000 / year
- New York, United States
Job highlights
- Lead Data & ML product strategy and vision.
- Translate ML/data to user value.
- Drive commercial outcomes through data.
- Prioritize and launch ML-powered features.
- Collaborate with engineering and stakeholders.
About the role
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 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-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 You
You 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
The pay range listed reflects base salary only. In addition to base pay, we offer additional forms of compensation for all roles, which may include variable pay (bonus or commission), stock options, and perks and benefits, depending on the role and location. We’ll discuss total compensation openly in our first conversation so there are no surprises when it comes time for an offer.
At #paid, we believe compensation should be transparent, fair, and grounded in reality.
Our compensation framework is built around clearly defined job levels based on seniority, expertise, scope of responsibility, performance expectations, and impact.
For all publicly posted roles, we share a clear compensation range that reflects the levels associated with the role and complies with applicable pay transparency laws. Candidates are hired into a specific level, and compensation is set at the base pay associated with that level. The posted range represents the span between levels, not a negotiable pay range.
When offers are made, they are presented at the compensation set for the level a candidate is hired into and are determined by experience, skills, and role alignment — not negotiation tactics or who asks first. This ensures consistent and equitable pay for team members in the same function and at the same level.
Key skills/competency
- Product Management
- Data Science
- Machine Learning
- SQL
- Product Strategy
- Roadmap Ownership
- Creator Economy
- Marketplace Dynamics
- AI Product Development
- LLM
Skills & topics
- Principal Product Manager
- Product Management
- Data Science
- Machine Learning
- AI
- LLM
- SQL
- Creator Economy
- Marketplace
- Product Strategy
How to get hired
- Tailor your resume: Highlight your 6+ years of PM experience, 2-3 years in data/ML, and SQL skills.
- Showcase technical fluency: Emphasize your engineering background and experience shipping ML features like recommendations or predictions.
- Demonstrate business acumen: Detail your success in driving commercial outcomes like NDR and retention.
- Prepare for technical interviews: Be ready to discuss SQL, data warehousing, and LLM/AI product workflows.
- Articulate your collaboration style: Highlight your curiosity, comfort with ambiguity, and ability to build trust with engineering.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the role of the Principal Product Manager at #paid?
- The Principal Product Manager at #paid will lead the product strategy for the Data & ML organization, focusing on how data and ML-powered features deliver value to brands and creators. This role involves translating complex technical capabilities into user-facing product value and driving the development of AI-native, insight-driven features.
- What technical skills are essential for this Principal Product Manager position?
- Essential technical skills include 6+ years of product management experience with 2-3 years in data, ML, or analytics products, strong technical fluency (ideally with an engineering background), hands-on SQL experience, experience shipping ML-adjacent features, and familiarity with LLMs or AI-native product workflows.
- How does #paid approach compensation for this role?
- #paid uses a transparent compensation framework based on job levels, seniority, expertise, scope, and impact. They share clear compensation ranges for publicly posted roles and hire candidates into a specific level with a set base pay, ensuring equitable pay. Total compensation may include base pay, variable pay, stock options, and benefits.
- What is the Forge AI creator agent mentioned in the job description?
- The Forge AI creator agent is a key initiative within #paid's Data & ML organization. It represents an investment in AI-native capabilities aimed at enhancing the creator economy by leveraging AI to improve creator-brand collaborations and drive predictable performance.
- What is the significance of the 'net-new, foundational investment' for this role?
- This indicates that the Principal Product Manager role is critical to establishing and shaping new strategic directions within #paid's Data & ML organization. It's an opportunity to build foundational AI and data-driven capabilities that will define the company's future competitive advantage.
- What does #paid look for in terms of a candidate's mindset?
- #paid seeks candidates who are deeply curious, comfortable with ambiguity, capable of engaging at multiple levels (technical to leadership), can build trust with engineering teams, and possess a high ownership mentality, thriving in lean, founder-adjacent environments.
- How does #paid utilize AI in its hiring process?
- #paid may use AI tools to assist in reviewing applications, analyzing resumes, or assessing responses. These tools are intended to support the recruitment team and do not replace human judgment in the final hiring decisions.