Product Manager

Fingerpaint Group•Philadelphia, PA

37d•Hybrid

About The Position

Reset your expectations of a health and wellness agency. Independent by design and built on a foundation of empathy, Fingerpaint celebrates what you bring as both a professional and an individual. With talent across the United States and Europe launching more than 200 brands, we are committed to creating and executing meaningful experiences. In 2021, Fingerpaint was named to Ad Age's Best Places to Work and was awarded Agency of the Year by Med Ad News. Here, creativity happens naturally. We attract top talent and give them a space to grow and collaborate. Team Overview The Product & Applied AI team is a dedicated group focused on transforming our business by building unique Fingerpaint solutions and evolving our ways of working. In partnership with the business, we build the foundations, software, agents, tools, and workflows that will empower the broader agency to achieve its business goals and deliver strategic and imaginative work. Role Overview The Product Manager, Applied AI, is a strategic conduit between the business and the agency's AI product engineers for one or more products. You partner with business sponsors to deeply understand the problem, document requirements, and translate business needs into clear specifications that technical teams can build against. This role serves as a central connector between technical execution and business strategy. You build BRDs and PRFAQs, manage the product backlog, and own the product lifecycle from documented requirement through launch and adoption for your product or products, ensuring we deliver solutions that drive measurable value. Please note that this is a hybrid role that requires 2 days per week onsite in Philadelphia, PA.

Requirements

Minimum of 5–7 years of experience in Product Management, preferably within a digital agency, marketing technology, or SaaS environment.
Strong understanding of LLM capabilities and limitations, with the ability to make informed trade-offs between cost, speed, and quality. Coding skills are not required.
General fluency in data concepts, including pipelines, data quality, and structure, sufficient to scope feasibility conversations with Engineering and Data teams. Deep technical data skills are not required.
Experience with Agile/Scrum methodologies and proficiency with product management tools.
Exceptional ability to manage without authority, rallying cross-functional teams around a shared vision.
Comfort with analytics; ability to define success metrics and pivot product strategy based on performance data.

Nice To Haves

Experience working in regulated industries (healthcare/pharma) is highly preferred.

Responsibilities

Build and maintain relationships with Business Sponsors.
Conduct interviews with Business Sponsors and their teams to translate stated needs into documented problems, requirements, and features.
Write Business Requirement Docs (BRDs) as the core artifact connecting business intent to technical build.
Maintain the backlog of documented requirements and feature updates in partnership with Business Sponsors.
Develop and maintain the product roadmap, envisioning current and future versions and defining modular builds where applicable.
Validate roadmap direction with Business Sponsors to confirm business value and with Engineering to confirm feasibility before committing.
Establish, own, and communicate project timelines across the product lifecycle, from requirements through launch.
Proactively flag schedule risks to Business Sponsors and Engineering, and adjust sequencing as priorities or capacity shift.
Manage day-to-day engineering tasking related to product build-out, sequencing work against capacity and priority.
Partner with Product Applied AI Engineers to review and approve technical requirements in accordance with the BRD.
Ensure build matches specs.
Lead the agile development process (Sprint Planning, Standups, Retrospectives), serving as the primary point of contact between Business Sponsors and engineering to clarify specs and unblock decisions.
Manage cross-team dependencies and sequencing across Applied AI Engineering, Data Engineering, IT, and other partner teams.
Keep apprised of the market to consider off-the-shelf tools when applicable.
Partner with Business Sponsors to lead rollout, including co-developing training materials and documentation to drive adoption.
Define success metrics for each product at launch.
Monitor usage and satisfaction data on an ongoing basis to inform backlog prioritization and feature updates.
Establish channels for user feedback with Business Sponsors to iterate on live products.
Collaborate with Client Service teams to package successful internal tools into client-facing offerings where appropriate.
Maintain general working awareness of data privacy, security, and regulatory considerations relevant to AI products.
Flag potential policy or compliance concerns early in the requirements process and escalate to Legal/Operations for review.