Principal Product Manager

Wisdom•New York, NY

1d•Remote

About The Position

Wisdom blends industry expertise with advanced technology to make dental practices work better for everyone involved. We believe dentistry is about people, and we exist to make the future of dentistry stronger and more sustainable for dentists, their teams, and the patients they serve. We match administrative teams with expert billers and custom-built technology to take on the heavy lifting of dental billing while maximizing dentists’ time in-office, and their bottom line. Coming from a fresh $21M Series A round of funding we are looking for exceptional candidates to help us build a category-defining company. We are a fully distributed, remote-first team with employees across the US. We’re looking for a Principal Product Manager (AI) who can bridge deep domain expertise with new AI capabilities and re-imagine what Dental billing looks like. This is a hands-on, high-impact role where you’ll own products from discovery through launch, working directly with domain experts, revenue cycle specialists, dental offices, and engineering teams to build AI-native solutions. You’ll be responsible for understanding complex, manual workflows in dental insurance billing such as claims aging, billing, appeals and reimagining them with AI agents and automation. This requires both strategic product thinking and willingness to get into the weeds: shadowing billers, reading insurance EOBs, understanding PMS integrations, and prototyping AI workflows. This role reports directly to the Head of Product and will help shape Wisdom’s product strategy for the next 1-3 years.

Requirements

8+ years as a Product Manager, with at least 2 years working on AI/ML products or complex SaaS products
Deep domain expertise in at least one complex vertical (healthcare, fintech, insurance, logistics, legal etc) where you became a subject matter expert
Hands-on AI product experience: You’ve shipped products using LLMs / agentic frameworks, or ML models (not just “AI strategy”)
Technical fluency: You can read code, prototype workflows, and have detailed technical discussions with engineering as well as design teams.
Bias for action: You don’t wait for perfect data—you shadow users, run experiments, and iterate quickly

Nice To Haves

You’ve built human-in-the-loop AI systems where humans review/correct AI actions
You’ve worked on workflow automation or RPA (robotic process automation) products
You’ve shipped products in regulated industries (healthcare, finance) and navigated compliance constraints
You’ve done ethnographic research or jobs-to-be-done discovery (shadowing users for days, not hours)
You have strong written communication skills (you write crisp PRDs, strategy memos, and can explain complex workflows clearly)

Responsibilities

Own AI-First Product Strategy
Define product vision and roadmap for AI-powered products
Make build-vs-buy decisions on AI capabilities (LLMs, agentic frameworks, ML models)
Balance short-term wins (human-assisted tools) with long-term bets (autonomous AI agents)
Deep Domain Discovery
Shadow revenue specialists, billers, and dental office managers to understand workflows end-to-end
Become an expert in dental insurance processes: claims aging, EOB reconciliation, appeals, timely filing deadlines, payer-specific rules
Translate tribal knowledge from 10+ year industry veterans into structured product requirements and context for AI tools.
Build AI-Enabled Products
Partner with engineering to design AI agent architectures (agentic workflows, human-in-the-loop patterns, escalation logic)
Write detailed product strategy, vision docs, and specs with user flows, success metrics, and AI accuracy thresholds
Design Partnership: Work closely with the Design team to build AI-native user experiences that effectively blend autonomous actions with necessary human oversight.
Define gradual autonomy models: when should AI act autonomously vs escalate to humans
Launch and Iterate
Run pilots with 5-10 offices, gather feedback, iterate based on real-world performance
Define KPIs for AI products, setup iteration strategy based on evals
Build feedback loops: How do we train AI agents based on human corrections?