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About This Role
Job Purpose
The Associate Product Manager – Denials AI supports the development and execution of the Denials AI product by translating business needs, operational workflows, and data insights into clear product requirements and deliverables. The Associate Product Manager – Denials AI helps deliver features that improve denial classification, root-cause analysis, automation, and decision support across the denial and appeal lifecycle. The Associate Product Manager contributes insights that inform continuous product improvement and drive measurable impact aligned with Med-Metrix’s enterprise goals.
Duties & Responsibilities
- Support enhancements to denial classification and root-cause analysis across the denial and appeal lifecycle, helping translate clinical and administrative denial reasons into product requirements
- Assist with tracking and reporting on core Denials AI KPIs, including denial overturn rate, submission rate, denial-to-submission turnaround, and dollars recovered, across automated and manual-assisted workflows
- Support requirements for FHIR/HL7 data ingestion, EMR retrieval, and case creation on denial receipt, partnering with Data and Engineering to ensure claim, EOB, and coverage data are available for downstream models
- Support execution of the product vision and strategy by contributing insights, gathering inputs, and assisting in prioritization decisions aligned with Med-Metrix’s overall goals
- Support stakeholder communication through status updates, documentation, roadmap priorities, release impacts, and performance outcomes, ensuring clarity and alignment
- Support workflow standardization efforts while helping maintain configurable pathways to meet client, payer, and regulatory requirements
- Collaborate with client-facing and delivery teams to support workflow adoption, change management, and continuous improvement initiatives
- Assist in maintaining the product roadmap and tracking feature progress against defined goals
- Assist in defining and prioritizing product features under the guidance of Product Managers
- Translate product roadmap features into well-defined product requirements, including user stories, acceptance criteria, and supporting documentation
- Support Scrum activities, including backlog grooming, sprint planning, standups, and tracking sprint progress
- Identify and escalate risks and blockers impacting successful completion of release and sprint goals
- Support product demos, user acceptance testing (UAT), and feedback collection to inform product improvements
- Other duties as assigned
- Use, protect and disclose patients’ protected health information (PHI) only in accordance with Health Insurance Portability and Accountability Act (HIPAA) standards
- Understand and comply with Information Security and HIPAA policies and procedures at all times
- Limit viewing of PHI to the absolute minimum as necessary to perform assigned duties
Qualifications
- Bachelor’s degree in a related discipline preferred
- 1–3 years of experience in product management, business analysis, healthcare technology, or a related field
- Familiarity with AI/ML-powered products or cloud-hosted platforms (e.g., AWS), including how models and data pipelines support product features
- Experience with payer–provider data exchange standards
- Foundational understanding of the product development lifecycle, with exposure to discovery, delivery, and iteration
- Experience with or familiarity with healthcare workflows; exposure to medical claim denials processes is a plus
- Experience supporting product delivery activities, including writing user stories, defining requirements, and working within Agile environments (e.g., Jira or similar tools)
- Understanding of iterative development practices, including prototyping, testing, and continuous improvement
- Familiarity with healthcare interoperability standards is a plus
- Strong analytical and problem-solving skills, with the ability to interpret data and support insight-driven decisions
- Demonstrated critical thinking skills, with the ability to break down problems and contribute to structured solutions
- Familiarity with EHR systems or integration workflows (Epic, Cerner, Meditech, etc.) is a plus
- Proficiency in Microsoft Office Suite
- Strong interpersonal skills, ability to communicate well at all levels of the organization
- Strong problem solving and creative skills and the ability to exercise sound judgment and make decisions based on accurate and timely analyses
- High level of integrity and dependability with a strong sense of urgency and results oriented
- Excellent written and verbal communication skills required
Working Conditions
- Ability to work outside of normal business hours to support special projects
- Must possess a smart-phone or electronic device capable of downloading applications, for multifactor authentication and security purposes
- Physical Demands: While performing the duties of this job, the employee is occasionally required to move around the work area; Sit; perform manual tasks; operate tools and other office equipment such as computer, computer peripherals and telephones; extend arms; kneel; talk and hear
- Mental Demands: The employee must be able to follow directions, collaborate with others, and handle stress
- Work Environment: The noise level in the work environment is usually minimal
Med-Metrix will not discriminate against any employee or applicant for employment because of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), political affiliation, military service, veteran status, other non-merit based factors, or any other characteristic protected by federal, state or local law.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Med-Metrix, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Med-Metrix AI Hiring
Med-Metrix has 1 open AI role right now. They're hiring across AI Product Manager. Based in Parsippany-Troy Hills, NJ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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