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About This Role
Job Summary
The Group Product Manager, Data, Analytics & AI leads the effort of defining the vision, strategy, roadmap, and lifecycle management of ICANN's enterprise data, analytics, artificial intelligence, reporting, and integration capabilities. This role treats enterprise data and AI capabilities as strategic products, ensuring they are aligned with business priorities, deliver measurable organizational value, and enable informed decision-making across the organization.
The role partners closely with business stakeholders, Enterprise Architecture, Enterprise Applications, Software Engineering, Security, and Data & AI teams to deliver trusted, scalable, and business-focused data capabilities. The Group Product Manager is responsible for maximizing the value of ICANN's investments in enterprise data, analytics, AI, reporting, integrations, and data governance while ensuring solutions remain aligned with organizational priorities and enterprise standards.
Key Responsibilities and Duties
Product Strategy and Roadmap
- Define strategy, roadmap, and investment priorities for ICANN's enterprise data, analytics, AI, reporting, and integration products.
- Treat enterprise data capabilities as products with defined roadmaps, measurable outcomes, and continuous improvement objectives.
- Develop product strategies aligned with organizational priorities, business capabilities, and technology strategy.
- Identify opportunities to leverage data, analytics, automation, and AI to improve organizational effectiveness and decision-making.
Data Products and Analytics
- Own the portfolio of enterprise data products, reporting platforms, analytics capabilities, dashboards, and business intelligence services.
- Partner with business stakeholders to define reporting, analytics, and decision-support priorities.
- Drive adoption of self-service reporting and analytics capabilities.
- Ensure enterprise reporting and analytics products provide trusted, timely, and actionable information.
Artificial Intelligence Product Management
- Define and manage the roadmap for AI-enabled business capabilities and enterprise AI products.
- Identify, evaluate, and prioritize AI use cases that improve business processes, workforce productivity, operational efficiency, and organizational outcomes.
- Partner with Enterprise Architecture and Security teams to ensure AI capabilities align with enterprise standards, governance, and responsible AI practices.
- Monitor emerging AI technologies and identify opportunities to deliver business value through AI innovation.
Data Governance and Information Management
- Partner with business stakeholders to establish data ownership, stewardship, and governance practices.
- Define product requirements that improve data quality, consistency, accessibility, and trustworthiness.
- Support development of enterprise data standards, metadata practices, and master data strategies.
- Ensure data products align with organizational governance, privacy, compliance, and security requirements.
Integration Strategy
- Define product roadmaps and business priorities for enterprise integrations and data exchange capabilities.
- Partner with Enterprise Architecture and Engineering teams to establish integration priorities, API capabilities, and enterprise interoperability strategies.
- Identify opportunities to simplify integrations, improve data availability, and reduce manual processes through automation.
Business Partnership and Stakeholder Engagement
- Partner with business leaders to understand reporting, analytics, AI, and data needs.
- Translate business requirements into prioritized product roadmaps and measurable outcomes.
- Serve as the primary business relationship manager for enterprise data and analytics capabilities.
- Facilitate prioritization of competing requests while balancing organizational value, technical feasibility, and strategic alignment.
Product Governance and Value Realization
- Define success measures and key performance indicators for data, analytics, AI, and reporting products.
- Monitor adoption, utilization, business value, and customer satisfaction.
- Develop executive dashboards and reporting that demonstrate value realization and product performance.
- Continuously evaluate opportunities to improve products based on user feedback, operational metrics, and evolving business needs.
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Leadership and Cross-Functional Collaboration
- Collaborate with Product Management, Enterprise Applications, Software Engineering, Enterprise Architecture, Security, Service Management, and business leaders to ensure successful delivery of product outcomes.
- Lead cross-functional planning and prioritization activities.
- Foster a culture of innovation, customer focus, and continuous improvement.
- Perform other duties as assigned.
Required Knowledge, Skills, and Abilities
- Demonstrated experience defining and managing enterprise data, analytics, business intelligence, AI, or integration products.
- Strong understanding of modern product management principles and product operating models.
- Experience with enterprise reporting, business intelligence, dashboards, and self-service analytics platforms.
- Understanding of enterprise data governance, data quality, metadata, and master data management concepts.
- Knowledge of AI technologies, generative AI, intelligent automation, and enterprise AI governance.
- Experience defining integration strategies, APIs, and enterprise data exchange capabilities.
- Strong stakeholder management, facilitation, and executive communication skills.
- Ability to translate business needs into product strategies and measurable outcomes.
- Experience using metrics and user feedback to drive product improvements.
- Strong analytical, strategic thinking, and problem-solving capabilities.
Education and Experience Requirements
- Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, Business Administration, or a related field.
- Minimum ten (10) years of progressively responsible experience in product management, enterprise data, analytics, AI, business intelligence, or related disciplines.
- Minimum five (5) years leading cross-functional product initiatives or product teams.
- Demonstrated experience delivering enterprise-scale data, analytics, AI, or reporting capabilities.
- MBA or advanced degree preferred.
Preferred Certifications
- Certified Scrum Product Owner (CSPO), Professional Scrum Product Owner (PSPO), or equivalent.
- Microsoft Fabric, Power BI, Tableau, or equivalent analytics certifications.
- DAMA Certified Data Management Professional (CDMP).
- AI or machine learning certifications from recognized cloud providers or technology vendors.
- Lean Six Sigma certification.
Success Measures
- Adoption and business value of enterprise data, analytics, AI, and reporting products.
- Improvement in organizational data quality and reporting capabilities.
- Successful delivery of AI-enabled business capabilities.
- Increased use of self-service analytics and reporting.
- Improved integration and interoperability across enterprise platforms.
- Stakeholder satisfaction and measurable business outcomes.
- Delivery of product roadmaps aligned with organizational priorities.
Language
- Fluency, both written and spoken, in English is required.
- ICANN is a global organization that values diversity; preference will be given to candidates with demonstrated skills in additional languages besides English.
Working Conditions & Physical Requirements
- Work is performed in a normal office environment with limited privacy and some exposure to background noise.
- While performing the duties of this job, the employee is frequently required to stand and walk. The employee regularly is required to sit. The employee is frequently required to talk or hear; use hands and arms to reach, handle or feel. Specific vision abilities required by this job include close vision, color vision, and ability to adjust focus.
- The employee may occasionally lift and/or move up to 25 pounds.
Targeted Base Salary Low: $148,000.00 + 20% Bonus + Benefits Targeted Base Salary High: $198,000.00 + 20% Bonus + Benefits
Note: The salary range provided here is a general estimation for the position at the time of posting based on the primary location. Salary ranges vary based upon geographic regions and countries. Final compensation packages take into consideration of a variety of factors including but not limited to a candidate’s location, work experience, knowledge, skills and other compensable factors.
Salary Context
This $148K-$198K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 roles with salary data).
View full AI Product Manager salary data →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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Icann, 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 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($173K) sits 21% below the category median. Disclosed range: $148K to $198K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Icann AI Hiring
Icann has 1 open AI role right now. They're hiring across AI Product Manager. Based in Los Angeles, CA, US. Compensation range: $198K - $198K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,486 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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