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About This Role
About This Role
Fanatics Tech is executing one of the most ambitious supply chain transformations in sports retail, rebuilding the technology backbone across product creation, merchandising, inventory, order management, sourcing, and fulfillment operations. At the center of that transformation is data: the need to make it trustworthy, AI\-ready, and consumable across a rapidly expanding ecosystem of applications, agents, and decision\-makers.
As Principal Product Manager, you will own the vision, strategy, and roadmap for Fanatics’ enterprise supply chain data products and semantic layer, creating the trusted data foundation that powers self\-service analytics, enterprise decision\-making, and AI across Supply Chain Technology.
This is not a traditional BI or reporting role. You will define, govern, and continuously evolve a portfolio of enterprise data products, semantic models, and business definitions that create a single source of truth for both people and AI.
This is a senior individual contributor role. You will operate with a high degree of autonomy across complex, cross\-functional programs, partnering closely with data and engineering leaders, domain PMs, and business stakeholders to ensure Fanatics’ enterprise data products, semantic layer, and AI\-ready foundation continue to evolve as the business scales.
How You Will Make an Impact
Data Asset \& Data Product Ownership
- This is the heart of the role. Fanatics' supply chain data spans Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Operations. It must be trusted, AI\-ready, and consumable across analytics, enterprise applications, and AI.
- Define, build, and govern a portfolio of enterprise supply chain data products by treating each data asset (e.g., Item Master, Bill of Materials, Inventory Position, Purchase Orders, Demand Signals, OTIF, Vendor Performance) as a managed product with documented consumers, SLAs, and evolution roadmaps.
- Drive the semantic layer for Supply Chain, ensuring enterprise metrics (e.g., OTIF, inventory turns, cost of goods, fill rates) have consistent, authoritative business definitions and calculation methods that analytics and AI can rely on.
- Ensure every enterprise data product is richly documented with business definitions, lineage, metadata, and certified quality standards that make data discoverable, trusted, and reusable.
- Build and maintain a discoverable knowledge layer consumable across analytics, enterprise applications, and AI, rather than siloed within a single BI platform or team.
- Partner with engineering to establish observable, measurable data pipelines with embedded quality checks, anomaly detection, and certification throughout the product lifecycle.
- Own the data contract model, defining how enterprise data products are accessed, versioned, and evolved as source systems change.
AI Readiness \& Agentic Data Strategy
- Building AI\-ready enterprise data products is a strategic objective of this role. Raw data from ERP, WMS, OMS, PLM, and other source systems must be transformed into trusted, contextual, and consumable products before it can reliably power AI.
- Define and execute the roadmap for making enterprise supply chain data AI\-ready through semantic enrichment, metadata standards, contextualization, quality certification, and governance.
- Partner with engineering to identify, productize, and scale AI capabilities and agentic workflows that deliver measurable business value across Supply Chain.
- Establish governance, validation, and feedback mechanisms that ensure AI outputs are trusted, explainable, and decision\-grade.
- Stay current on emerging AI technologies and translate new capabilities into practical product opportunities across the enterprise data portfolio.
BI Experience \& Analytics Delivery
- Own the vision for how Supply Chain and Operations stakeholders interact with data, evolving from static reporting toward self\-service BI, conversational analytics, automated operational briefings, and AI\-enabled experiences built on trusted enterprise data products.
- Champion BI products that go beyond dashboards, including contextual narratives, proactive insights, and AI\-assisted decision support grounded in certified enterprise data products.
- Partner with Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Supply Chain Operations to ensure BI capabilities align with the business processes and decisions they are meant to improve.
- Measure success through adoption, decision quality, operational efficiency, and business impact, not just delivery.
Domain Coverage
This role sits primarily within Supply Chain Technology, with close adjacency to Product Creation and Merchandising and Planning. Deep familiarity with at least two Supply Chain domains is preferred, with the ability to quickly develop a working understanding across the broader ecosystem.
- Product Creation and PLM: Items, products, line plans, bills of materials, licensing, and digital assets.
- Merchandising and Planning: Assortment planning, demand signals, inventory allocation, and merchandise performance.
- Inventory and Order Management: Inventory position, order lifecycle, fulfillment events, and OTIF.
- Sourcing and Vendor Management: Purchase orders, vendor performance, compliance data, and costing.
- Supply Chain Operations: Warehouse events, production and shop floor activity, logistics, and distribution performance
Roadmap \& Portfolio Management
- Own the vision, strategy, and roadmap for Fanatics’ enterprise supply chain data product portfolio, balancing near\-term business priorities with long\-term platform evolution and AI enablement.
- Translate complex, ambiguous business problems into clear product requirements, epics, success metrics, and measurable outcomes in close partnership with engineering and architecture.
- Manage cross\-domain dependencies and keep the enterprise data product roadmap aligned with the broader ERP and Supply Chain transformation as source systems evolve.
- Communicate roadmap priorities, risks, and trade\-offs clearly to senior stakeholders, bringing recommendations rather than simply identifying problems.
Data Governance \& Quality
- Champion data governance practices across Fanatics’ enterprise supply chain data products, ensuring quality standards, metadata, certification, access policies, and semantic consistency are consistently applied and measurable.
- Drive consensus on business definitions, KPI calculations, and enterprise metrics, particularly where multiple systems or teams currently produce conflicting numbers.
- Partner with data engineering to improve reliability, observability, and the long\-term health of the enterprise data ecosystem as platforms and source systems evolve.
Cross\-Functional Partnership \& Stakeholder Management
- Serve as the primary product voice for enterprise data products and BI across Supply Chain, influencing engineering, architecture, analytics, domain PMs, and business stakeholders without direct authority.
- Partner closely with Product Creation, Merchandising, Inventory, Order Management, Sourcing, and Supply Chain leaders to ensure the roadmap supports immediate business priorities and the long\-term AI strategy.
- Act as a thought partner to engineering teams building enterprise data platforms, semantic capabilities, and AI agents, translating technical possibilities into meaningful business outcomes.
- Represent the enterprise data product strategy during roadmap planning, program reviews, and executive discussions.
What You Bring
Experience \& Education
- 6 \-10 years of product management experience, with significant depth in enterprise data products, semantic layers, business intelligence, analytics platforms, or AI\-enabled data products.
- Demonstrated success owning product vision, strategy, roadmap, prioritization, launch, adoption, and continuous improvement across complex cross\-functional programs.
- Experience operating as a senior individual contributor with a high degree of autonomy and influence.
- Experience within Supply Chain, eCommerce, retail, manufacturing, logistics, or operations technology is strongly preferred.
- Bachelor’s degree in Computer Science, Information Systems, Business, or a related field. An advanced degree is a plus but not required.
Enterprise Data Product Strategy
- Proven experience treating enterprise data as a product, with clearly defined consumers, SLAs, contracts, quality dimensions, success metrics, and evolution roadmaps.
- Experience defining or owning semantic layers, business glossaries, canonical models, KPI frameworks, or ontology standards across multiple business domains.
- Deep understanding of what makes enterprise data discoverable, trustworthy, reusable, and consumable across BI, applications, analytics, and AI.
- Experience aligning stakeholders on shared business definitions and resolving conflicting metrics across systems or teams.
- Strong point of view on the difference between a governed enterprise data product and a one\-off reporting solution.
AI Fluency \& Product Strategy
- Experience incorporating AI into product strategy and identifying where AI can create meaningful business value.
- Familiarity with LLM\-powered analytics, conversational interfaces, AI agents, prompt engineering, or related enterprise AI capabilities.
- Understanding of what makes data AI\-ready, including semantic context, metadata, documentation, lineage, quality certification, and validation.
- Ability to evaluate emerging AI capabilities and translate them into practical roadmap opportunities.
- Experience establishing governance, access, validation, or human\-in\-the\-loop controls for AI\-enabled products is a plus.
BI \& Self\-Service Analytics
- Strong understanding of enterprise BI platforms and the data products that support trusted, self\-service analytics.
- Experience evolving organizations beyond static reporting and dashboards toward reusable data products, automated insights, conversational analytics, or decision\-support capabilities.
- Track record of measuring BI products through adoption, decision quality, operational efficiency, and business outcomes.
- Experience partnering with data engineering teams to deliver trusted and certified data sources across tools such as Snowflake, Databricks, MicroStrategy, Tableau, or Power BI.
Supply Chain Domain Knowledge
- Working familiarity with at least two of the following domains: inventory management, order management, sourcing, warehouse operations, fulfillment, logistics, manufacturing, or vendor management.
- Familiarity with Product Creation, PLM, merchandise planning, or financial planning is a plus.
- Understanding of the business context behind supply chain data, including how metrics such as OTIF, inventory position, fill rate, vendor performance, and cost of goods support operational decisions.
- Experience working through an ERP, WMS, OMS, PLM, or broader enterprise platform transformation is strongly preferred.
Technical Acumen
- Proficient in SQL and comfortable independently exploring data to validate product decisions, investigate issues, and assess quality.
- Working knowledge of data warehouse concepts, dimensional modeling, semantic modeling, ETL and ELT patterns, APIs, and event\-driven data flows.
- Familiarity with cloud data platforms, with Snowflake preferred.
- Ability to assess technical trade\-offs, write clear product requirements, and hold engineering teams accountable for outcomes.
- Experience with Agile and Scrum practices and tools such as Jira and Confluence.
Communication \& Influence
- Exceptional written and verbal communication skills, with the ability to translate complex data concepts into business value and clear product direction.
- Demonstrated success building consensus across Product, Engineering, Analytics, Architecture, and business teams.
- Comfortable influencing without direct authority and navigating competing priorities across multiple domains.
- Able to present product strategy, roadmaps, risks, trade\-offs, and recommendations to senior and executive audiences.
Why This Role
This is a rare opportunity to build something genuinely foundational, the governed, AI\-ready data product layer that will power Fanatics' supply chain intelligence for years to come. You will work at the intersection of a major enterprise transformation and the frontier of AI\-driven analytics, with the autonomy to define the approach and the stakeholder access to make it real.
The scale is real. The problem is hard. And the impact on how Fanatics makes decisions, deploys AI, and runs its supply chain, will be visible across the entire organization.
Where you’ll work and what's required
- This role may be based in Atlanta, GA or Remote \- US, subject to company eligibility requirements.
- Ability to travel for onboarding, partner meetings, team sessions, and other business needs.
Why Fanatics
- You will be building something that matters, the integration platform backbone behind the world's most passionate fan community.
- The scale is real, a global operation spanning eCommerce, wholesale, and in\-venue retail, with a partner network that continues to grow.
- You will work alongside a talented, experienced team of engineers, operators, and product leaders who are deeply energized in getting this right.
- Above all, you'll have fun doing it, building industry\-defining integration capabilities alongside some of the best people in the business.
- Competitive compensation, comprehensive benefits, and the chance to wear some very good kit.
*At Fanatics, we value transparency and honesty. If you don’t meet every single requirement, that’s okay – we still want to hear from you! We believe in the power of diverse experiences and talents. If you’re excited about the role and confident that you can contribute, don’t hesitate to apply. We’re genuinely interested in how your unique skills and perspective can help us build something amazing together.*
What's in it for you
Culture:Join a team where you're surrounded by top\-tier talent, driven by a shared passion to relentlessly enhance the fan experience. With a focus on collaboration, support, and continuous development, you’ll be empowered to help shape a our culture that celebrates both individual and team successes.
Benefits: We provide a wide range of health, financial, legal, and development assistance, including wellness programs with fitness and weight management partners, paid maternity paternity leave, and infertility treatment. Additionally, we offer flexible time off to help you recharge, along with a competitive 401k plan to support your financial future. At Fanatics, we’re dedicated to supporting you in all aspects of work and life.
Fanatics Commerce is a leading designer, manufacturer, and seller of licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods. It operates a vertically\-integrated platform of digital and physical capabilities for leading sports leagues, teams, colleges, and associations globally – as well as its flagship site, www.fanatics.com\<http://www.fanatics.com/\>.
Fanatics Commerce has a broad range of online, sports venue, and vertical apparel partnerships worldwide, including comprehensive partnerships with leading leagues, teams, colleges, and sports organizations across the world—including the NFL, NBA, MLB, NHL, MLS, Formula 1, and Australian Football League (AFL); the Dallas Cowboys, Golden State Warriors, Paris Saint\-Germain, Manchester United, Chelsea FC, and Tokyo Giants; the University of Notre Dame, University of Alabama, and University of Texas; the International Olympic Committee (IOC), England Rugby, and the Union of European Football Associations (UEFA).
At Fanatics Commerce, we infuse our BOLD Leadership Principles in everything we do:
- Build Championship Teams
- Obsessed with Fans
- Limitless Entrepreneurial Spirit
- Determined and Relentless Mindset
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting \& Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.
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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Fanatics, 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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fanatics AI Hiring
Fanatics has 1 open AI role right now. They're hiring across AI Product Manager. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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