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About This Role
Fanatics Commerce is the global leader in licensed sports merchandise, operating a vertically integrated platform that designs, manufactures, and delivers officially licensed apparel, jerseys, headwear, and collectibles for major leagues, teams, and events worldwide. With more than 900 e\-commerce sites and a global omnichannel presence across digital, in\-venue, and retail, Fanatics Commerce reaches fans in over 180 countries and powers official fan experiences for many of the world's most iconic sports properties.
At Fanatics, we bring our BOLD Leadership Principles to life every day \- building championship teams, obsessing over fans, acting with entrepreneurial speed, and delivering with a determined and relentless mindset.
#### ROLE OVERVIEW
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 — and at the center of that transformation is data. As Principal Product Manager, you will own the vision, strategy, and roadmap for Fanatics' enterprise supply chain data products and semantic layer, creating a single source of truth for both people and AI across a rapidly expanding ecosystem of applications, agents, and decision\-makers. This is a senior individual contributor role operating with a high degree of autonomy across complex, cross\-functional programs in partnership with data and engineering leaders, domain PMs, and business stakeholders. The Principal Product Manager, Enterprise Data Products \& AI delivers business and fan impact through BOLD leadership and execution excellence, leveraging data, automation, and AI\-enabled insights.
#### HOW WILL YOU DRIVE IMPACT
Success is measured by the ability to deliver results through BOLD capabilities and measurable outcomes.
Team \& Leadership Impact (Build Championship Teams)
- Partner closely with data engineering, architecture, analytics, and domain product leaders to align enterprise data product strategy with the broader ERP and supply chain transformation, ensuring cross\-functional dependencies are managed and roadmaps stay coherent as source systems evolve.
- 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 and driving consensus on shared business definitions, KPI calculations, and enterprise metrics.
- Act as a thought partner to engineering teams building enterprise data platforms, semantic capabilities, and AI agents — translating technical possibilities into meaningful business outcomes and representing the enterprise data product strategy during roadmap planning, program reviews, and executive discussions.
Fan \& Customer Impact (Obsessed with Fans)
- 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 that enable faster, higher\-quality operational decisions.
- 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, measuring success through adoption, decision quality, operational efficiency, and business impact.
- Ensure every enterprise data product is richly documented with business definitions, lineage, metadata, and certified quality standards that make data discoverable, trusted, and reusable across analytics, enterprise applications, and AI — not siloed within a single BI platform or team.
Innovation \& Problem Solving (Limitless Entrepreneurial Spirit)
- Define and execute the roadmap for making enterprise supply chain data AI\-ready through semantic enrichment, metadata standards, contextualization, quality certification, and governance — transforming raw data from ERP, WMS, OMS, PLM, and other source systems into trusted, consumable products that reliably power AI.
- Partner with engineering to identify, productize, and scale AI capabilities and agentic workflows that deliver measurable business value across Supply Chain, establishing governance, validation, and feedback mechanisms that ensure AI outputs are trusted, explainable, and decision\-grade.
- Stay current on emerging AI technologies — including LLM\-powered analytics, conversational interfaces, AI agents, and prompt engineering — and translate new capabilities into practical product opportunities across the enterprise data portfolio.
- Drive the semantic layer for Supply Chain, ensuring enterprise metrics such as OTIF, inventory turns, cost of goods, and fill rates have consistent, authoritative business definitions and calculation methods that analytics and AI can rely on.
Ownership \& Execution (Determined \& Relentless Mindset)
- Define, build, and govern a portfolio of enterprise supply chain data products — 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.
- Own the data contract model, defining how enterprise data products are accessed, versioned, and evolved as source systems change, and partner with engineering to establish observable, measurable data pipelines with embedded quality checks, anomaly detection, and certification throughout the product lifecycle.
- Translate complex, ambiguous business problems into clear product requirements, epics, success metrics, and measurable outcomes; communicate roadmap priorities, risks, and trade\-offs clearly to senior stakeholders, bringing recommendations rather than simply identifying problems.
- 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 as platforms and source systems evolve.
#### AI \& DIGITAL CAPABILITY
We are building a future\-ready organization. This role is expected to:
- Apply AI and technology to improve efficiency, quality, and outcomes
- Use data and digital tools to inform decisions and enhance performance
- Demonstrate curiosity and adaptability in adopting new technologies and ways of working
- Contribute to a culture of innovation and continuous improvement
#### CAPABILITIES \& EXPERIENCE YOU BRING
Required Qualifications:
- 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, including demonstrated success owning product vision, strategy, roadmap, prioritization, launch, adoption, and continuous improvement across complex cross\-functional programs.
- Proven experience treating enterprise data as a product with clearly defined consumers, SLAs, contracts, quality dimensions, success metrics, and evolution roadmaps — including experience defining or owning semantic layers, business glossaries, canonical models, KPI frameworks, or ontology standards across multiple business domains.
- Experience incorporating AI into product strategy, with familiarity with LLM\-powered analytics, conversational interfaces, AI agents, or prompt engineering, and a clear understanding of what makes data AI\-ready (semantic context, metadata, documentation, lineage, quality certification, and validation).
- Working familiarity with at least two supply chain domains such as inventory management, order management, sourcing, warehouse operations, fulfillment, logistics, manufacturing, or vendor management; experience working through an ERP, WMS, OMS, PLM, or broader enterprise platform transformation is strongly preferred.
- Strong technical acumen including proficiency in SQL, working knowledge of data warehouse concepts, dimensional modeling, semantic modeling, ETL/ELT patterns, APIs, and event\-driven data flows, with familiarity with cloud data platforms (Snowflake preferred) and experience partnering with data engineering teams across tools such as Snowflake, Databricks, MicroStrategy, Tableau, or Power BI.
- Exceptional written and verbal communication skills with demonstrated success building consensus across Product, Engineering, Analytics, Architecture, and business teams, and the ability to present product strategy, roadmaps, risks, trade\-offs, and recommendations to senior and executive audiences.
- Experience operating as a senior individual contributor with a high degree of autonomy and influence, including comfort influencing without direct authority and navigating competing priorities across multiple domains within Supply Chain, eCommerce, retail, manufacturing, logistics, or operations technology.
Education:
Bachelor's degree in Computer Science, Information Systems, Business, or a related field. An advanced degree is a plus but not required.
#### WORK LOCATION
- 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.
*At Fanatics, we operate with a BOLD mindset \- We Build Championship Teams, we're Obsessed with Fans, we embrace a Limitless Entrepreneurial Spirit, and we approach every challenge with a Determined and Relentless Mindset. If you're ready to contribute to a dynamic, fast\-paced environment that thrives on collaboration and growth, we want you to be part of our team.*
*ABOUT US*
*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.*
*ABOUT THE TEAM*
*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**.*
*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, 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*
*By submitting your application, you agree to our terms of service and acknowledge you have read our* *Candidate Privacy Policy.*
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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Fanatics AI Hiring
Fanatics has 1 open AI role right now. They're hiring across AI Product Manager. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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