Marcom Data Strategist, AI Program

Apple • Sunnyvale, CA

3d

About The Position

Marcom is Apple’s creatively led global team responsible for consumer-facing marketing and the development and execution of communications across media and platforms. The Marcom Data Strategy function defines the data architecture, governance standards, and measurement foundations that support global marketing decisions. Working across Analytics, Product Management, and Data Engineering, the team ensures marketing intelligence systems use unified, high-integrity data assets. Key partners include Product Managers, Machine Learning Engineers, Analytics Leads, Data Governance, IS&T, and Legal and Privacy teams.

The Marcom Data Strategist will architect and maintain the unified data and insights foundation for Marcom’s artificial intelligence products and platforms. The role translates complex business and product requirements into reusable, governed data products and continuous feedback loops that improve AI model trust, relevance, and performance over time. It also serves as a core data strategy stakeholder in cross-functional work on data rights, access, provenance, and permitted AI use.

Requirements

• At least 7 years of experience in data strategy, data governance, data product management, or analytics strategy, including direct experience supporting data-intensive or AI/ML-driven products.
• Proven ability to translate complex business and product requirements into shared data definitions, unified schemas, and measurable quality criteria.
• Experience partnering with analytics, data engineering, and product management teams to ship and scale production data products.
• Hands-on experience architecting and managing structured and unstructured data assets within digital marketing, consumer analytics, or machine learning ecosystems.
• Strong communication and alignment skills, including the ability to bridge technical data engineering concepts with non-technical business and creative objectives.

Nice To Haves

• Hands-on experience designing and scaling reusable enterprise data products, including knowledge graphs, content libraries, semantic layers, taxonomies, or data catalogs.
• Direct experience supporting AI/ML applications, such as personalized recommendation systems, content intelligence pipelines, generative AI workflows, or automated model evaluation frameworks.
• Expertise designing practical data operating models covering ownership charters, intake prioritization, stewardship programs, and recurring governance cadences.
• Experience building measurement loops and telemetry pipelines that translate real-world user interactions and performance signals into refined data structures and model inputs.
• Background navigating enterprise data privacy policies, licensing restrictions, copyright parameters, and data provenance frameworks.
• Familiarity with modern data stack tooling, vector databases, and semantic search architectures.

Responsibilities

• Define and govern the data strategy for Marcom’s AI program, including core data domains, ownership boundaries, standardized metadata definitions, lineage tracking, and data quality thresholds.
• Partner with Product Management, Analytics, and Data Engineering to design and deliver shared, reusable data products, such as content repositories, intelligence layers, taxonomies, and knowledge graphs.
• Translate marketing, research, and product requirements into data specifications, shared entity schemas, and measurable quality benchmarks.
• Build and operationalize feedback and evaluation loops that capture user engagement and model output telemetry and use those signals to improve data structures and model prompts.
• Represent data strategy in cross-functional forums with Legal, Policy, Privacy, and IS&T to establish data rights, access tiers, provenance standards, and usage constraints for AI training and inference.
• Establish data stewardship and intake cadences to integrate disparate, product-specific datasets into a reusable data foundation across Marcom tools.
• Monitor downstream data consumption and model accuracy metrics, identify data degradation risks, and prioritize remediation backlogs with engineering teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed