Marcom Data Strategist, AI Program

Marcom is the creatively-led global team that oversees Apple’s consumer facing marketing. We ensure the flawless development and execution of world-class communications across all medias and platforms. Every day, hundreds of millions of Apple customers around the world interact with our products. We drive the strategy and creative work that provide both new and existing customers with simple, engaging and inspiring marketing experiences.

The Marcom Data Strategy function defines the data architecture, governance standards, and measurement foundations that power global marketing decisions. Sitting at the nexus of Analytics, Product Management, and Data Engineering, the team ensures all marketing intelligence systems run on unified, high-integrity data assets. Key partners include Product Managers, Machine Learning Engineers, Analytics Leads, Data Governance, IS&T, and Legal/Privacy teams.

Description

This Marcom Data Strategist role will architect and maintain the unified data and insights spine powering Marcom’s suite of artificial intelligence products and platforms. Additionally, this role translates complex business and product requirements into reusable, governed data products and continuous feedback loops that increase AI model trust, relevance, and performance over time.

Responsibilities

Define and govern the overarching data strategy for Marcom’s AI program, specifying 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 (e.g., content repositories, intelligence layers, taxonomies, and knowledge graphs) rather than one-off, single-use datasets. Translate ambiguous marketing, research, and product requirements into clear data specifications, shared entity schemas, and measurable quality benchmarks. Build and operationalize continuous evaluation and feedback loops that capture user engagement and model output telemetry, converting signals into improved data structures and model prompts. Serve as the core data strategy stakeholder 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 transition disparate, product-specific datasets into an integrated, reusable data foundation across Marcom tools. Monitor downstream data consumption and model accuracy metrics, identifying data degradation risks and prioritizing remediation backlogs with engineering teams.

Minimum qualifications

7+ years of experience in data strategy, data governance, data product management, or analytics strategy, with direct experience supporting data-intensive or AI/ML-driven products. Proven track record translating complex business and product requirements into shared data definitions, unified schemas, and measurable quality criteria. Demonstrated experience partnering cross-functionally with analytics, data engineering, and product management teams to ship and scale production data products. Hands-on experience architecting and managing both structured and unstructured data assets within digital marketing, consumer analytics, or machine learning ecosystems. Strong communication and alignment capabilities, with a proven ability to bridge technical data engineering concepts with non-technical business and creative objectives.

Preferred qualifications

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. Proven 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