Drimva

Apple

Marcom Data Strategist, AI Program

$207K–$373K

Sunnyvale, California · Full-time · Posted Oct 30, 2026

Experience
7+ years in specified data-related fields; direct experience supporting data-intensive or AI/ML-driven products.

About the job

Architects and governs reusable data foundations, products, and feedback loops that support Apple Marcom’s AI products and improve model trust, relevance, and performance.

What you'll do

  • Define data strategy, governance, metadata, lineage, ownership, and quality standards for Marcom’s AI program.
  • Translate business and product needs into shared data products, schemas, and measurable quality benchmarks with product, analytics, and engineering teams.
  • Establish data rights and usage standards with Legal, Policy, Privacy, and IS&T, and monitor data quality and model performance.

What they ask for

  • 7+ years in data strategy, data governance, data product management, or analytics strategy, including direct support for data-intensive or AI/ML-driven products.
  • Experience translating business and product requirements into shared data definitions, unified schemas, and 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 in digital marketing, consumer analytics, or machine learning ecosystems.

Nice to have

  • Experience building reusable enterprise data products such as knowledge graphs, content libraries, semantic layers, taxonomies, or data catalogs.
  • Experience with AI/ML applications, data governance operating models, measurement and telemetry pipelines, privacy and provenance frameworks, or modern data stack tooling.

Pay

USD $207,400 to $372,700 base salary

Tools

vector databases, semantic search architectures

AI focus

Builds and governs data foundations for AI products, including data for training and inference, model evaluation and feedback loops, and improvements to model prompts and performance.

Model-written summary · the original posting is authoritative

First seen Oct 31, 2026 · Last verified Nov 4, 2026 (UTC)