AI Data Program Manager - Marcom

Summary

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. Marcom is building an enterprise AI-powered intelligence platform that makes consumer research, analytics, and marketing insights actionable and accessible across global teams. The group operates at the intersection of creative strategy, machine learning engineering, and product operations to evolve standalone conversational AI tools into shared enterprise intelligence services. Key cross-functional collaborators include ML Engineers, Data Engineers, Analytics Leads, Researchers, Product Managers, and Creative teams.

Minimum Qualifications

8+ years of technical program management, product operations, or related experience delivering complex software, data platform, or machine learning initiatives. Demonstrated track record of leading cross-functional technical programs spanning engineering, data science, product, and non-technical business teams. Strong working knowledge of modern platform architectures, API-driven workflows, data pipelines, and scalable software delivery lifecycles. Proven ability to establish operating rhythms, clarify ownership, and drive cross-functional alignment in ambiguous, rapidly evolving technical environments. Demonstrated executive communication and stakeholder management skills, with proven ability to translate complex technical constraints into actionable strategic decisions.

Preferred Qualifications

Direct experience delivering applied AI, GenAI, or decision-support platforms (e.g., retrieval-augmented generation [RAG], agentic orchestration, vector search, or automated LLM evaluation harnesses). Hands-on background with platform-as-a-service (PaaS) models, developer tooling, or shared enterprise services consumed by external technical teams. Domain knowledge in marketing analytics, consumer research insights, experimentation platforms, media optimization, or MarTech stacks. Deep familiarity with enterprise data governance, source attribution/provenance protocols, and corporate data privacy standards. Experience defining user onboarding funnels and developer experience frameworks for internal APIs and shared microservices. Track record of building quantifiable business-case frameworks and measurement dashboards for emerging technology programs.

Description

This AI Data Program Manager role drives the strategic roadmap, operational execution, and scale required to transition Marcom’s conversational AI platform into an enterprise shared intelligence service. Additionally, this role will partner with cross-functional technical and business leaders to establish delivery cadences, governance frameworks, and adoption mechanisms that maximize organizational productivity.

Responsibilities

Manage the multi-phase program roadmap transitioning an internal conversational AI tool into a shared enterprise intelligence platform powering multiple downstream Marcom applications and automated workflows. Establish operating cadences, dependency tracking, and cross-functional decision-making forums across engineering, product management, analytics, consumer research, design, and creative teams. Define and operationalize standardized data intake pipelines and frameworks to ingest market research, telemetry, and domain knowledge efficiently and repeatedly. Partner with ML and Data Engineering teams to establish and enforce standards for retrieval grounding, vector search, data provenance, privacy compliance, and model output evaluation. Define, instrument, and track program-level key performance indicators (KPIs) reflecting system reliability, user adoption velocity, and quantifiable organizational productivity gains. Establish evaluation harnesses to benchmark output accuracy and citation integrity, ensuring platform outputs are defensible and trusted by executive creative and analytics leaders. Identify operational bottlenecks and technical risks across work streams, driving mitigation plans and alignment without direct hierarchical authority.