Senior Vice President, Data & AI Enablement

Sirius XM Radio•New York, NY

14h•$342,000 - $416,000

About The Position

As the Senior Vice President, Data & AI Enablement, you will report directly to the Chief Product & Technology Officer and lead SiriusXM’s enterprise Data & AI strategy, shared foundation, governance, enablement capabilities, and measurable business impact. You will bring together the capabilities needed to make data trusted, reusable, accessible, and actionable across the company, including Data Platform & Governance, Applied ML and Experimentation, Enterprise AI Standards, Agent and LLM Platforms, and Responsible AI and Adoption. You will establish common platforms, standards, controls, and talent practices while enabling domain teams to retain ownership of the business and product outcomes they support. You will partner closely with the Chief Product & Technology Officer, Chief Financial Officer, Executive Leadership Team, and leaders across the business to align Data & AI priorities, investments, operating models, and value realization across SiriusXM.

Requirements

Typically has 20 or more years of progressive leadership experience across enterprise data, analytics, artificial intelligence, machine learning, data platforms, technology, or a comparable complex technology-enabled environment.
10–15 years of progressive leadership experience, including significant experience leading senior teams and complex organizations.
Bachelor’s degree in computer science, engineering, mathematics, statistics, business, or a related field; advanced degree is a plus.
Significant executive leadership experience across data, analytics, AI, technology, or enterprise enablement, with accountability for strategy, execution, talent, investment, and measurable business outcomes.
Deep experience shaping enterprise data strategy and modernizing shared platforms, governance, data quality, and self-service capabilities.
Proven success leading federated operating models that balance enterprise standards, platforms, controls, and talent practices with domain-level ownership and execution.
Strong expertise across modern Data & AI capabilities, including applied ML, experimentation, MLOps, generative AI and LLM platforms, responsible AI, and enterprise adoption.
Strong understanding of data architecture, identity, semantic models, metadata and lineage, privacy, security, compliance, and operational reliability.
Track record of translating Data & AI investments into measurable customer, financial, and operating outcomes, including growth, retention, engagement, personalization, efficiency, and improved decision-making.
Demonstrated ability to partner with senior executives across Product, Technology, Finance, and the business to set priorities, evaluate investments, resolve trade-offs, and drive enterprise outcomes.
Experience leading large, complex organizations through transformation, operating-model change, and organizational scaling.
Exceptional executive presence and communication skills, with the ability to translate complex technical and analytical topics into clear decisions and action.
A pragmatic, enterprise-minded leader who knows what to centralize, what to federate, and how to create leverage without unnecessary bureaucracy.
A strong people leader who develops senior talent, builds organizational capability, and fosters a culture of accountability, innovation, inclusion, and continuous improvement.
Deep commitment to responsible AI, data stewardship, privacy, security, and the trust required to scale Data & AI across the enterprise.
Experience in media, advertising, subscriptions, consumer technology, automotive, connected services, or another complex multi-domain business preferred.
Must have legal right to work in the U.S.

Nice To Haves

advanced degree is a plus

Responsibilities

Design and build the Data & AI Enablement organization, including its leadership structure, capability model, federated partnerships, decision rights, and operating model.
Partner with the Chief Financial Officer and other Finance leaders to shape enterprise data strategy, investment choices, financial and operating measurement, and value realization.
Own SiriusXM’s shared data and ML foundation, including core platform capabilities across storage, ingestion, streaming, orchestration, modeling, APIs, BI, and ML workflows.
Establish the architecture, standards, and operating practices required to ensure shared data platforms are secure, reliable, observable, cost-efficient, and scalable, while retiring redundant legacy technologies.
Partner with the CTO, CIO, Architecture, Security, Privacy, and domain engineering leaders to align platform roadmaps, technical capacity, enterprise architecture, and key operating dependencies.
Manage shared data capabilities as enterprise products with clear ownership, roadmaps, service expectations, adoption measures, lifecycle management, and retirement plans.
Build reusable data products and self-service capabilities, including enterprise metrics and semantic definitions, identity frameworks, experimentation and measurement infrastructure, curated datasets, APIs, cataloging, and enablement.
Establish a governed enterprise framework for high-value metrics and data definitions, with clear ownership, business rules, calculation logic, versioning, and approved use cases in partnership with Finance and business leaders.
Own SiriusXM’s enterprise framework for data governance, quality, privacy, access, retention, permitted use, and control automation in partnership with Privacy, Legal, Security, and domain leaders.
Establish clear accountability for critical data, enterprise definitions, quality standards, monitoring, issue resolution, and escalation, with governance embedded through scalable standards and automation.
Lead enterprise AI standards and responsible AI practices, including approved tooling, risk frameworks, data-use expectations, human oversight, model monitoring, auditability, and post-launch measurement.
Integrate data and AI governance into a coherent enterprise operating model that enables innovation while preserving clear decision rights, regulatory compliance, and security.
Drive responsible AI adoption across SiriusXM by translating governance into practical, scalable operating practices that support secure innovation, measurable impact, and human-centered use of AI.
Establish the enterprise Data Science and Applied ML discipline, including standards for model development, experimentation, evaluation, MLOps, production adoption, performance management, and talent development.
Lead Applied ML and Experimentation in partnership with Product, Engineering, Marketing, Advertising, Automotive, and business leaders to move high-value use cases from concept to measurable production impact.
Enable product-embedded ML teams through shared platforms, reusable capabilities, governance, evaluation frameworks, and communities of practice, while preserving accountability within Product and Engineering.
Build and scale SiriusXM’s Agent and LLM platform and enterprise AI enablement model, including approved tools, reusable patterns, training, adoption mechanisms, and ongoing support.
Drive AI-enabled ways of working across Product & Technology and the broader enterprise, with clear measures of adoption, productivity, quality, speed, and organizational capacity.
Partner with People and functional leaders to evolve roles, skills, and operating models so AI transformation strengthens organizational leverage, workforce readiness, and appropriate governance.
Build durable partnerships across SiriusXM’s major product and business domains, including listener experiences, personalization and discovery, automotive, advertising and MarTech, subscriptions and growth, content and programming, and enterprise operations.
Enable domain analytics and applied science teams to translate business priorities into actionable insights, experimentation, recommendations, and production capabilities that drive measurable outcomes.
Establish a clear federated operating model in which domain and product leaders retain accountability for business outcomes and priorities, while Data & AI Enablement provides shared capabilities, standards, governance, talent practices, and enterprise-wide coordination.
Partner with the CPTO, CFO, and Finance leadership to align Data & AI priorities, investment decisions, enterprise metrics, platform economics, and value realization with broader business and technology priorities.
Establish an enterprise Data & AI scorecard that measures business impact, data trust, platform performance, adoption, risk and control health, workforce effectiveness, and realized value.
Define clear executive measures of effectiveness, including speed from approved business need to trusted decision or production capability, while distinguishing realized benefits from forecasts and surfacing material risks and variances.
Provide regular executive reporting on progress, investment performance, trade-offs, dependencies, risks, decisions, and measurable outcomes.
Establish a strong functional home for Data & AI talent, with clear career frameworks, leadership expectations, talent development, and succession planning across critical disciplines.
Build the leadership capacity and organizational mechanisms required to manage a broad, enterprise-wide mandate with clarity, accountability, and effective decision-making.