VP, AI & Data

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What the market looks like

We've tracked over 4,000 senior-level AI strategy postings in the US over the last six months, with strong concentrations in San Francisco, New York, and Texas. Compensation for this cohort clusters around a $230,000 median, with top performers commanding packages into the $400,000 range. The strongest candidates combine deep technical fluency in machine learning and data infrastructure with board-level business acumen—they can bridge the gap between engineering teams and executive stakeholders, translate AI capability into competitive advantage, and navigate the organizational change required to embed AI across operations.

Job responsibilities

• Own the end-to-end AI and data strategy, from capability assessment and roadmap development to execution and business impact measurement

• Lead cross-functional teams (data science, ML engineering, analytics, infrastructure) to build and deploy AI systems that solve material business problems

• Partner with C-suite and board leadership to articulate AI opportunity, secure investment, manage risk, and communicate progress on strategic initiatives

• Build and scale a high-performing data and AI organization, including hiring, mentoring, and establishing technical and cultural standards

• Drive adoption of AI across business functions by identifying high-ROI use cases, removing organizational friction, and establishing governance frameworks

• Integrate AI capabilities with existing systems, data architecture, and business processes while managing technical debt and infrastructure modernization

• Set technical direction, evaluate emerging tools and methodologies, and ensure the organization stays competitive in a rapidly evolving landscape

Candidate requirements

• 15+ years of experience in data, analytics, machine learning, or AI—with at least 5 years in a senior leadership role (VP, Chief Data Officer, Chief AI Officer, or equivalent)

• Proven track record building and scaling AI/ML teams from the ground up, with demonstrated ability to hire, develop, and retain top technical talent

• Deep technical foundation in machine learning, data engineering, and MLOps—comfortable reviewing technical work and making architectural decisions

• Strong business acumen and P&L accountability; experience translating AI initiatives into measurable business outcomes and managing budgets at scale

• Demonstrated ability to operate in ambiguous, fast-moving environments and drive organizational change across business silos

• Excellent communication skills with the ability to present to boards, C-suite, and technical teams with equal clarity