Fractional Head of AI

Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we'll add you to our candidate network and may reach out when relevant roles come up.

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What the market looks like

Over the last six months, we've tracked 100+ executive-level postings in AI strategy across the US, with concentrated hiring in California, New York, and Texas. Organizations across technology, financial services, professional services, and life sciences are building out fractional AI leadership capacity — bringing in experienced strategists on a part-time or advisory basis to shape roadmaps, assess organizational readiness, and bridge the gap between technical capability and business outcomes. The strongest candidates combine deep AI/ML fluency with board-level communication skills and a track record of moving strategy into execution across multiple industries.

Job responsibilities

• Partner with the C-suite and board to define AI transformation strategy, assess competitive positioning, and align AI initiatives with business objectives

• Evaluate current AI/ML capabilities, data infrastructure, and team composition; identify gaps and recommend build-vs-buy-vs-partner approaches

• Shape the roadmap for AI adoption across the organization — prioritize pilot projects, scale winners, and manage organizational change

• Lead governance and risk conversations around responsible AI, data quality, model validation, and compliance with emerging regulations

• Coach internal teams (product, engineering, data) on AI-native product thinking and help embed AI literacy across the organization

• Advise on talent acquisition, retention, and org design as the AI function scales

• Serve as a strategic thought partner and advisor on emerging AI capabilities, market trends, and competitive threats

Candidate requirements

• 10+ years of experience in AI/ML, data science, or AI strategy — with at least 5 years in a leadership or advisor role shaping AI direction at scale

• Proven ability to translate AI/ML concepts for non-technical stakeholders and influence C-suite and board-level decision-making

• Track record of successfully moving AI strategy into execution — shipped products, scaled teams, or led transformations across multiple organizations or business units

• Deep hands-on knowledge of machine learning, LLMs, generative AI, MLOps, or data engineering — not theory alone

• Experience building or scaling AI/ML teams, defining hiring strategies, and developing internal talent

• Comfort working in ambiguity and advising on governance, responsible AI, and emerging regulatory landscapes