Job Description

Your Opportunity

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together.

Schwab Data’s AI Strategy & Governance organization was established to accelerate responsible AI adoption across the enterprise by enabling teams with clear guidance, alignment, and decision‑making frameworks. While regulatory alignment and risk mitigation are critical, our guiding principle is to enable faster, safer progress - helping Schwab move confidently from AI experimentation to scalable, reusable enterprise capability.

The Director - AI / Unstructured Data Management plays a critical leadership role in advancing Schwab’s AI readiness by ensuring AI‑relevant datasets - particularly unstructured data - are discoverable, governed, trusted, and reusable across the firm. This role sits at the intersection of AI strategy, data governance, and enterprise enablement, partnering closely with business teams, data platforms, risk, and technology to operationalize data as a strategic AI asset.

This is a leadership role for an experienced data and AI professional who combines deep technical understanding with strong enterprise influence, execution rigor, and governance discipline.

What You’ll Do

Enterprise AI Data Enablement

• Lead onboarding and management of unstructured data (text, docs, transcripts, images) in the enterprise data catalog for AI/GenAI.
• Maintain central dataset records (annotation, enrichment, lineage, and usage metadata) supporting AI initiatives.
• Deliver trusted, discoverable AI datasets that increase reuse and reduce duplication across teams.

AI Governance & Data Stewardship

• Define and run governance processes so AI data stays accurate, compliant, and fit for purpose.
• Convert regulatory, privacy, and risk expectations into clear data guardrails that enable innovation.
• Partner with Legal, Risk, Compliance, and Privacy to align with evolving AI regulations and policy.

Strategic Partnership & Influence

• Partner with AI product, data science, and platform teams by standardizing governable data assets.
• Align leaders on AI data needs, onboarding priorities, and data readiness for enterprise AI roadmaps.
• Lead on AI data management and unstructured data strategy to shape firm-wide standards.

Operational Leadership

• Build and mentor teams and matrixed contributors delivering AI data management and governance.
• Set metrics, track outcomes, and refine operating models to scale AI data capabilities.
• Monitor trends and best practices in AI data management, unstructured data, and GenAI enablement.

What You’ll Get

• An opportunity to shape Schwab’s enterprise AI future at a foundational level.
• High visibility and partnership with senior leaders across data, technology, and business teams.
• A collaborative, mission‑driven environment focused on responsible innovation.
• Competitive compensation, comprehensive benefits, and opportunities for continued growth and leadership impact.

What you have

Required Qualifications

• 15+ years of progressive experience in data, analytics, AI, or technology leadership roles, with demonstrated enterprise impact.
• 8+ years of experience leading data management, data governance, AI enablement, or related functions at scale.
• Strong understanding of AI/ML and GenAI data requirements, particularly for unstructured data (e.g., text corpora, embeddings, annotations, metadata).
• Proven experience designing and implementing enterprise data governance frameworks that balance control with speed.
• Track record of influencing senior stakeholders and driving alignment across business, technology, and risk organizations.
• Excellent communication skills with the ability to translate complex technical and regulatory concepts into clear, actionable guidance.

Preferred Qualifications

• Experience working with enterprise data catalogs, metadata management, lineage, and data quality tooling.
• Familiarity with cloud‑based data and AI platforms (e.g., GCP) and modern AI development pipelines.
• Exposure to regulated financial services environments and AI risk management practices.
• Experience enabling AI or GenAI initiatives through scalable data foundations rather than point solutions.