Chief AI & Data Architect (Oakland)

Chief AI & Data Architect

Oakland, CA (Hybrid - 2 days onsite a week on Tuesday and Wednesday)

Duration: 6 Months

LOCAL CANDIDATES ONLY

Hybrid: The team needs this role in OGO at least 2 days a week on Tuesday and Wednesday. Other days may be needed depending on meetings and requirements being worked on.

Equipment: Client laptop will be provided.

Top Things

• Proven experience delivering enterprise-scale AI and data programs in complex, regulated environments.
• Strong understanding of data governance, cloud platforms, AI/ML lifecycle management, and risk controls.
• Executive leadership presence with the ability to influence across business, operations, and IT.

Chief AI & Data Architect Overview

The Chief AI & Data Architect is accountable for the enterprise-wide strategy, governance, and value realization of Artificial Intelligence, Advanced Analytics, and Data. This role ensures that data is trusted, governed, reusable, and AI-ready, and that AI capabilities are deployed safely, compliantly, and at scale across a regulated enterprise.

The Chief serves as the bridge between data foundations and AI-driven outcomes, ensuring alignment across business strategy, technology platforms, risk management, and regulatory obligations.

Required Qualifications

• 15+ years of leadership experience across data, analytics, AI, or enterprise technology.
• Proven experience delivering enterprise-scale AI and data programs in complex, regulated environments.
• Strong understanding of data governance, cloud platforms, AI/ML lifecycle management, and risk controls.
• Executive leadership presence with the ability to influence across business, operations, and IT.

Key Responsibilities

Enterprise AI & Data Strategy

• Define and own the integrated AI and Data strategy, roadmap, and operating model aligned with enterprise goals and regulatory commitments.
• Partner with leaders to prioritize AI and data use cases that deliver measurable value (safety, reliability, efficiency, customer outcomes).
• Ensure AI investments are grounded in strong data foundations and avoid unmanaged experimentation.

Data Governance & Stewardship

• Serve as sponsor for enterprise data governance, ensuring clarity in ownership, quality, lineage, and lifecycle management.
• Ensure data policies, standards, and controls support AI/ML, GenAI, and analytics use cases.
• Establish metrics for data quality, trust, and accessibility across critical data domains.

AI Platform, Architecture & Delivery

• Own strategy for AI and data platforms, including model lifecycle management, data pipelines, MLOps, and GenAI enablement.
• Ensure AI and data solutions are secure, scalable, auditable, and cost-effective.
• Partner with all areas of IT to define reference architectures and approved patterns.

Governance, Risk & Responsible AI

• Establish and enforce AI and data governance frameworks, including intake, classification, approval gates, and production readiness.
• Operationalize Responsible AI principles (privacy, transparency, explainability, human oversight).
• Collaborate closely with Legal, Cybersecurity, Privacy, Compliance, and Risk functions to ensure regulatory alignment.

Portfolio & Value Realization

• Oversee the enterprise AI and data portfolio, ensuring initiatives progress from pilot to production with clear value realization.
• Define success measures that include financial and non-financial value (risk reduction, reliability, regulatory confidence).
• Reduce duplication and fragmentation across AI and analytics efforts.

Executive & Board Engagement

• Serve as the enterprise technical authority on AI and Data for executive leadership, regulators, and the Board.
• Translate complex technical topics into clear, decision-oriented executive insights.
• Monitor external technology, regulatory, and industry trends to inform strategy.