Chief Data Scientist

Job Title: - Chief Data Architect Location: - Oakland CA 94612 (Hybrid) Duration: - 6+ months with possible extension 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 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.