Natera - Director, Data and AI Governance

• Governance Council & Operating Model • Define enterprise data management strategy and operating model and ensure that it is executed consistently across the enterprise. • Chair and operationalize the AI & Data Governance Council, driving decision-making and accountability across legal, regulatory, compliance, IT, security, engineering, and product. • Lead a federated stewardship model, ensuring business units own data while governance enforces consistency and compliance. • Establish governance forums (steering committees, working groups, architecture boards) with clear outcomes. • Data Governance & Quality • Build and drive adoption of 360° master/reference datasets (e.g., Case360, Patient 360, Provider 360, Billing 360) and ensure they are maintained as sources of truth for analytics and AI • Partner with engineering teams to build interoperable standards that can be used to connect domain datasets to create longitudinal data products • Define and enforce enterprise data governance policies, ensuring consistency in data definitions, lineage, and stewardship across all domains • Build and manage enterprise data catalogs and metadata services to make data discoverable, trustworthy, and reusable across the organization. • Establish and operate data quality frameworks with validation rules, anomaly detection, and automated testing to ensure accuracy, completeness, and timeliness. • Embed data quality checks and lineage tracking directly into data and AI pipelines so that governance guardrails can be adopted without friction. • AI Policy Engineering & Implementation • Develop AI use case risk management framework (RMF) to evaluate AI use cases from a governance, regulatory, medical, privacy, security, and risk standpoint • Build and maintain an AI risk register and incident response plan for all AI use cases • Develop governance policies (privacy, security, quality, fairness, integrity) aligned to HIPAA, CLIA, FDA, GDPR, and emerging AI regulations. • Translate policies into technical implementations by embedding controls into: • ETL pipelines, feature stores, and model registries • CI/CD workflows for ML/GenAI models • Prompt orchestration and output logging for LLMs • Bias/fairness testing, drift detection, explainability dashboards • AI Risk & Automation • Build and execute agentic automation processes and associated guardrails to enable business process automation • Build documentation and process to ensure agent accountability through change history, audit logs, versioning etc. • Track external regulatory trends and industry standards (e.g., NIST AI RMF, EU AI Act, FDA AI/ML guidance) • AI Change Management & Vendor Governance • Lead AI change management initiatives, including training programs, awareness campaigns, and a network of governance champions to drive adoption of best practices. • Partner with Corporate Communications to cascade governance updates, AI guardrails, and usage guidelines across all levels of the organization. • Develop and enforce vendor and third-party AI evaluation frameworks, assessing external AI tools for governance, data security, model risk, and compliance posture before integration. • Track and manage vendor AI risks through standardized assessments, approvals, and monitoring processes.