Top Tech Jobs & Startup Jobs in Los Angeles, CA

Lead enterprise metadata strategy, standards, governance, capture, curation, validation, lineage, and lifecycle management across data products and business assets. Partner with data governance, engineering, quality, analytics, modeling, and stewardship teams to improve data trust, discoverability, reuse, and AI readiness. Drive metadata automation, catalog synchronization, certification support, issue remediation, and stakeholder communications across a complex enterprise data environment.

Lead enterprise data quality practices by defining standards, rules, thresholds, scorecards, dashboards, and control frameworks. Partner with governance, engineering, product, and business teams to establish measurable requirements, embed controls across data products, monitor critical data assets, and resolve quality issues through root-cause analysis and remediation. Support AI-ready data initiatives through trusted datasets, lineage, certification, and governed usage while maintaining documentation and promoting continuous improvement.

Design and govern conceptual, logical, and physical enterprise data models supporting analytics, reporting, AI-ready datasets, and certified data products. Translate business requirements into scalable data structures across customer, product, payer, and territory domains. Establish modeling standards, review models against Common Data Model standards, maintain metadata and lineage accuracy, and resolve data gaps and inconsistencies. Collaborate with business, engineering, and technology stakeholders to provide modeling expertise and design guidance.

Develops and executes strategic internal communications for Novartis’ US Customer Engagement organization. Responsibilities include creating executive and associate communications, supporting change and transformation initiatives, preparing content for town halls and field forums, managing communication channels and calendars, producing written and visual digital content, and monitoring engagement metrics. The role partners with senior leaders and cross-functional teams to improve clarity, alignment, adoption, and employee engagement across geographically dispersed pharmaceutical field and headquarters teams.

Own the roadmap, backlog, delivery, and continuous improvement of performance reporting, dashboards, and analytics products within the PRIME platform. Partner with commercial stakeholders and cross-functional analytics, engineering, UX, and data teams to define requirements, deliver scalable reporting capabilities, manage agile ceremonies and releases, and monitor adoption, quality, satisfaction, and business outcomes. Ensure products align with data governance, platform standards, and regulatory requirements.

Leads enterprise analytics engineering by establishing statistical standards, validation and QA practices, measurement frameworks, reporting standards, governance, reusable data pipelines, automation, and production-grade analytics solutions. Connects Commercial, Medical, Market Access, and other teams to enable scalable, trusted, compliant, and decision-focused analytics. Requires strong technical and cross-functional leadership, analytics infrastructure expertise, and the ability to build enterprise capabilities from ambiguity.

Leads the long-term strategy, innovation roadmap, and ecosystem partnerships for NovaOS, an enterprise AI platform. Identifies emerging technologies and market opportunities, develops AI-enabled concepts and business cases, manages strategic partnerships, and transitions validated innovations into product roadmaps. Establishes governance and investment frameworks, collaborates with Product, Applied AI, Engineering, UX, and Commercial teams, and builds a high-performing strategy and innovation organization.

Leads enterprise semantic and knowledge engineering for NovaOS, including ontologies, taxonomies, knowledge graphs, semantic models, metadata standards, and governance. Partners with AI, data science, product, and engineering teams to enable AI reasoning, RAG, search, analytics, interoperability, and enterprise intelligence. Evaluates semantic technologies, establishes quality controls, and mentors semantic and knowledge engineering teams.

Designs, builds, and governs enterprise ontologies, taxonomies, semantic models, knowledge graphs, metadata services, and semantic APIs. Partners with product, AI, analytics, platform, and business teams to enable RAG, agentic AI, contextual search, reasoning, interoperability, and analytics consistency. Establishes metadata governance, quality controls, stewardship, lifecycle management, and reusable engineering patterns. Evaluates semantic technologies, mentors engineers, and communicates technical trade-offs and recommendations to diverse stakeholders.

Leads enterprise strategy, architecture, governance, and implementation for semantic layers, knowledge graphs, ontologies, taxonomies, and AI-enabled knowledge capabilities. Partners with AI, data science, product, engineering, and business teams to enable trusted AI, analytics, search, interoperability, and reusable knowledge. Establishes metadata and knowledge lifecycle standards, advances RAG and semantic search innovation, and builds a high-performing team of semantic and knowledge engineers.