Jobs at Novartis

Lead enterprise data engineering standards, governed pipelines, data contracts, observability, quality controls, lineage, testing, and compliant data delivery. Partner with governance, privacy, architecture, modeling, and platform teams to ensure reliable, traceable, protected data products. Monitor operational performance, automate deployment and validation, and resolve pipeline and data reliability issues across complex enterprise environments.

Lead enterprise data governance capabilities across KPI governance, governance platforms, or data access and security. Responsibilities include defining frameworks and controls, managing governance platforms and access policies, partnering with business, technology, privacy, legal, and compliance teams, overseeing vendors, and reporting governance maturity and operational health. The role supports data strategy, trusted data access, Data Command Center capabilities, and responsible data use in a regulated environment.

Leads the strategy, architecture, development, deployment, and optimization of an agentic AI platform. The role transforms products and workflows into scalable AI experiences, oversees AI engineering operations, establishes security and governance practices, evaluates models and orchestration frameworks, and drives observability and continuous improvement. The Associate Director partners with product, data, architecture, security, and business teams while building and mentoring high-performing engineering teams.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

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.

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.

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.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Leads enterprise core data management by defining standards for data, metadata, modeling, and quality; driving governance adoption; and delivering trusted, AI-ready datasets. Oversees data operations, service levels, documentation, metrics, risk management, and continuous improvement. Partners with engineering, business teams, vendors, and senior stakeholders while leading and developing Associate Directors and distributed teams.

Leads strategic external data partnerships supporting customer engagement and integrated marketing. Oversees vendor performance, spend, data quality, security, compliance, governance, and integration into enterprise platforms. Establishes SLAs, manages data acquisition and stewardship, resolves quality issues, reports partnership performance to leadership, and coordinates training. Evaluates emerging data offerings, AI-enabled capabilities, and partnership models to improve marketing effectiveness, customer engagement, decision-making, and enterprise value.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Coordinates technical onboarding and ongoing operations for data partners and vendors. Ensures data delivery, ingestion, metadata, catalogs, documentation, and inventories remain accurate and current. Tracks data quality, SLAs, delivery performance, operational metrics, and issue resolution across business, technical, engineering, analytics, and vendor teams. Maintains centralized issue trackers, escalates risks, translates technical problems into business impacts, and drives cross-functional follow-through in a matrixed environment.

Leads rapid design, development, and validation of agentic AI proof-of-concepts. Partners with stakeholders, product, architecture, and engineering teams to identify opportunities, build LLM-based prototypes, evaluate emerging technologies, document methodologies, and transition successful solutions into production roadmaps. The role also mentors AI engineers and supports enterprise AI innovation.

Leads the design, prototyping, and validation of AI agents and autonomous workflows. Evaluates foundation models, agent frameworks, orchestration technologies, and developer tools; creates reusable proofs of concept and reference implementations; and partners with engineering, product, platform, and architecture teams to productionize successful innovations. Establishes best practices for prompt engineering, reasoning, memory, evaluation, and observability while mentoring AI engineers and promoting experimentation.

Leads strategy, roadmap, prioritization, adoption, governance, and lifecycle management for customer-facing payer, HCP, omnichannel, and engagement data products. Partners with data enablement, technology, analytics, and business teams to create trusted, reusable, AI-ready products incorporating semantic definitions, metadata, lineage, standards, and measurable business value.

Executive Director responsible for vision, roadmap, prioritization, governance, funding, adoption, and value realization across an enterprise data product portfolio. Leads innovative data products and offerings teams, partners with business, analytics, technology, DEO, and vendors, and drives build-versus-buy decisions, reusability, stakeholder engagement, and measurable outcomes across customer, patient, commercial, and foundational data domains.