Jobs at Novartis

Leads the strategy, architecture, development, deployment, monitoring, and optimization of an agentic AI platform. Transforms products, processes, and workflows into scalable AI-enabled capabilities; establishes standards for performance, security, governance, and reliability; evaluates models and orchestration frameworks; partners cross-functionally on technical roadmaps; and builds and mentors 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.

Leads enterprise reporting for data quality, governance, compliance, and operational performance. Designs executive dashboards, scorecards, and reporting packages; integrates metadata and quality metrics; aligns definitions and thresholds with stakeholders; oversees incident and remediation tracking; manages vendors, schedules, documentation, and change controls; validates data integrity; automates recurring reports; and escalates risks and compliance issues to leadership.

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.

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.

Owns a portfolio of NovaOS capabilities from concept through delivery, adoption, and continuous improvement. Responsibilities include managing product backlogs and roadmaps, prioritizing features, defining requirements and success metrics, coordinating cross-functional engineering, AI, UX, data, and architecture teams, leading product discovery and release planning, monitoring adoption and KPIs, and ensuring alignment with product governance, architecture, and Responsible AI standards.

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.

Oversee strategic oncology data partnerships supporting US Commercial brands and business priorities. Identify data needs, evaluate vendors and data assets, monitor partnership spend and performance, and ensure data quality, privacy, security, compliance, governance, and stewardship. Collaborate with vendors, analytics teams, field enablement, access, and oncology stakeholders to resolve data issues, recommend fit-for-purpose datasets, translate business needs into data solutions, and provide advisory support on data interpretation and appropriate use.

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.

Leads demand intake, prioritization, governance, portfolio planning, resource capacity alignment, and delivery oversight across the Data Governance and Data Management Office. Develops executive dashboards, monitors portfolio health and milestones, escalates risks and dependencies, facilitates governance forums, standardizes program-management practices, and drives continuous improvement. Partners with senior leaders and cross-functional teams to align data initiatives with business value, organizational capacity, and strategic priorities.

Leads enterprise data product strategy, roadmaps, prioritization, adoption, governance, and lifecycle management for reference data, master data, metadata, and standards. Defines reusable, AI-ready products containing business meaning, rules, metrics, lineage, and context. Partners with data enablement, technology, analytics, governance, and business teams to scale reliable products, establish standards and service expectations, measure value, resolve issues, and drive stakeholder adoption.