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Develop and execute site robotics strategies for manufacturing operations. Lead technology evaluations, standards, URS documentation, roadmaps, pilot programs, vendor selection, and deployment of scalable robotics, computer vision, and physical AI solutions. Collaborate with global stakeholders and site teams to drive adoption, measurable business value, and operational excellence while ensuring GMP, GxP, cybersecurity, validation, data integrity, and quality-system compliance.

Leads strategy, prioritization, and delivery for data-centric CRM and audience activation capabilities. Owns the product backlog, manages a scrum team, coordinates business and technology stakeholders, supports data modeling and identity resolution, and enables segmentation and activation workflows. The role partners across CRM, data engineering, architecture, analytics, privacy, legal, and commercial teams to deliver scalable, compliant platform enhancements.

Own the site robotics strategy and automation roadmap, identifying opportunities across manufacturing, laboratories, warehouses, quality, and support operations. Lead technology assessments, pilots, business cases, and implementation planning. Serve as the subject matter expert for robotics, machine vision, digital twins, and autonomous systems while partnering with cross-functional teams and vendors. Ensure solutions meet GMP, GxP, validation, cybersecurity, FDA, and quality requirements, and communicate progress and value realization to leadership.

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 the enterprise data governance program by establishing governance councils, operating models, data ownership and stewardship frameworks, lifecycle controls, KPIs, scorecards, and maturity assessments. Partners with IT, Legal, Compliance, Privacy, and business stakeholders to operationalize auditable data controls, enforce governance standards, and deliver executive reporting across the organization.

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.

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.

Novartis invites professionals to join its 2026 Talent Community by submitting a CV and sharing career interests. The Talent Acquisition team may conduct exploratory conversations and provide updates about future opportunities across sales, market access, medical affairs, marketing, operations, research and development, and corporate functions. Membership does not guarantee employment.