Associate Director, Data Systems & AI, Enterprise Competitive Intelligence

Key Responsibilities:

Strategic Vision and Roadmap

• Drive the ECI Data Systems and AI vision, roadmap and delivery plan, aligned to ECI priorities, Novartis business-unit needs and enterprise technology direction.
• Translate ECI’s ambition for a single, trusted CI knowledge layer into sequenced initiatives, milestones, governance forums and investment decisions.
• Maintain a balanced build/buy/partner perspective, evaluating whether capabilities should be built internally, sourced from vendors, or co-developed with partners.
• Represent Data Systems and AI on the ECI Leadership Team and support ECI Steering Committee discussions on AI, repositories, data governance, interoperability, prioritization and funding.

CI Repository and Knowledge Layer

• Lead the design and implementation of the ECI repository / knowledge layer, consolidating curated CI outputs and enabling clear navigation across TAs, business units, congress outputs, Strategic CI, and other CI domains.
• Ensure CI data and outputs are findable, reusable, appropriately tagged and governed, including metadata, taxonomy, ownership, access rights and archival rules
• Drive interoperability with key enterprise and business-unit systems
• Ensure the repository is not only a document store, but evolves toward an AI-ready data product that can be consumed by humans, AI agents and other approved enterprise applications.

AI-Enabled Workflows and Automation

• Identify, prioritize and deliver AI-assisted workflows that reduce manual effort and improve quality in recurring ECI activities
• Develop reusable templates, prompts, workflow patterns and validation checkpoints that ECI associates can apply consistently across therapeutic areas and business-unit requests.
• Partner with ECI leaders and specialists to define future-state workflows before tool selection, ensuring AI augments expert judgement rather than replacing critical CI interpretation.
• Support pilots and MVPs over validated CI content and potential CI agent capabilities anchored in governed internal data.

Governance, Quality and Access Management

• Establish data governance standards for CI assets, including lineage, ownership, lifecycle, metadata, access policies, quality checks and decision rights.
• Ensure CI data is accurate, current, appropriately curated and protected, with expert oversight and clear escalation routes for sensitive or uncertain content.
• Coordinate access management across ECI, insights teams and broader approved user groups, balancing enterprise accessibility with confidentiality, compliance and business-unit needs.
• Work with Legal, ERC, DDIT, knowledge-management teams and business owners to ensure AI-enabled CI workflows comply with Novartis policies, privacy, security and responsible AI expectations.

Stakeholder Engagement and Enterprise Partnership

• Serve as the primary ECI interface for data systems and AI topics with business-unit CI / insights leaders, ECI TA Leads, Strategic CI, DDIT, NKC, and other platform owners
• Build alignment across stakeholders on data standards, workflow priorities, repository design, access model, change-management needs and success measures.
• Create feedback loops with end users to ensure systems and AI workflows improve usability, relevance, trust and adoption across ECI and its stakeholders.
• Actively promote cross-BU reuse of CI outputs and reduce duplication by making the right intelligence easier to find, compare and apply.

Delivery, Vendor and Financial Discipline

• Run a lightweight but rigorous delivery model across Data Systems and AI workstreams, including backlog management, MVP demos, milestone tracking, dependency management and risk escalation.
• Evaluate and manage vendors and pilots in the AI for CI space, including clarity on expected value, data requirements, procurement implications, integration feasibility and exit criteria.
• Manage the Data Systems and AI budget / project spend once approved, supporting timely forecasting, vendor decisions and efficient allocation of scarce resources.
• Help ECI leadership make transparent trade-offs across repository build, AI workflow development, vendor pilots, external data feeds and internal technical capacity.

Talent, Capability Building and Change Management

• Build ECI capability in data systems, AI literacy, prompt/skill development, responsible AI use and data governance fundamentals.
• Lead or coordinate a small expert pod/matrix team of data systems, AI, repository and workflow specialists as the book of work evolves; this may evolve into a small team of direct reports
• Develop onboarding, guidance, playbooks and training that help ECI associates adopt new ways of working and confidently use AI-enabled workflows against validated CI content.
• Support change-management communication so stakeholders understand what is changing, why it matters, and how to engage with the new repository and AI-enabled capabilities.

Results Orientation and Continuous Improvement

• Deliver measurable improvements in accessibility, reuse, speed, quality and scalability of ECI outputs.
• Use adoption analytics, user feedback, data quality indicators and workflow performance metrics to iterate rapidly and course-correct.
• Maintain a pragmatic “start now, evolve over time” mindset while preserving a clear target architecture for an AI-ready, enterprise CI capability.
• Ensure Data Systems and AI work continually strengthens ECI’s position as a trusted, proactive strategic partner to the enterprise.

Essential Requirements:

• Advanced degree or equivalent experience in life sciences, data science, computer science, engineering, healthcare analytics, business technology, or related field; PhD / MBA / MSc or equivalent experience desirable.
• ≥12yrs experience with demonstrated ability to bridge business and technical teams: able to understand CI needs, translate them into data / AI requirements, and work effectively with IT, data science, vendors and senior stakeholders.
• Strong understanding of data systems, knowledge management, metadata / taxonomy, data governance, search, APIs, RAG / knowledge-layer concepts, workflow automation and AI-enabled productivity tools.
• Experience delivering digital products or data platforms through MVPs, agile delivery, stakeholder feedback, adoption tracking and iterative improvement.
• Excellent judgement on data quality, responsible AI, information security, access management, compliance and vendor / partner management.
• Strong stakeholder-management skills, with ability to influence across a matrix and engage senior leaders while remaining pragmatic and delivery-oriented.
• Curiosity, energy and change leadership mindset; comfortable working in ambiguity and building new capabilities while maintaining business continuity.
• Excellent oral and written English communication skills; able to explain technical choices in simple, business-relevant language.
• People leadership or matrix leadership experience preferred, including coaching technical and non-technical colleagues through new ways of working