Associate Director, Data Systems & AI, Enterprise Competitive Intelligence

Job description

Job Description Summary #LI-Hybrid Location: Hyderabad, India About The Role: Enterprise Competitive Intelligence (ECI) is responsible for gathering, assessing, organizing, and delivering timely, relevant, high-quality competitive intelligence that supports Novartis pipeline, portfolio, and strategic decision-making. As ECI moves from a service-provider model toward a strategic partnership model, the function needs scalable data systems, governed repositories, and AI-enabled workflows that make validated CI accessible, reusable, and useful across business units. In this role you will shape and govern the ECI data systems and AI agenda: building and leading the CI knowledge layer, ensuring CI data is available, searchable, API-ready and usable by AI, and enabling repeatable AI-assisted workflows that improve speed, quality, consistency and enterprise leverage. The role will act as the bridge between ECI, business-unit insights teams, Data Digital & IT (DDIT), enterprise knowledge management teams (e.g. NKC), and relevant external partners to deliver pragmatic solutions while preparing ECI for future AI-enabled ways of working. Job Description 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 Skills Desired Cross-Functional Collaboration, Customer Engagement, Customer Insights, Data Analytics, Digital Marketing, Market Research, Media Campaigns, Product Marketing, Stakeholder Engagement, Stakeholder Management, Team Leadership, Waterfall Model