Associate Director – Solution Delivery Analytics Products

What matters most

• Enterprise-Scale AI and Analytics Product Lifecycle

  The role requires end-to-end ownership of data and AI products, from initial concept and rapid prototyping to full-scale enterprise adoption. You must demonstrate the ability to manage the entire product roadmap while ensuring these solutions are governed, secure, and compliant.

• Cross-Functional Leadership in Matrix Organizations

  You will lead global, diverse teams including engineers, architects, data scientists, and product managers. Success depends on your ability to align business, technology, and executive stakeholders around complex investment strategies and technology roadmaps.

• Platform Modernization and Engineering Standards

  You are expected to drive the modernization of platforms using cloud-native architectures, APIs, and intelligent automation. This involves establishing rigorous engineering standards and reusable frameworks to ensure operational excellence and scalability across the organization.

• Responsible Generative AI Scaling

  A critical priority is the transition of emerging AI and Generative AI solutions into production-ready, enterprise-grade products. This requires a focus on MLOps, lifecycle management, and ensuring that all deployed AI solutions meet strict corporate governance and ethical standards.

Likely challenges to think through

• Balancing Innovation with Regulatory Compliance

  In the pharmaceutical and life sciences sector, the need for rapid prototyping often conflicts with stringent security and compliance requirements. Prepare to discuss how you have successfully navigated these constraints without stifling innovation or compromising data integrity.

• Driving Adoption in a Global Matrix Environment

  Scaling products across global markets involves managing diverse stakeholder needs and varying regional requirements. You may face resistance to new processes or technologies; prepare to share strategies for building consensus and demonstrating measurable value to win over skeptical business units.

• Managing Technical Debt During Modernization

  Modernizing legacy platforms while simultaneously delivering new AI capabilities creates significant technical complexity. Be ready to explain your approach to prioritizing modernization efforts and managing the trade-offs between maintaining existing systems and implementing new, cloud-native architectures.

Topics to prepare

• Enterprise AI Governance and Ethics

  The role requires scaling Generative AI into responsible, enterprise-grade products. Exercise: Draft a framework for evaluating AI model risks, including data privacy, bias mitigation, and compliance, specifically for a regulated life sciences environment.

• Cloud-Native Analytics Architecture

  Modernizing platforms using cloud-native architectures and semantic layers is a core responsibility. Exercise: Map out a high-level architecture for a self-service data product that integrates Snowflake or Databricks with existing enterprise APIs, focusing on scalability and security.

Skills to revise

• MLOps and Lifecycle Management

  You must drive operational excellence through automation and monitoring. Exercise: Design a CI/CD pipeline for an AI model that includes automated testing, performance monitoring, and a rollback strategy for production deployments.

• Strategic Product Roadmapping

  The role involves developing roadmaps from concept to business process change. Exercise: Create a 12-month product roadmap for a new analytics solution, identifying key milestones for MVP launch, stakeholder feedback loops, and enterprise-wide adoption metrics.

• Cross-Functional Stakeholder Management

  Aligning diverse stakeholders around technology investments is essential. Exercise: Prepare a communication plan for a complex, multi-year digital transformation project, detailing how you would manage expectations between technical teams and non-technical business executives.

• Prepare an enterprise-scale product case study

  Develop a structured walkthrough of a past project where you led an AI or data product from concept to production. Focus on your methodology for scaling, managing cross-functional teams (engineers, data scientists, product managers), and achieving measurable business value.

• Map your technical expertise to the Novartis stack

  Be prepared to discuss your hands-on experience with cloud-native architectures and specific platforms mentioned in the job description, such as AWS, Databricks, Snowflake, or Collibra. Highlight how you have used these tools to modernize data platforms or implement MLOps.

• Formulate a strategy for Generative AI governance

  Prepare to explain your approach to scaling Generative AI solutions responsibly. Focus on how you balance innovation with enterprise-grade governance, security, and compliance requirements in a highly regulated environment like healthcare or life sciences.

• Draft questions regarding stakeholder alignment

  Prepare thoughtful questions about how Novartis manages the intersection of business, technology, and executive stakeholders. Ask about their current challenges in roadmap execution or how they prioritize investments in data and analytics products.

• Refine your leadership and mentorship narrative

  Prepare specific examples of how you have built and mentored high-performing, cross-functional teams within a matrix organization. Emphasize your ability to drive operational excellence and foster a culture of collaboration.