Summary

Associate Director of DDIT US&I Analytics Operations is responsible is required to partner with with Business Stakeholders and DDIT Strategic Business Partners for demand analysis, solution proposal/evaluation and project delivery -Drive operations of systems and applications in scope (both Global and Local), ensuring their stability and integrity and meeting customer service levels and also oversee AI Ops, ML Ops, and LLM Ops, ensuring the efficient and scalable operation of AI and machine learning models. This role involves managing the entire model lifecycle, from development to deployment, monitoring, and maintenance, while adhering to predefined Service Level Agreements (SLAs). The position emphasizes innovation, rapid prototyping, and enterprise-scale deployment of analytics services.

• Oversee AI Ops, ML Ops, and LLM Ops to ensure efficient and scalable operations of AI and machine learning models.
• Manage the entire model lifecycle, including development, deployment, monitoring, and maintenance.
• Ensure adherence to predefined Service Level Agreements (SLAs) for AI and ML operations.
• Develop and maintain CI/CD Ops pipelines for seamless integration and deployment of models.
• Implement and manage model registries for version control and governance.
• Establish and enforce coding checklists and best practices.
• Develop and automate testing frameworks to ensure model quality and reliability.
• Design and manage inference pipelines for real-time and batch predictions.
• Drive innovation by adopting emerging technologies (GenAI, AI, NLP) and accelerating product/service development through rapid prototyping and iterative methods.
• Align analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements.
• Identify and develop advanced analytics capabilities and ecosystem partnerships in alignment with DnA strategy.

• Lead AI Ops, ML Ops, and LLM Ops to ensure the efficient and scalable operation of AI and machine learning models.
• Develop and manage the model lifecycle, including development, deployment, monitoring, and maintenance.
• Ensure adherence to predefined SLAs for AI and ML operations.
• Create and manage analytics product/services roadmaps from concept to launch.
• Develop and maintain CI/CD Ops pipelines for seamless integration and deployment of models.
• Implement and manage model registries for version control and governance.
• Establish and enforce coding checklists and best practices.
• Develop and automate testing frameworks to ensure model quality and reliability.
• Design and manage inference pipelines for real-time and batch predictions.
• Incubate and adopt emerging technologies (GenAI, AI, NLP) to accelerate product/service development through rapid prototyping and iterative methods.
• Align analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements.
• Establish and update strategies, implementation plans, and value cases for emerging technologies.
• Drive innovation using appropriate people, processes, partners, and tools.
• Identify and develop advanced analytics capabilities and ecosystem partnerships in alignment with DnA strategy.
• Oversee end-to-end delivery of analytics services and products across cross-functional business areas.
• Serve as the point of escalation, review, and approval for key issues and decisions.
• Manage resource and capacity planning in line with business priorities and strategies.
• Foster continuous improvement within the team.
• Decide on program timelines, governance, and deployment strategies.

• Achieved targets in Enterprise business case contribution, KPIs, customer satisfaction, and innovation measures
• Delivery on agreed KPIs including business impact -Launch of innovative technology solutions across Novartis at scale
• Business impact and value generated from DDIT solutions -Adoption and development of Agile Productization and DevOps practices -Operations stability and effective risk management -Feedback on customer experience -Applications adherence to ISC requirements and are audit ready.
• Business capability, vision & strategy clearly defined, communicated, and executed, well aligned to business strategy and Enterprise IT strategy, and providing a competitive advantage to Novartis
• Role model with the highest standards of professional conduct in leading the business capability area in line with the new IT operating model
• Deployment of digital platforms and services at scale to deliver the digital strategy

• Demonstrated experience in Budget Management. Business Acumen. Performance Management. Planning. Project Management, , Risk Management. Service Delivery Management and stakeholder management
• Strong understanding of AI Ops, ML Ops, and LLM Ops.
• Experience in developing and managing the model lifecycle, including deployment and maintenance.
• Proficiency in managing operations with predefined SLAs.
• Expertise in CI/CD Ops pipelines development.
• Experience with model registry and management.
• Knowledge of coding checklists and best practices.
• Proficiency in developing and automating testing frameworks.
• Experience in designing and managing inference pipelines.
• Production experience with commercial and open-source ML platforms.
• Strong knowledge of AWS, Databricks, and Snowflake service offerings.
• Ability to collaborate with business teams to gather requirements, groom product backlogs, and drive delivery.
• Agile delivery experience managing multiple concurrent delivery cycles.
• Solid foundation in CRISP analytical life cycle management.
• Strong leadership skills with the ability to build high-performing teams.
• Excellent vendor management and IT governance skills.
• Innovative and analytical mindset with a focus on continuous improvement. Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, Building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.

• Strong understanding of descriptive vs. prescriptive Analytical frameworks.
• Strong knowledge of visualization platforms and project life cycle management, including Power BI, Qlik, and MicroStrategy.
• Significant production experience addressing visualization platform and data pipeline performance constraints.
• Strong analytical and problem-solving skills, effective communication, and the ability to influence and collaborate with cross-functional teams.

• English.