About the role

Bringing life-changing medicines to millions of people, Novartis sits at the intersection of cutting-edge medical science and innovative digital technology. As a global company, the resources and opportunities for growth and development are plentiful including global and local cross functional careers, a diverse learning suite of thousands of programs & an in-house marketplace for rotations & project work. With strong medicines pipeline our current transformation will not just deliver growth for our business but continue to allow us to bring innovative medicines to patients quickly.

Come to work each day with an inclusive and collaborative US&I business-facing team. As an Associate Director ML Ops – DSAI Products in the DDIT US&I - Data, Analytics, and Insights team, you’ll have opportunities to contribute to the modernization of the data ecosystem, deliver DnA (Data and Analytics) products to the countries and the core brand teams to deliver exceptional customer experience, reach twice as many patients twice as fast to help them prevail over severe diseases by leveraging data-driven insights.

Purpose and Focus Areas

• As a key leader within the Advanced Analytics Products, you will enable the rollout of Analytics and AI Products for US and international markets, working in conjunction with the DnA (Data and Analytics) products team.
• Work across suite of the Advanced Analytics Products to create advanced analytics product/services roadmaps from concept to development to launch, encompassing technology adoption, product engineering, service design, security and compliance, and business process change in partnership with the internal and external partners.
• Incubate and adopt emerging (GenAI, AI) technologies and launch products/services faster with rapid prototyping & iterative methods to prove and establish value. For identified technologies, launch to enterprise scale, ensuring value is derived.
• Partner with IT Architecture to incubate, adopt emerging DnA technologies and launch products/services faster with rapid prototyping & iterative methods to prove and establish value. For identified technologies, launch to enterprise scale, ensuring value is derived.
• Focus and align DnA innovation efforts with the Business strategy, IT strategy, and legal/regulatory requirements. Establish and update developed innovation strategies, implementation plans, and value cases to implement emerging technologies.

Accountabilities

• 1. Model Deployment: Collaborate with data scientists to deploy machine learning models into production environments. Implement deployment strategies such as A/B testing or canary releases to ensure safe and controlled rollouts.

• 2. Infrastructure Management: Design and manage the infrastructure required for hosting ML models, including cloud resources and on-premises servers.Utilize containerization technologies like Docker to package models and dependencies.

• 3. Continuous Integration/Continuous Deployment (CI/CD): Develop and maintain CI/CD pipelines for automating the testing, integration, and deployment of ML models.Implement version control to track changes in both code and model artifacts.

• 4. Monitoring and Logging: Establish monitoring solutions to track the performance and health of deployed models.Set up logging mechanisms to capture relevant information for debugging and auditing purposes.

• 5. Scalability and Resource Optimization: Optimize ML infrastructure for scalability and cost-effectiveness.

Implement auto-scaling mechanisms to handle varying workloads efficiently.

• 6. Security and Compliance: Enforce security best practices to safeguard both the models and the data they process.Ensure compliance with industry regulations and data protection standards.

• 7. Data Management: Oversee the management of data pipelines and data storage systems required for model training and inference. Implement data versioning and lineage tracking to maintain data integrity.

• 8. Collaboration with Cross-Functional Teams: Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints. Collaborate with DevOps teams to align MLOps practices with broader organizational goals.

• 9. Performance Optimization: Continuously optimize and fine-tune ML models for better performance.

Identify and address bottlenecks in the system to enhance overall efficiency. 10. Documentation:

• 10. Documentation: Maintain comprehensive documentation for deployment processes, configurations, and system architecture. Communicate effectively with non-technical stakeholders, providing insights into the performance and impact of ML models.

Skills and Knowledge

• Solid understanding of analytical and technical frameworks for descriptive and prescriptive analytics
• Good familiarity with AWS, Databricks, and Snowflake service offerings. Abreast of emerging technology within AI/ML space
• Strong collaborative interactions with customer-facing business teams.
• Track record delivering global solutions at scale.
• Ability to work and lead (a cross-functional team) in a matrix environment.
• Product-centric approach to defining solutions. Collaborate with business in gathering requirements, grooming product backlogs, driving delivery, and ongoing data product enhancements.
• Agile delivery experience managing multiple concurrent delivery cycles with sound foundation in Analytical Data life cycle management.
• Soft Skills - Consulting, Influencing & persuading, Unbossed Leadership, IT Governance, Building High Performing Teams, Vendor Management, Innovative & Analytical Technologies

Experience

• University Degree and/or 10 years relevant experience and professional qualifications