Associate Director Cloud Engineer (Data & ML)

• Act as the primary connection between Finance end to end Solution delivery and the Digital business for a given capability
• Analyse and refine requirements for delivering the necessary technology service to business
• Solution Design of Finance system solutions in alignment with design and architectural standards, while meeting quality, performance, security and business requirements. Manage project teams in different locations.
• Own and Drive the end to end Technical Product Delivery.
• Ensure IT services are aligned with business strategy, business objectives, business requirements, standards and regulations
• Understand the processes, plans, objectives, drivers, and issues related to the business capability, as well as the appropriate external policies and regulations.
• Identify and qualify new demand with overall responsibility for steering proposals through the early phases of approval
• Significantly contribute and drive multi-year, large-scale product roadmaps for key finance processes on global & headquarter core applications with significant impact on Novartis financial results
• Provide IT consulting to the capability stakeholders, educate them on IT processes and methodologies and drive adoption of global solutions
• Ensure digital technology impact analysis is provided to business
• Initiate and drive projects from conception to completion with minimal guidance.
• Establishes scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.
• Communicate complex data or algorithms into simple conclusions that will empower leadership to drive action based on the insights you derive.
• Collaborate in multi-functional teams to evaluate business activities, and then develop innovative and effective approaches to tackle team’s analytics problems and communicate results and methodologies.
• Develop and maintain infrastructure for machine learning models in production. Monitor and optimize machine learning models for scalability, performance, and reliability
• Implement and manage continuous integration and delivery pipelines for machine learning products. Collaborate with data scientists and ML engineers to ensure smooth integration of models to production environment.

• University working and thinking level, degree in technical computer science or information security area or comparable education/experience
• 8+ years of relevant professional IT experience in the related functional area.
• Experience of running ML solutions in production, making the data accessible and maintaining a Data Science platform (cloud based on AWS, Azure, GCP)
• You are a problem solving, hands-on and analytical personality, a strong team player with solution orientated and initiative-taking mindset.
• Demonstrated ability in the establishing of MLOps practice and associated practices in complex environments.
• Experience in coaching and leading agile dev teams, and building a team and culture around agile development practices.
• Deep, hands-on technical and operational expertise with IT technologies like: Snowflake, DB design / modelling, Databricks Airflow or equivalent data orchestration tool Bitbucket or equivalent source code repository tool AWS foundational .
• Demonstrated experience AI scalable solutions industrialization, establishing the vision for major products.
• Experience interacting and influencing cross-functional stakeholders to execute high-impact projects.
• Experience developing CI/CD pipelines for AI/ML development, deploying models to production, and managing the lifecycle in a regulated environment.
• Hands-on experience in development, deployment and agile life cycle management of data science apps (MLOps).
• Deep, hands-on technical and operational expertise with Cloud Services like: Jenkins, CloudWatch, SonarQube, Lambda, AWS Batch, Sagemaker, S3, Docker, EKS / ECS, EC2, ECR, Fargate, API Gateway, VPC, Cognito.
• Pluses:

• Linux / Unix base system administration.
• System Architecture.
• Solid knowledge of data science fundamentals like models, methods and workflows

Novartis