Senior Manager, Machine Learning Advanced Analytics

The Coca-Cola Company's Technology organization is in the midst of a digital transformation that allows our employees to use world class technology to connect our products to our customers all over the world. This journey is a very exciting time for Coca-Cola and our employees are big contributors to our Success and Growth. Our large scale and complex environment offers an incredible opportunity to address challenges, enable innovative solutions to make a difference for our customers.

In this position, you will embark on a journey of leveraging vast amounts of data to transform it into actionable insights. You will aid in the development of analytics models and work under the guidance of seasoned data science professionals to drive decision-making and strategy across the organization. This is an exciting opportunity to grow in your career in data science and analytics within a supportive and innovative environment.

** What*
* ** You'll*
* ** Do for Us:*
* + Model Deployment & Operationalization:
Partner with data science teams to transition machine learning models from experimentation to production environments, packaging models into robust Docker containers for scalable and reproducible deployments.

+ Pipeline Automation:
Build andmaintainautomated CI/CD pipelines for machine learning workflows (e.g., model training, evaluation, and deployment) utilizingtools like Git Hub Actions. Leverage Azure Container Registry to securely manage container images and deploy scalable workloads to Azure Kubernetes Service (AKS) or Azure Container Instances (ACS).

+ Utilize Azure Machine Learning and Microsoft Fabric Data Science to manage the ML lifecycle. Adapt prior experience from other cloud platforms to effectively navigate andoptimizeour current stack.

+ Monitoring & Maintenance:
Implement monitoring solutions to track model performance, data drift, and system health in production. Ensure comprehensive logging and observability for containerized model endpoints running on Kubernetes clusters. Troubleshoot and resolve operational issues as they arise.

+ Data Integration:
Collaborate with data engineering teams to ensure clean, reliable data pipelines (such as Medallion architectures) seamlessly feed into machine learning models.

+ Engineering Best Practices:
Write clean, modular, and testable code (primarily in Python) while adhering to version control best practices using Git.

+ Mentor, guide, and develop junior/aspiringMLOpsEngineeracross the organization.

+ Lead continuous career development and drive engineering excellence through performance reviews.

** Qualifications & Requirements:*
* + 6+years of professional experience (orequivalentstrong academic/internship experience) inMLOps, Data Engineering, Software Engineering, or a related field.

+ 3+ years of experience managing and scaling high-performingMLOpsor data platform teams, with a focus on career development, performance management, and technical mentorship.

+ Cloud ML Platforms:
Hands-on experience with at least one major cloud ML platform. While Azure ML and Microsoft Fabric are preferred, experience with AWS Sage Maker, GCP Vertex AI, or similar platforms is highly acceptable.

+ Programming:
Strongproficiencyin Python for scripting, automation, and model deployment.

+ Dev Ops & Containerization:
Familiarity with version control (Git), building CI/CD pipelines (e.g., Git Hub Actions, Azure Dev Ops), and containerization ecosystems (Docker, Azure Container Registry, Kubernetes/AKS/ACS).

+ Foundational Knowledge: A solid understanding of the machine learning lifecycle, containerizedmicroservicesarchitectures, and fundamental software engineering principles.

** Functional

Skills:

*
* + Practical experience with as many of the following as possible:

+…