Why This Role Stands Out
This hybrid role at The Coca-Cola Company offers an exceptional opportunity to drive impactful machine learning initiatives within a global digital transformation, allowing you to grow your skills in advanced analytics and model operationalization. You'll thrive here if you're passionate about leveraging vast datasets to create actionable insights and contributing to innovative solutions within a supportive, forward-thinking technology organization. Apply to join a world-class team and make a significant difference in how iconic products connect with customers worldwide.
Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Atlanta, GA, United States
Posted
Yesterday
DockerAWSMachine LearningAgileAzureGitGitHub ActionsGoogle CloudKubernetesPython
Job Description
The Coca-Cola Company's Technology organization isin the midst ofa digital transformation that allows our employees to useworld classtechnology to connect our products to our customers all over the world. This journey is a very exciting time forCoca-Colaand our employees are big contributors to our Success and Growth. Our large scale and complex environmentoffersan incredible opportunity to address challenges, enable innovative solutions to make a difference for our customers.
In this position, you will embark on a journey ofleveragingvast 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 strategyacross the organization. This is an exciting opportunity togrow inyour career in data science and analytics within a supportive and innovative environment.
WhatYou'llDo for Us:
Qualifications& Requirements:
Functional Skills:
What We Can Do for You:
In this position, you will embark on a journey ofleveragingvast 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 strategyacross the organization. This is an exciting opportunity togrow inyour career in data science and analytics within a supportive and innovative environment.
WhatYou'llDo 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 GitHub 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 SageMaker, Google Cloud Platform Vertex AI, or similar platforms is highly acceptable.
- Programming: Strongproficiencyin Python for scripting, automation, and model deployment.
- DevOps & Containerization: Familiarity with version control (Git), building CI/CD pipelines (e.g., GitHub Actions, Azure DevOps), 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:
- Handles multiple competing priorities in a fast-paced, deadline-driven environment
- Strong attention to details and excellent problem-solving skills
- Ability to work in a collaborative team environment
- Highly innovative, adaptable, and self-directed
- Results-oriented with a delivery focus
- Presentation skills: Ability to communicate technical topics to businessaudience.
- Be able to collaborate across other levels of the organization
- Team player who can lead a discussion to defined outcomes
- Effective Communication
- Pursuing Innovation
What We Can Do for You:
- Innovation & Technology:The ability to work with an award-winning team that is on thecutting edgeof innovation.
- Exposure to World Class Leaders:Availability to global technology leaders that will expand your network and exposure you toemergingtechnologies and techniques.
- Agile Work Environment:We embrace agile with management that believes in removing barriers, so you are empowered to experiment,iterateand innovate.
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