Machine Learning Engineer II

Job Description Summary: The Senior Manager, Software Engineer, Data Platform & Segmentation is a individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model. This role functions as a hands-on technical engineer with 3 to 6 years of experience on data and machine learning engineering. The role emphasizes deep technical expertise, product partnership, and architecture, rather than people management. Core Accountabilities Product Model & Discovery Partnership

• Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets.
• Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound.
• Translate business and customer questions into durable data models and segmentation frameworks. Data Platform & Segmentation Architecture
• Analyze and integrate structured and unstructured data from enterprise platforms, customers, and external data providers.
• Build scalable data preparation and feature engineering pipelines for ML applications. Machine Learning
• Develop predictive and recommendation models using appropriate statistical and machine learning techniques.
• Evaluate and select appropriate approaches based on each use case, including: Classification and regression Ranking and recommendation Clustering and segmentation Time-series and forecasting Gradient-boosting and tree-based models Deep learning and transformers Computer vision Embeddings, vector search and RAG
• Build reliable training and inference pipelines for batch and near-real-time use cases.
• Develop APIs and services that expose model predictions to web, mobile, CRM, Salesforce, and other enterprise applications.
• Establish rigorous model evaluation, testing and validation practices. Engineering Execution & Data Quality
• Build and maintain high-quality, production-grade data pipelines and services.
• Ensure strong standards for data quality, lineage, observability, and reliability.
• Implement MLOps pipelines covering training, testing, versioning, deployment and model lifecycle management.
• Monitor production models for model performance, data quality, drift and other operational issues.
• Implement appropriate retraining, rollback and model versioning strategies.
• Troubleshoot issues across data pipelines, models, inference services, APIs, and production environments.
• Create reusable ML components and patterns that can support multiple Transaction Growth use cases.
• Participate in architecture reviews, code reviews and engineering design discussions. Microsoft Azure Data Platform & Fabric Expertise
• Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains.
• Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers.
• Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns.
• Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls.
• Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy.
• Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty. Business Partnership & Communication
• Serve as a trusted technical partner to Customer and Commercial stakeholders.
• Communicate segmentation concepts, assumptions, and limitations in clear business language.
• Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions.
• Support external partner and vendor conversations as a technical authority when needed. Governance, Privacy & Compliance
• Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements.
• Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later.
• Advocate for responsible and ethical use of customer and commercial data. Success Measures
• Segmentation capabilities measurably improve customer engagement and commercial outcomes.
• Reduced duplication and inconsistency in segmentation logic across products.
• Improved data quality, freshness, and trustworthiness.
• Faster time-to-insight and activation for product teams.
• Platforms and models that scale with growth while controlling cost and risk. Required Experience & Capabilities
• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 3+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
• Strong programming experience with Python or common data and ML libraries.
• Experience developing production machine learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.
• Strong understanding of supervised and unsupervised learning, model selection, feature engineering and statistical modeling.
• Experience preparing large datasets for machine learning, including cleansing, transformation, feature generation and quality validation.
• Strong SQL skills and experience working with large enterprise datasets.
• Experience designing training, evaluation and inference pipelines.
• Experience deploying machine learning models into production environments.
• Practical understanding of MLOps, including experiment tracking, model versioning, CI/CD, automated testing, deployment and monitoring.
• Experience developing or integrating APIs and services used for model inference.
• Strong software engineering practices including modular design, source control, code review, automated testing and production debugging.
• Experience working with cloud-based data and ML platforms.
• Ability to assess multiple modeling approaches and select the simplest solution capable of meeting the business objective.
• Strong communication skills and the ability to collaborate with product managers, business stakeholders, software engineers and data teams. The Coca-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1-B visa status and other employment-based nonimmigrant visas) for this position. Accordingly, all applicants must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company's sponsorship to continue to work legally in the United States. Skills: Pay Range: United States: 152,000
• 178,300 USD
 Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered. Annual Incentive Reference Value Percentage: 15 Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target. Location(s): United States of America City/Cities: Atlanta Travel Required: 00%
• 25% Relocation Provided: No Job Posting End Date: October 14, 2026 Our Purpose and Growth Culture: We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thr