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