Director, Supply Chain Lead Data Scientist

US - GA - Atlanta
Full-time
Posted Nov 10, 2026

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Skills

CI/CD
Large Language Models
Machine Learning
Python
SQL
Supply Chain
Wireframing
Communication
Data Analysis
Leadership
Logistics
Operations
Teamwork

Job description

Job Description Summary:

Job Summary

Supply Chain Digital Enablement applies data, analytics, and artificial intelligence to improve planning, service, cost, resilience, and execution across the North America Operating Unit. Our teams turn complex operational decisions into scalable digital capabilities that create measurable value for customers, bottlers, and the business.

Our work spans demand planning, inventory and replenishment, transportation, manufacturing, customer service, and network decisions. Strong data foundations, rigorous experimentation, optimization, and responsible AI are essential to building solutions that teams trust and use.

About the Role

As a Data Scientist in Supply Chain Digital Enablement, you will build analytical products that improve how supply-chain teams make decisions. You will partner with Supply Chain, Operations, Product, Engineering, Data, Commercial, and Finance colleagues to translate ambiguous business problems into scalable models, optimization tools, simulations, and AI-enabled decision support.

This role combines statistical analysis, predictive modeling, optimization, simulation, and modern AI techniques. You will lead the technical direction for complex, high-value solutions and establish reusable architecture, MLOps, evaluation, and governance practices that raise capability across the team. We are looking for someone with intellectual curiosity, technical rigor, practical supply-chain judgment, and a collaborative approach to delivery.

What You Will Do

•Develop, evaluate, and productionize predictive, prescriptive, optimization, simulation, and NLP models for supply-chain decisions

•Translate planning, inventory, transportation, manufacturing, network, and service problems into clear analytical requirements, model approaches, and measurable outcomes

•Design ML architectures and rapid prototypes; partner with Engineering and Data teams to scale successful solutions into reliable products

•Apply optimization and simulation methods, evaluating algorithmic complexity, performance, scalability, and trade-offs

•Apply statistical analysis, forecasting, causal methods, and experimental design to evaluate interventions and quantify business impact

•Partner with data engineering to ensure trustworthy data architecture, scalable pipelines, robust querying, instrumentation, and data-quality foundations

•Build model lifecycle practices including reproducibility, versioning, testing, deployment, monitoring, drift detection, retraining, and documentation

•Evaluate and apply deep learning, large language models, NLP, and agentic systems where they create measurable value with appropriate controls and human oversight

•Apply AI governance, privacy, security, fairness, explainability, and responsible-AI principles throughout development and deployment

•Communicate technical choices, assumptions, insights, limitations, and recommendations clearly to technical and nontechnical stakeholders

•Contribute to cross-functional software lifecycle management from discovery and prototyping through deployment, adoption, support, and continuous improvement

•Define technical strategy, reference architectures, and modeling standards for priority supply-chain data-science products

•Mentor data scientists and review technical designs, model quality, deployment readiness, and responsible-AI controls

What You Will Need

Key Qualifications

•7 years applying data science, machine learning, optimization, forecasting, or advanced analytics in a business, digital, operations, or supply-chain environment

•Demonstrated technical leadership delivering complex data-science products from problem definition through production adoption and measurable business impact

•Advanced Python and SQL skills and experience designing scalable data, model, and ML architecture patterns

•Deep expertise in several of predictive modeling, optimization, simulation, statistical inference, experimental design, NLP, deep learning, or agentic systems

•Experience designing MLOps practices including CI/CD, model registries, testing, monitoring, drift detection, retraining, and incident response

•Ability to evaluate algorithmic complexity, trade-offs, scalability, explainability, and operating cost when selecting solution approaches

•Experience establishing responsible-AI and model-governance practices, including documentation, review, risk controls, and human-in-the-loop decision design

•Proven ability to influence senior stakeholders, guide cross-functional delivery, and mentor other data scientists

Preferred Qualifications

•Significant supply-chain experience in demand planning, inventory, replenishment, manufacturing, logistics, network design, route optimization, or service operations

•Experience leading enterprise-scale optimization, simulation, ML-platform, or AI-product initiatives

•Experience setting technical standards across Data Science, Data Engineering, Product, and Software Engineering teams

•Experience assessing and deploying generative-AI, NLP, deep-learning, or agentic solutions in governed production environments

Skills

•Model development and optimization: Builds reliable predictive, prescriptive, optimization, and simulation models that improve supply-chain decisions

•ML architecture and lifecycle management: Designs scalable prototypes and production-ready patterns; applies testing, deployment, monitoring, and continuous improvement practices

•Statistical rigor: Applies sound statistical methods, experimental design, forecasting, and causal reasoning to quantify impact and reduce uncertainty

•Data architecture and querying: Works effectively with complex data, SQL, pipelines, and data-quality practices to create trustworthy analytical foundations

•AI and responsible innovation: Applies deep learning, NLP, LLMs, and agentic systems thoughtfully, with governance, ethics, explainability, and human oversight

•Business partnership: Connects technical work to supply-chain outcomes and communicates clear, actionable recommendations to cross-functional stakeholders

•Technical leadership: Sets direction, raises technical standards, mentors others, and aligns cross-functional teams around scalable solutions

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:

Apply an Enterprise Mindset, Apply an Enterprise Mindset, Build Inclusion, Business Process Modeling, Change Management, Cloud Platform, Communication, Data Analysis, Data Strategies, Design Thinking (Innovation Solutions), Develop Talent, DevOps, Digital Collaboration, Digital Transformation, Emerging Technology Trends and Practice, Enterprise Architecture Framework, Envision the Future, Experimentation, Foster Innovation, Generative AI, Ideation, Influence Across the Network, Long Term Planning, Make Courageous Choices, Metric Creation {+ 12 more}

Pay Range:

United States: 164,000 - 193,000 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:

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:

Yes

Job Posting End Date:

November 27, 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 thrive after 130 years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.