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 proto@types; 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 proto@types 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
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.
