The Coca-Cola Company

Supply Chain

• Own regional end-to-end supply chain performance outcomes, including availability, cost, inventory, productivity and resilience
• Establish a “best-looks-like” end-to-end operating model across bottlers
• Drive fact-based discussions on service, cost, inventory and risk trade-offs
• Govern the effectiveness and maturity of S&OP / IBP processes across bottlers
• Integrate demand, supply, inventory and value chain perspectives into decision making
• Elevate planning quality through scenario-based and unconstrained thinking
• Lead system-level discussions on network design, cross-border flows, capacity allocation and sourcing footprint
• Challenge and validate bottler proposals using end-to-end system economics and risk scenarios
• Align supply chain structures with growth, innovation and route-to-market strategies
• Build and govern a regional supply chain value pipeline covering COGS, logistics, working capital and complexity
• Ensure benefits are quantified, finance-validated and transparently tracked
• Convert operational improvement into tangible business value
• Enforce standard KPI definitions and objective performance measurement
• Replace narrative reporting with data-driven scorecards and benchmarks
• Enable leadership to see performance gaps versus peers and best-in-class
• Ensure robust business continuity and risk mitigation frameworks across bottlers
• Stress-test assumptions and challenge perceived readiness
• Identify continuous improvement opportunities throughout the end-to-end supply chain and take necessary actions
• Operate through influence, governance and transparency rather than direct line authority
• Partner with bottler COEs, Supply Chain leaders, TISC leaders, Franchise Tech Ops, Commercial and Finance
• Act as a trusted advisor and challenger, escalating issues when required

Director, Supply Chain – Caucasus and Central Asia

Machine Learning Engineer

• Partner with data science teams to transition machine learning models from experimentation into production
• Package models into robust Docker containers for scalable and reproducible deployments
• Build and maintain automated CI/CD pipelines for ML workflows
• Manage container images with Azure Container Registry
• Deploy scalable workloads to Azure Kubernetes Service or Azure Container Instances
• Use Azure Machine Learning and Microsoft Fabric Data Science to manage the ML lifecycle
• Implement monitoring for model performance, data drift, and system health
• Ensure logging and observability for containerized model endpoints on Kubernetes
• Troubleshoot and resolve operational issues
• Collaborate with data engineering teams on reliable data pipelines and Medallion architectures
• Write clean, modular, testable Python code using Git best practices
• Mentor, guide, and develop junior/aspiring MLOps engineers
• Lead career development and engineering excellence through performance reviews

Senior Manager, Machine Learning, Advanced Analytics

Manager

• Serve as the primary point of contact between CPS NA and the North America Operating Unit for commercialization projects
• Work with internal and external stakeholders in cross-functional teams to execute commercialization initiatives and meet concentrate availability dates
• Identify and implement solutions to optimize commercialization processes, reduce timelines, and improve speed to market
• Identify and implement continuous improvement opportunities
• Report monthly Product and Portfolio Team metrics
• Represent CPS NA in NAOU lead project meetings and coordinate activities within CPS NA
• Lead decision-making and problem-solving for CPS NA
• Coordinate and monitor commercialization for new products, reformulations, and innovation projects within CPS NA plants
• Develop and track project timelines and contribute to supply model design and execution
• Provide COGS estimates and lead times for prototype cost assessments and launch financial commitments
• Collaborate to mitigate financial exposure from reformulations or project-plan changes
• Propose risk-management and issue-mitigation solutions affecting project timelines
• Lead plant-based Commercialization Meetings and review active projects with cross-functional teams
• Support collection of documentation for new products and reformulations, including SDS, PARs, and country-specific registrations
• Record, maintain, and share project status and metrics
• Maintain, review, and update Quality System documentation; represent the Product & Portfolio Team in internal and external system audits
• Develop and implement corrective actions for audit opportunities or findings as needed
• Initiate and monitor certifications for new products, including Kosher, Non-GMO, and Organic

Director, Supply Chain – Caucasus and Central Asia

Senior Manager, Machine Learning, Advanced Analytics

Product and Portfolio Manager