Global Data Scientist: AI, Analytics & Strategy

The Coca-Cola Company; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

The Coca-Cola Company’s Technology organization is driving a data science transformation, turning vast data into actionable insights to inform decision making across Marketing, Finance, Technical Innovation and Supply Chain globally.

You will collaborate with cross-functional teams, translate business needs into ML/AI solutions, and build scalable data science libraries while maintaining documentation and supporting production deployments.

Qualifications

• Bachelor’s or Master’s degree in quantitative field; PhD preferred.
• Full English proficiency.
• Experience gathering, interpreting and translating business requirements.
• Experience with large-scale data wrangling with relational databases and/or Spark.

Responsibilities

• Collaborate with cross-functional teams to understand business requirements and objectives.
• Translate business requirements by incorporating data and develop ML and AI algorithms to produce actionable insights.
• Leverage diverse data to derive insights for modeling and decision making.
• Visualize and interpret data and create reports for stakeholders.
• Communicate complex analytical work to technical and non‑technical audiences.
• Partner with ML Ops to scale and operationalize ML/AI use‑cases.
• Maintain technical documentation and build reusable data science templates.
• Automate deployments using CI/CD.

Skills

Python

R

SQL

Spark

Data visualization

Communication

Education

Bachelor’s or Master’s degree in a quantitative field

Ph.D. preferred

Tools

GitHub

GitLab

Azure DevOps

Job description

The Coca-Cola Company’s Technology organization is driving a data science transformation, turning vast data into actionable insights to inform decision making across Marketing, Finance, Technical Innovation and Supply Chain globally.

You will collaborate with cross-functional teams, translate business needs into ML/AI solutions, and build scalable data science libraries while maintaining documentation and supporting production deployments.