Global Data Scientist: ML, AI & Impact Architect

The Coca-Cola Company; Dublin; On-site; EUR 70,000 - 110,000; Full time

Job summary

The Coca-Cola Company’s Technology organization is seeking a Data Scientist in Dublin to transform vast data into actionable insights. You will develop analytics models and work with seasoned professionals to drive decision-making across Marketing, Finance, Technical Innovation and Supply Chain.

You will collaborate with cross-functional teams, translate requirements into data-driven solutions, apply ML and statistical methods, and communicate findings to both technical and non-technical

Qualifications

• Bachelor’s or Master’s degree in a quantitative field; Ph.D. preferred.
• Experience gathering, interpreting and translating business requirements.
• Proficient with Python, R, SQL.
• Experience with large-scale data wrangling in relational databases or Spark.
• Experience applying statistical modeling techniques and ML.

Responsibilities

• Collaborate with cross-functional teams to understand business objectives.
• Translate requirements into data and develop ML/AI algorithms.
• Leverage large structured/unstructured data for modeling.
• Visualize data and create reports and insights.
• Communicate analytical results to stakeholders.
• Partner with ML OPS to scale AI use-cases.
• Maintain technical documentation.
• Build and maintain a library of data science solutions and templates.
• Leverage CI/CD to automate deployments.
• Keep abreast of industry trends.

Skills

Python

R

SQL

Data wrangling

Experimentation

Communication

Education

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

Tools

GitHub

GitLab

Azure DevOps

Job description

The Coca-Cola Company’s Technology organization is seeking a Data Scientist in Dublin to transform vast data into actionable insights. You will develop analytics models and work with seasoned professionals to drive decision-making across Marketing, Finance, Technical Innovation and Supply Chain.

You will collaborate with cross-functional teams, translate requirements into data-driven solutions, apply ML and statistical methods, and communicate findings to both technical and non-technical