Job description

## Qualifications - Master’s degree in a quantitative field, such as Data Science, Statistics, Economics, Finance, Mathematics, Operations Research or other quantitative discipline - Experience gathering, interpreting and translating business requirements - Proficient experience with analytical and programming languages and packages, such as Python, R, and SQL - Able to understand various data structures and common methods in data transformation - Demonstrated experience in large-scale data wrangling with relational databases and/or Spark, PySpark. 5+ years’ experience applying a range of statistical and modeling techniques including hypothesis testing, dimensionality reduction, supervised learning (classification and regression), forecasting, and unsupervised clustering and putting solutions into production - Strong aptitude for learning and applying new technologies related to Data Science and Data Management - Demonstrated ability to communicate complex analytical concepts and results at multiple levels to both technical and non-technical audiences - Experience with code version control platforms like GitHub, GitLab or Azure DevOps - Handles multiple competing priorities in a fast-paced, deadline-driven environment - Strong attention to details and excellent problem-solving skills - Ability to work in a collaborative team environment - Highly innovative, adaptable, and self-directed - Results-oriented with a delivery focus - Presentation skills: Ability to communicate technical topics to business audience - Be able to collaborate across other levels of the organization - Team player who can lead a discussion to defined outcomes - Effective Communication - Data Science; Data Warehousing (DW); Structured Query Language (SQL); Data Systems; Data Modeling; Agile Methodology; Business Intelligence (BI); Machine Learning Techniques; Data Analytics; Data Engineering; Data Processing; Artificial Intelligence (AI); Machine Learning; SQL Databases ## 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 predict outcomes for various functional areas and use-cases, including Finance, Marketing, Supply Chain (among others) across the Globe - Leverage a diverse set of large structured and unstructured data to derive meaningful insights and information sets for modeling - Develop, implement, and optimize ML and AI algorithms to predict outcomes and derive insights from large structured and unstructured datasets - Visualize and interpret data and create reports and actionable insights - Communicate complex analytical work to a variety of technical and non-technical stakeholders, including executive management - Partner with ML OPS and other cross-functional teams to scale and operationalize ML and AI use-cases - Maintain technical documentation in accordance with the agreed standards - Build and maintain a robust library of data science solutions, reusable templates, algorithms and supporting code - Leverage CI and CD principles to automate and improve repeatability of deployments - Keep abreast of industry trends and developments in data science and analytics ## Full Description What You’ll Do for Us: Collaborate with cross-functional teams to understand business requirements and objectives. Translate business requirements by incorporating data and develop ML and AI algorithms to predict outcomes for various functional areas and use-cases, including Finance, Marketing, Supply Chain (among others) across the Globe. Leverage a diverse set of large structured and unstructured data to derive meaningful insights and information sets for modeling. Develop, implement, and optimize ML and AI algorithms to predict outcomes and derive insights from large structured and unstructured datasets. Visualize and interpret data and create reports and actionable insights. Communicate complex analytical work to a variety of technical and non-technical stakeholders, including executive management. Partner with ML OPS and other cross-functional teams to scale and operationalize ML and AI use-cases. Maintain technical documentation in accordance with the agreed standards. Build and maintain a robust library of data science solutions, reusable templates, algorithms and supporting code. Leverage CI and CD principles to automate and improve repeatability of deployments. Keep abreast of industry trends and developments in data science and analytics. Qualifications & Requirements: Master’s degree in a quantitative field, such as Data Science, Statistics, Economics, Finance, Mathematics, Operations Research or other quantitative discipline. Experience gathering, interpreting and translating business requirements. Proficient experience with analytical and programming languages and packages, such as Python, R, and SQL. Able to understand various data structures and common methods in data transformation. Demonstrated experience in large-scale data wrangling with relational databases and/or Spark, PySpark. 5+ years’ experience applying a range of statistical and modeling techniques including hypothesis testing, dimensionality reduction, supervised learning (classification and regression), forecasting, and unsupervised clustering and putting solutions into production. Strong aptitude for learning and applying new technologies related to Data Science and Data Management. Demonstrated ability to communicate complex analytical concepts and results at multiple levels to both technical and non-technical audiences. Experience with code version control platforms like GitHub, GitLab or Azure DevOps. Functional Skills: Handles multiple competing priorities in a fast-paced, deadline-driven environment. Strong attention to details and excellent problem-solving skills. Ability to work in a collaborative team environment. Highly innovative, adaptable, and self-directed. Results-oriented with a delivery focus. Presentation skills: Ability to communicate technical topics to business audience. Be able to collaborate across other levels of the organization. Team player who can lead a discussion to defined outcomes. Effective Communication. Pursuing Innovation. What We Can Do for You: Innovation & Technology: The ability to work with an award-winning team that is on the cutting edge of innovation. Exposure to World Class Leaders: Availability to global technology leaders that will expand your network and exposure you to emerging technologies and techniques. Agile Work Environment: We embrace agile with management that believes in removing barriers, so you are empowered to experiment, iterate and innovate. Skills: Data Science; Data Warehousing (DW); Structured Query Language (SQL); Data Systems; Data Modeling; Agile Methodology; Business Intelligence (BI); Machine Learning Techniques; Data Analytics; Data Engineering; Data Processing; Artificial Intelligence (AI); Machine Learning; SQL Databases 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. #J-18808-Ljbffr

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