Manager, Data Scientist

Summary: The Coca-Cola Company’s Technology organization is in the midst of a digital transformation that allows our employees to use world class technology to connect our products to our customers all over the world. This journey is a very exciting time for Coca-Cola and our employees are big contributors to our Success and Growth. Our large scale and complex environment offers an incredible opportunity to address challenges, enable innovative solutions to make a difference for our customers.  As a Machine Learning Architect 1 (Individual Contributor), you will embark on a journey of leveraging vast amounts of data to transform it into actionable insights. You will aid in the development of analytics models and work under the guidance of seasoned data science professionals to drive decision-making and strategy across the organization. This is an exciting opportunity to grow in your career in data science and analytics within a supportive and innovative environment.  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 produce actionable insights for various functional areas and use-cases, including Marketing, Finance, Technical Innovation and 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.  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 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:   Bachelor’s or Master’s degree in a quantitative field, such as Data Science, Statistics, Computer Science, Economics, Finance, Mathematics, Operations Research or other quantitative discipline.  Ph.D. preferred.  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.   4+ years’ experience applying a range of statistical, modeling, and mathematical optimization techniques including hypothesis testing, dimensionality reduction, Mixed-Integer Programming (MIP), supervised learning (classification and regression), Bayesian modeling, 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:      Practical experience with as many of the following as possible:  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