Senior Director, Machine Learning Architect

The Coca-Cola Company•Atlanta, GA

1d•$195,500 - $226,800•Onsite

About The Position

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 Senior Director, Machine Learning Architect, 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.

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 disciplines. Ph.D. preferred.
10+ 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.
5+ years of experience managing and scaling high-performing machine learning teams.
Experience gathering, interpreting and translating business requirements, with a preferred background driving product innovation or solving complex supply chain and operational challenges.
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.
Demonstrated experience building end-to-end (E2E) data science and analytic solutions from ideation, production, and ongoing monitoring.
Demonstrated experience coordinating with cross-functional teams in completion of E2E solutions.
Demonstrated experience with the Microsoft Azure analytics stack (Cosmos DB, Azure Synapse, Azure ML, Databricks).
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.

Nice To Haves

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, including senior executive stakeholders
Be able to collaborate across other levels of the organization
Effective Communication
Pursuing Innovation

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 for various functional areas and use-cases, including Marketing, Finance, Technical Innovation and Supply Chain (among others) across the Globe.
Partner with product teams and incorporate experience in product design to ensure seamless solution integration.
Leverage a diverse set of large structured and unstructured data to derive meaningful insights and information sets for modeling.
Develop, manage, and maintain end-to-end (E2E) AI/ML solutions from ideation, production, and ongoing monitoring.
Communicate complex machine learning 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.
Develop and 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 AI and Machine Learning.
Mentor, guide, and develop junior/aspiring data scientists across the organization.
Lead continuous career development and drive engineering excellence through performance reviews.
Manage vendor selection and oversee external vendors' delivery and work items.