Director, Data Science

Location(s):

United States of America

City/Cities:

Atlanta

Travel Required:

00% - 25%

Relocation Provided:

No

Job Posting End Date:

March 17, 2026

Shift:

Job Description Summary:

Role Overview:

The Director, Data Scientist, AI/ML and Molecular Discovery, is a critical role within Flavor & Ingredient Research and Application (FIRA) and Research to Experimentation (RtE). The role will be responsible for setting the FIRA digital and AI strategy, providing system wide thought leadership in using AI/ML in discovery, and drive advanced data and AI initiatives to accelerate research and development initiatives.

The Flavor/Ingredient Research and Application (FIRA) team aims to develop and deploy cutting-edge research to drive business-related innovation and growth. We leverage the latest innovations in science and technology, including artificial intelligence, machine learning, and data science approaches, to analyze complex research data, discover new insights, and enable a deeper understanding of The Coca-Cola Company’s ingredients and beverage systems.

Key Responsibilities

FIRA AI & Digital Strategy

• Develop a comprehensive AI/ML strategy to enable and accelerate R&D initiatives across FIRA with measurable results.
• Leverage expertise to direct internal model development, evaluating external AI opportunities, and to guide future AI-driven capabilities.

Scientific & Technical Leadership

Develop and implement AI / ML strategy to accelerate departmental research and technology platforms Advance computational biology and systems biology to unlock the science of taste and olfaction as well as and prioritized functional areas. Use and develop AI / ML tools to accelerate molecular discovery research efforts, such as:

• Chemical property predictive models
• Virtual / in silico screening and lead optimization
• Generative AI for protein structure prediction co-folding (peptides, small molecules)
• De novo molecular design using generative chemistry

Data Analytics

• Utilize advanced data analytics and AI strategies to optimize insights from biological, sensory, and clinical data.
• Guide and evaluate digital strategy and data management across FIRA.

Cross Functional Collaboration

• Collaborate across multiple disciplines – such as Biology, Chemistry, Sensory, Application, Analytical Sciences
• Partner with data scientists and engineers across TI&SC and Platform services
• Participate in company-wide AI initiatives and communities of practice to further the adoption AI and enable digital synergy across TCCC network

External Engagement & Strategy

• Represent our data-driven research agenda to external parties and collaborators.
• Advance AI/ML research projects through external consortia and networks.
• Champion the development of Agentic AI frameworks with partners such as Microsoft, Accenture, CapGemini, etc.

Skill/Knowledge Areas

• Communication Skills: Ability to present complex scientific concepts and articulate business impact to both peers and senior leadership.
• Strategy Development and Innovation: Provide strategic recommendations to influence research direction and AI adoption as it relates to TCCC business priorities and long-term goals.
• Advanced Knowledge of Machine Learning: Familiarity with machine learning algorithms and techniques, such as supervised and unsupervised learning, neural networks, and deep learning. Ability to apply machine learning models to chemistry-related problems, including drug discovery, molecular property prediction, and biological function.
• Collaboration and Project Management: Manage research collaborations with CROs, academic labs, and research consortia.
• Strong Foundation in Computational Biology/Chemistry.
• Programming Skills: Proficiency in programming languages commonly used in cheminformatics and machine learning, such as Python, R, and Java. Experience with data analysis libraries and machine learning frameworks, such as NumPy, SciPy, Scikit-learn, TensorFlow, and PyTorch.
• Data Handling and Manipulation: Skills in data preprocessing, cleaning, and transformation to ensure high-quality input for machine learning models. Ability to work with large datasets.
• Analytical Thinking and Problem-Solving: Strong analytical skills to interpret complex chemical and computational problems. Creative problem-solving abilities to develop novel solutions in cheminformatics.
• <