Senior Manager, Data Scientist

Location(s):

United States of America

City/Cities:

Atlanta

Travel Required:

00% - 25%

Relocation Provided:

No

Job Posting End Date:

September 5, 2025

Shift:

Job Description Summary:

Location: Atlanta, GA (Global HQ)

Estimated Travel: 0-20%

Direct Reports: None

The Global Equipment Platforms (GEP) team is seeking an exceptional and highly skilled Data Scientist to unlock the profound value hidden within the telemetry data of The Coca-Cola Company's global fleet of 17MM+ connected equipment. Reporting to the Head of Data within GEP Digital, this individual contributor role is crucial in transforming raw data from beverage vending machines, dispensers, coolers, and retail racks into actionable intelligence that drives revenue growth, reduces operating expenses, and provides unprecedented real-time market understanding.

You will be at the forefront of designing, developing, and deploying advanced analytical models, machine learning algorithms, and potentially AI Agents, leveraging vast datasets from equipment running on the KO Operating System (KOS) and other embedded systems. This role demands a deep technical expert with a proven track record of extracting insights from complex, high-volume data, building robust predictive solutions, and effectively communicating findings to influence strategic decisions across our internal teams, 200+ global franchise bottlers, and OEM partners. Your work will directly enable predictive maintenance, optimize equipment placement, personalize consumer experiences, and inform real-time commercial strategies.

Key Responsibilities:

Advanced Analytics & Model Development (40%):

• Lead the end-to-end development of advanced analytical models and machine learning algorithms (e.g., predictive maintenance, anomaly detection, demand forecasting, sales optimization, personalization, inventory management) using diverse equipment telemetry data.

• Design and implement statistically sound experiments to test hypotheses and evaluate the impact of digital initiatives on business outcomes.

• Explore and apply cutting-edge AI capabilities, including the potential for AI Agents for autonomous decision-making and computer vision solutions for equipment-level insights.

• Leverage vast datasets from 17MM+ connected devices, considering the nuances of various equipment types and global market conditions (premium to ultra low-cost solutions).

• Develop and evaluate advanced machine learning models (e.g., predictive maintenance, anomaly detection, demand forecasting, consumer behavior analytics) with a clear pathway for product ionization by the Lead AI Engineer, ensuring insights are actionable and inform product decisions (in partnership with the Product Owner) to drive TCO reduction and revenue growth.

Insight Generation & Storytelling (25%):

• Translate complex analytical findings and model results into clear, concise, and actionable business insights for a diverse audience, including senior leadership, Global Customer Commercial teams, and Bottler partners.

• Develop compelling data visualizations, dashboards, and presentations to effectively communicate insights and recommendations.

• Identify strategic opportunities for leveraging connected equipment data to solve critical business problems, reduce the "fog of war," and create competitive advantage.

Data Exploration & Feature Engineering (20%):

• Collaborate closely with Lead Data Engineers to identify, acquire, and prepare high-quality, relevant data from disparate sources (telemetry, sales, customer data, external market data) for analysis and model building.

• Perform rigorous exploratory data analysis to uncover hidden patterns, trends, and correlations within complex IoT datasets.

• Develop robust feature engineering pipelines that transform raw data into features optimized for machine learning models.

• Proactively identify data quality issues and work with data engineering to resolve them, ensuring data integrity and reliability for analytical purposes.

• Design and implement robust feature engineering strategies, working closely with the Lead Data Engineer to ensure optimal data preparation and access for model training.

Model Deployment, Monitoring & Optimization (15%):

• Work with Data Engineering and Digital Technology Solutions (IT) teams to ensure seamless deployment of machine learning models into production environments (primarily Azure ML, Azure Databricks).

• Design and implement robust moni