Data Scientist – Global Equipment Platforms (Coca-Cola GEP)

Position Overview: 

The Data Scientist, Global Equipment Platforms plays a key role in delivering data-driven insights and machine learning solutions that improve equipment performance, optimize operations, and support innovation across Coca-Cola’s global connected equipment ecosystem. 

This is a hands-on role focused on building and deploying analytics and machine learning solutions using telemetry, service, and operational data across millions of connected devices. 

The role partners closely with Data Engineering (platform, infrastructure, and pipelines) and Data Product & Analytics (use case definition, analytics, and adoption) teams to translate business problems into scalable data solutions and ensure real-world impact across bottlers and operating units.  
 

Key Responsibilities: 

• Develop and deploy data science models and advanced analytics solutions for high-impact use cases such as predictive maintenance, equipment health monitoring, and service optimization. 

• Analyze large-scale telemetry and operational datasets to identify patterns, anomalies, and performance opportunities across equipment fleets. 

• Collaborate with cross-functional teams (Product, Engineering, AI, and Analytics) to translate business problems into data-driven solutions and measurable outcomes. 

• Work closely with data engineering teams to access, validate, and prepare data for analysis and model development. 

• Build feature pipelines and contribute to reusable datasets and analytics capabilities that support multiple use cases. 

• Support experimentation and innovation initiatives by developing metrics, dashboards, and analytical models to measure performance and impact. 

• Communicate insights and recommendations through clear, concise storytelling tailored to both technical and business stakeholders. 

• Validate, monitor, and continuously improve data models (analytics, statistical) and AI/ML models based on performance, feedback, and evolving business needs. 

• Ensure solutions are practical, scalable, and aligned with production environments and operational workflows. 
   

Qualifications & Requirements: 

• 3–5 years of experience in data science, analytics, or related roles with demonstrable business impact 

• Strong proficiency in Python, SQL, and data science libraries (e.g., Pandas, Scikit-learn, etc.) 

• Experience working with large, complex datasets, including time-series or telemetry data 

• Experience building and deploying predictive models in production or near-production environments 

• Strong analytical thinking with the ability to translate data into actionable insights 

• Experience working in cross-functional teams with product, engineering, and business stakeholders 

• Ability to work hands-on across data preparation, modeling, and analysis 

• Strong communication skills with ability to explain technical outputs in business terms 
   

Preferred Skills: 

• Experience with IoT, connected devices, or equipment/telemetry-based data 

• Experience in predictive maintenance, anomaly detection, or reliability analytics 

• Exposure to cloud platforms such as Azure, Fabric, Databricks, or similar 

• Familiarity with data pipelines and working with data engineering teams 

• Experience supporting experimentation, A/B testing, or product analytics 

• Experience contributing to reusable analytics or data product initiatives