Job Description Summary:
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
Skills:
Collaborative Leadership, Communication, Data Compilation, Manufacturing Analytics, Process Improvements, Risk Assessments, Statistical Process Control (SPC), Supply Chain ProcessesPay Range:
United States of America: 149,000 USD - 173,000 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
30Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
NoJob Posting End Date:
July 22, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.Skills Required
- 3-5 years of experience in data science, analytics, or related roles with demonstrable business impact
- Strong proficiency in Python
- Strong proficiency in SQL
- Experience with data science libraries (Pandas, Scikit-learn)
- Experience working with large, complex datasets including time-series or telemetry data
- Experience building and deploying predictive models in production or near-production environments
- 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
- 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
The Coca-Cola Company Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about The Coca-Cola Company and has not been reviewed or approved by The Coca-Cola Company.
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Retirement Support — Retirement benefits are positioned as a standout, combining a 401(k) match with a company-funded cash-balance pension and an employee stock purchase plan match that together materially increase long-term package value.
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Healthcare Strength — Health coverage is described as broad and feature-rich, including national medical coverage plus specialized add-ons like virtual care, second opinions, oncology navigation, fertility support, and chronic-condition programs.
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Leave & Time Off Breadth — Time-off benefits are outlined with structured vacation accrual that increases with tenure and a holiday program that includes both set and floating days.
The Coca-Cola Company Insights
What We Do
The Coca-Cola Company (NYSE: KO) is a total beverage company, offering over 500 brands in more than 200 countries and territories. In addition to the company’s Coca-Cola brands, our portfolio includes some of the world’s most valuable beverage brands, such as AdeS soy-based beverages, Ayataka green tea, Dasani waters, Del Valle juices and nectars, Fanta, Georgia coffee, Gold Peak teas and coffees, Honest Tea, innocent smoothies and juices, Minute Maid juices, Powerade sports drinks, Simply juices, smartwater, Sprite, vitaminwater and ZICO coconut water.
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