Founded in 1920, Eastman is a global specialty materials company that produces a broad range of products found in items people use every day. With the purpose of enhancing the quality of life in a material way, Eastman works with customers to deliver innovative products and solutions while maintaining a commitment to safety and sustainability. The company’s innovation-driven growth model takes advantage of world-class technology platforms, deep customer engagement, and differentiated application development to grow its leading positions in attractive end markets such as transportation, building and construction, and consumables. As a globally inclusive company, Eastman employs approximately 14,000 people around the world and serves customers in more than 100 countries. The company had 2024 revenue of approximately $9.4 billion and is headquartered in Kingsport, Tennessee, USA. For more information, visit www.eastman.com.

Role Description

Data Scientists at Eastman provide actionable insights using advanced analytics to help the company make better and faster decisions. Our centralized Data & Analytics department is organized into specialized groups including Applied Statistics, AI/Machine Learning, Operations Research, and Generative AI. As a Data Scientist in the AI/ML team at Eastman, you will design, build, and operationalize predictive and prescriptive models using traditional machine learning and computer vision techniques that deliver measurable business impact across diverse functions. You will collaborate with cross-functional teams across manufacturing, technology, commercial, and business operations, translating complex challenges into scalable machine learning solutions.

Key Responsibilities

Design, develop, and deploy machine learning models using Python for applications including process optimization, predictive analytics, demand forecasting, computer vision, and decision support systems
Network across manufacturing, R&D, commercial, and business functions to identify and collaboratively execute on high-impact challenges solvable through machine learning techniques
Build production-ready ML solutions within enterprise platforms (Databricks, Azure ML) including model training, endpoint deployment, and monitoring
Develop and maintain scalable, modular, well-documented code following MLOps best practices including version control, logging/metadata standards, and CI/CD principles
Collaborate with domain experts and stakeholders across a wide variety of domains to understand needs, integrate ML solutions into workflows, and deliver measurable business value
Communicate complex machine learning methodology, results, and business impact clearly to technical and non-technical audiences through presentations, documentation, and visualizations
Stay current with advancements in machine learning, and apply them to solve business challenges
Support ML governance processes including architecture and methodology reviews, risk assessments, and compliance with enterprise standards

Basic Qualifications

Master's or Ph.D. in a relevant field such as Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a domain-focused field (Engineering, Chemistry, Economics, Finance, etc.)
1 - 4 years of experience in data science, machine learning, or a related field with demonstrated impact
Strong foundation in statistical and machine learning theory with proven ability to select, apply, and evaluate appropriate techniques for complex, real-world problems
Hands-on experience building predictive models and deploying them to production environments using Python
Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost,
Hands-on experience with cloud platforms such as Azure and/or Databricks for deploying and managing ML models and solutions
Experience with version control (Git), collaborative development, and code review processes
Strong written and oral communication skills with ability to present technical concepts and results to diverse audiences from technical teams to business leadership
Strong problem-solving skills and the ability to work independently as well as lead and mentor team members

Preferred Qualifications

Domain expertise in one or more areas: manufacturing processes, chemical/materials engineering, product development, supply chain, commercial analytics, economics, finance, or related technical/business domains
Experience with MLOps platforms (Databricks, Azure ML, MLflow) and containerization (Docker)
Experience with time-series forecasting, computer vision applications, or hybrid modeling approaches (physics-informed ML, combining domain knowledge with ML)
Familiarity with Agile development methodologies
Knowledge of data engineering, data warehousing, and SQL
Experience using data science techniques to solve real-world problems across multiple business domains and communicate their value to stakeholders

Eastman Chemical Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, pregnancy, veteran status or any other protected classes as designated by law.

Eastman is committed to creating a highly engaged workforce, where everyone can contribute to their fullest potential each day.

Eastman

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