Advanced Data Scientist - Machine Learning

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 an Advanced Data Scientist in the AI/ML team at Eastman, you will develop, implement, and operationalize predictive and prescriptive models using traditional machine learning and computer vision techniques to address business needs and deliver measurable value across diverse functions. You will collaborate with cross-functional teams across manufacturing, technology, commercial, and business operations, translating complex challenges into practical and scalable machine learning solutions. This role will contribute to the continued growth of our data science capabilities in Hyderabad by applying technical expertise, sharing knowledge with team members, and following established engineering and analytical best practices. You will support the design, development, and adoption of new analytical capabilities, contribute to the evolution of machine learning solutions, and partner with stakeholders across the company to help deliver reliable, production-ready models.

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 and metadata standards, and CI/CD principles
• Participate in code reviews and architectural discussions, applying established design and development practices to deliver high-quality, production-ready solutions
• 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, risk assessments, and compliance with enterprise standards

Basic Qualifications

• Bachelor’s in Computer Science, Mathematics, Statistics, or a related field.
• 5 - 8 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
• Extensive hands-on experience building predictive models and deploying them to production environments using Python
• Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost, TensorFlow/PyTorch) and data manipulation libraries (pandas, polars, numpy)
• 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
• Drive initiatives from conception to production

Preferred 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.)
• 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