Manager, Data Science

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.

 

Responsibilities

 

We are seeking a Manager, Data Science to lead a multidisciplinary team of Machine Learning Engineers, Operations Research Analysts, and Statisticians. This leader will drive advanced analytics solutions that improve manufacturing reliability, optimize supply chain and logistics, accelerate R&D, and strengthen commercial decision-making across Eastman. The role spans strategy, delivery, and people leadership, with accountability for model lifecycle management, high-quality experimental design, optimization at scale, and responsible AI oversight.

•    Team Leadership & Talent Development
     o    Lead, mentor, and grow a 15-person team (ML Engineering, Operations Research, Statistics).
     o    Set clear goals, accountability frameworks, and career development plans.
     o    Foster a culture of scientific rigor, safety, and continuous improvement.
•    Analytics Strategy & Portfolio Management
     o    Build and execute a roadmap aligned to manufacturing excellence, commercial disciplines, supply chain optimization, and R&D innovation.
     o    Prioritize initiatives using business value, feasibility, risk, and time-to-impact.
     o    Define and track KPIs for model performance and business outcomes.
•    Solution Delivery & Technical Excellence
     o    Oversee end-to-end development of ML/AI, OR optimization, and statistical solutions (discovery through deployment and monitoring).
     o    Direct initiatives such as: process optimization, production scheduling, inventory/demand planning, logistics network optimization, price/mix analytics, formulation design, and experimental design.
     o    Ensure reproducibility, scalability, and robust MLOps practices.

•    Responsible AI & Compliance
     o    Partner with the Responsible AI council to enforce policies on data ethics, transparency, bias mitigation, safety, and model risk management.
     o    Establish documentation standards (model cards, data lineage), human-in-the-loop controls, and production guardrails.
     o    Align with quality systems, regulatory requirements, cybersecurity, and data privacy policies.
•    Cross-Functional Collaboration
     o    Engage closely with Manufacturing, Supply Chain, R&D, Commercial, IT/Data Architecture, etc. to translate business problems into analytical solutions.
     o    Work with Data Architecture and Data Management teams to ensure high-quality data pipelines, metadata standards, and governance.
     o    Communicate insights and decisions to executives and operational stakeholders.

Qualifications

 

•    Bachelor's degree required, advanced degree (M.S./Ph.D.) preferred in Operations Research, Statistics, Computer Science, Chemical Engineering, Industrial Engineering, or related field.
•    8+ years of experience in Data Science/Analytics or related role, including 3+ years leading technical teams.
•    Proven delivery of production-grade ML/AI and optimization solutions with measurable business impact.
•    Expertise in:
     o    Machine learning: supervised/unsupervised learning, time series, anomaly detection, NLP, feature engineering, model monitoring.
     o    Operations research (preferred): mathematical programming, network optimization, scheduling, simulation.
     o    Statistics: designed experiments, multivariate analysis, statistical process control, regression analysis, and data modeling.
•    Knowledge of software engineering practices: version control, code reviews, testing, CI/CD, containerization.
•    Excellent stakeholder management and communication skills; ability to translate analytics into decisions.
•    Knowledge of data governance, privacy, and model risk frameworks.

 

Eastman will not accept ap