Manager, Data Science

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.

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

• Lead, mentor, and grow a 15‑person team (ML Engineering, Operations Research, Statistics).
• Set clear goals, accountability frameworks, and career development plans.
• Foster a culture of scientific rigor, safety, and continuous improvement.

Analytics Strategy & Portfolio Management

• Build and execute a roadmap aligned to manufacturing excellence, commercial disciplines, supply chain optimisation, and R&D innovation.
• Prioritise initiatives using business value, feasibility, risk, and time‑to‑impact.
• Define and track KPIs for model performance and business outcomes.

Solution Delivery & Technical Excellence

• Oversee end‑to‑end development of ML/AI, OR optimisation, and statistical solutions (discovery through deployment and monitoring).
• Direct initiatives such as: process optimisation, production scheduling, inventory/demand planning, logistics network optimisation, price/mix analytics, formulation design, and experimental design.
• Ensure reproducibility, scalability, and robust MLOps practices.

Responsible AI & Compliance

• Partner with the Responsible AI council to enforce policies on data ethics, transparency, bias mitigation, safety, and model risk management.
• Establish documentation standards (model cards, data lineage), human‑in‑the‑loop controls, and production guardrails.
• Align with quality systems, regulatory requirements, cybersecurity, and data privacy policies.

Cross‑Functional Collaboration

• Engage closely with Manufacturing, Supply Chain, R&D, Commercial, IT/Data Architecture, etc. to translate business problems into analytical solutions.
• Work with Data Architecture and Data Management teams to ensure high‑quality data pipelines, metadata standards, and governance.
• Communicate insights and decisions to executives and operational stakeholders.

• 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 optimisation solutions with measurable business impact.
• Expertise in
  • Machine learning: supervised/unsupervised learning, time series, anomaly detection, NLP, feature engineering, model monitoring.
  • Operations research (preferred): mathematical programming, network optimisation, scheduling, simulation.
  • Statistics: designed experiments, multivariate analysis, statistical process control, regression analysis, and data modelling.
• Knowledge of software engineering practices: version control, code reviews, testing, CI/CD, containerisation.
• Excellent…