Principal Data Scientist, R&D Digitalization

The Principal Data Scientist is a strategic, senior-level contributor who will build, implement, lead, and manage Mactac's Research & Development (R&D) Digitalization Program. Following program implementation, this role will serve as the company's subject matter expert (SME) for data science and machine learning, accelerating profitable growth through innovation and continuous improvement in quality.

This role will spend their first 12 to 24 months owning the end-to-end development and implementation of the R&D Digitalization Program centered on the Mactac R&D Data Informatics Platform. During this time, responsibilities will include:

• Own the design and implementation of the Mactac R&D Data Informatics Platform, including requirements definition, data model/ontology development, experiment templates, integrations (SSO, data pipelines), validation, training, and adoption KPIs.
• Partner with the software vendor, company users (R&D, Innovation departments), and internal stakeholders (IT, EHS, Quality, Sourcing) to define requirements, configure data models and experiment templates, deliver training, and lead change management to full adoption.
• Lead vendor and internal team and coordinate day‑to‑day collaboration with the enterprise R&D data Informatics platform vendor and internal stakeholders to deliver the program.
• Lead change management and training by building a core network of R&D and IT champions, developing role‑based training, and driving adoption across company labs and sites.
• Establish acceptance and validation criteria, ensuring go‑live readiness and a structured handoff to sustainment with measurable usage.

Following implementation of the company's R&D Digitalization Program, this role will lead R&D Data Science and Machine Learning (ML) program development, cultivate and support "citizen data scientists" within R&D, and act as an enterprise Data Science SME for Mactac and Lintec USA. Responsibilities include:

• Own high impact analytics and ML projects that accelerate new products, improve service/quality, and grow margin.
• Serve as a Mactac SME for data science/ML, advising leaders, mentoring scientists/engineers, setting coding/ML standards (reproducibility, versioning, documentation), and guiding governance.
• Serve as a Lintec USA SME for data science/ML, advising sister companies on developing their own platform or adopting and integrating Mactac's R&D Data Informatics Platform.
• Partner cross-functionally (R&D, Quality, Operations, Sourcing, IT, Commercial) to embed analytics into decision-making and drive portfolio visibility and predictability.
• Continuously improve the R&D data platform, including backlog management, integrations, new analytics features, data quality, and security/compliance to ensure sustainability and value realization.

Qualifications:

• Bachelor's degree in Data Science, Statistics, Computer Science, Chemical Engineering, Materials Science, Chemistry or a related technical discipline is required. Master's degree or PhD preferred.
• Minimum of 7 years professional experience in data science and/or advanced analytics within an R&D and/or product development environment is required, including a minimum of 3 years experience leading multi‑stakeholder digitalization, ELN, or analytics platform implementations, including SaaS vendor collaboration, integrations, and change management.
• Prior industry experience with chemicals, polymers/materials, adhesives, polymeric films, paper, or other web handling R&D is desirable.
• Background in pressure‑sensitive adhesives (PSA), polymers, coatings, or materials; familiarity with web handling, converting and label/packaging markets preferred.
• Prior experience implementing Enterprise R&D Data Informatics Platforms and Machine Learning Tools is preferred.
• Proficiency in Python, SQL, and ML frameworks is preferred, including experience with MLOps/model lifecycle, data governance, and reproducible science.
• Strong background and expertise in applied statistics and experimental design (DOE).
• Demonstrated ability to translate business and scientific problems into deployable analytical solutions that deliver measurable outcom