PDI is seeking a Senior Data Scientist to help lead the digital transformation of R&D by building connected, AI-ready data and analytics capabilities across the organization.
This role will design, build, and operate systems that collect, structure, and analyze scientific data from R&D laboratories to accelerate product development, improve decision-making, reduce manual effort, and strengthen compliance across regulated environments. The position partners closely with R&D, Quality, IT, and external vendors to integrate laboratory instrumentation, automate workflows, and develop scalable analytical and machine learning capabilities across the R&D lifecycle.
- Opportunity to help shape the future of digital R&D within a growing organization
- Highly visible role partnering across Science, Quality, and Technology
- Ability to influence foundational AI and analytics capabilities
- Work in a regulated environment where innovation and scientific rigor intersect
- Define and maintain scientific data standards, metadata structures, and governance practices across R&D workflows
- Ensure data integrity, traceability, and compliance with ALCOA+ and 21 CFR Part 11 principles
- Validate data transformations and ensure scientific validity is preserved throughout the data lifecycle, from raw instrument output through interpreted results
- Lead structured recovery and AI-enabled extraction of historical R&D data to unlock reusable scientific knowledge and improve discoverability of prior experiments and learnings
- Translate scientific and business problems into analytical and statistical solutions
- Apply modeling and experimental design techniques such as DOE, chemometrics, predictive analytics, and machine learning methodologies
- Develop ML-ready datasets and reusable data structures to support advanced analytics and automation
- Partner with scientists, IT, and vendors to integrate laboratory instrumentation with digital data environments
- Define instrument-level data capture, metadata schemas, and audit trail requirements to support traceability, compliance, and scalable analytics
- Translate laboratory workflows into scalable digital processes and systems
- Serve as a key technical liaison between R&D and IT for scientific data architecture and digital initiatives
- Train and support scientists on digital tools, data capture best practices, and analytical workflows
- Translate scientific, regulatory, and business requirements into practical, scalable technical solutions
- Ensure inspection readiness and traceability from raw scientific data through analytical interpretation and decision-making
- Help drive the transition from intuition-based to evidence-based scientific decision-making across R&D
- Increased automation and reduced manual effort across R&D workflows
- Improved accessibility, reuse, and integrity of scientific data
- Reduction in redundant experimentation and development inefficiencies
- Increased adoption and effectiveness of digital and analytical tools
- Development of scalable, compliant, and AI-ready data environments
- Strong cross-functional collaboration across R&D, Quality, IT, and external partners
MS or PhD strongly preferred in a quantitative or scientific discipline such as:
- Laboratory instrumentation and scientific software platforms
- Experience with Python, R, SQL, JMP, or similar analytical tools
- Experience with DOE, predictive analytics, statistical modeling, and machine learning methodologies
- Familiarity with laboratory data sources including formulation, analytical, and stability data
- Understanding of regulated environments including FDA, EPA, Medical Device, and/or Cosmetic frameworks
- Experience with LIMS, ELN, or scientific informatics platforms
- Knowledge of 21 CFR Part 11, audit trails, and data integrity principles
- Experience supporting regulated product development environments
- Exposure to AI/ML platform engineering or workflow deployment
- Strong communication and collaboration skills across technical and non-technical teams
- Ability to translate complex scientific workflows into scalable digital systems
- Comfortable operating across R&D, Quality, Informatics, and IT environments
- Strong analytical thinking and problem-solving capabilities
- Some work on laboratory systems may be required, resulting in a hybrid office and lab environment.