Senior R&D Data Scientist

DESCRIPTION

Driven by a commitment to research, quality, and service, PDI provides innovative products, educational resources, training, and support to prevent infection transmission and promote health and wellness. Encompassing three areas, our Healthcare, Sani Professional and Contract manufacturing divisions, we develop, manufacture, and distribute leading edge products for North America and the world. We have several locations across the US and are looking for new Associates to join our team! 

POSITION PURPOSE

PDI is seeking a Senior Data Scientist to serve as the technical foundation of its R&D digital transformation. This individual will design and operate the data systems, pipelines, and analytical capabilities that transition PDI's R&D from siloed, manual data environments to a connected, AI-ready platform ecosystem. This role is empowered to enable faster formulation decisions, reduced development cycle time, and compliance by design across EPA, FDA OTC/NDA, Medical Device, and Cosmetic regulatory domains.

The scientist will design and operate systems that collect, structure, process, and use experimental data from R&D labs, resulting in accelerated development, improved decision-making, and reduced manual effort. This role enables PDI’s R&D organization by bridging lab instrumentation, data science & engineering, and machine learning workflows to create integrated, compliant, and scalable digital capabilities.

The position partners closely with R&D scientists, Quality, IT, and external vendors to ensure instrumentation is connected, data flows are automated, ML workflows are production‑ready, and digital tools support execution across the R&D lifecycle.

ESSENTIAL FUNCTIONS AND BASIC DUTIES

Scientific Data Fidelity & Integrity

• Define and enforce scientifically meaningful data standards across lab workflows, including raw, processed, and reduced data
• Ensure data transformations preserve scientific validity and traceability from raw instrument output through to interpreted results
• Partner with scientists to validate analytical assumptions, calculations, and interpretation logic
• Establish minimum viable metadata standards to enable reuse and traceability without overengineering
• Define and maintain data dictionaries and controlled vocabularies for key experimental parameters, in support of ALCOA+ and 21 CFR Part 11 compliance

Legacy Data Enablement & Knowledge Recovery

• Lead structured assessment of historical R&D data, including formulation, stability, analytical, and process development records, to identify high-value, recoverable knowledge assets
• Evaluate AI‑assisted extraction methods (e.g., semantic search, pattern mining) with a focus on scientific validity
• Enable discoverability of prior experiments and learnings to reduce redundant work and speed decision‑making
• Quantify redundancy and rework attributable to inaccessible historical data; translate findings into a prioritized data recovery roadmap

Decision‑Focused Analytics & Modeling

• Design data science solutions based on gathering and translation of business requirements.
• Translate high‑value scientific decision points into analytical and statistical models
• Apply appropriate mathematical, statistical, or experimental design techniques to evaluate hypotheses and trends (e.g. DOE, Arrhenius modeling, chemometrics)
• Partner with stakeholders to ensure outputs are explainable, interpretable, and trusted
• Support development of predictive or comparative models where scientifically justified
• Develop and maintain ML-ready datasets and reusable feature layers that support R&D modeling, advanced analytics, and automation.
• Apply Design of Experiment (DOE) principles to help R&D teams structure studies that generate AI-ready, analyzable datasets from the outset

Lab Instrumentation & Digital Integration

• Work alongside scientists, IT, and external vendors to Integrate lab instrumentation (e.g., chromatography, spectroscopy, automated systems) with digital data environments by defining structured data capture schemas, metadata requirements, and audit trail specifications at the instrument level
• Define structured data capture, metadata schemas, and workflow models at the instrument level to ensure compliance and traceability
• Collaborate with internal lab teams to map experimental workflows and translate them into digital processes
• Governance, Compliance & Scientific Trust
• Ensure analytical processes and data interpretations align with regulated R&D expectations (ALCOA+, data integrity pri