Senior Engineer, R&D Lab Digital Systems

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 

We are seeking an experienced Senior Digital Engineer who is passionate about creating impact through data, digital systems, and AI. This role enables PDI’s R&D organization by bridging lab instrumentation, data engineering, and machine learning workflows to create integrated, compliant, and scalable digital capabilities.

The Senior Digital Engineer 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.

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 

• Lab Instrumentation & Digital Integration
  • Integrate lab instruments (e.g., chromatography, spectroscopy, automated systems) with digital data environments.
  • 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.
• Data Engineering & Management
  • Build and maintain data pipelines (streaming + batch) connecting instruments, LIMS/ELN, data lakes, and analytics/ML environments.
  • Implement and govern master data, metadata, lineage, and catalog standards for lab-originated datasets.
  • Ensure data quality controls and audit‑ready traceability across experimental data and analytical outputs.
• AI/ML Engineering & Analytics Enablement
  • Develop and maintain ML-ready datasets and reusable feature layers that support R&D modeling, advanced analytics, and automation.
  • Build core platform capabilities that are scalable, secure, and promote self-service ML infrastructure for scientists and engineers.
  • Partner with research teams to translate scientific problems into ML workflows; validate model inputs/outputs for correctness.
• Workflow Digitization & Process Automation
  • Partner with R&D Discovery & Innovation, Product Development Delivery, Sustainment & Lifecycle Optimization, and the Project & Performance Office teams to map current workflows end-to-end (inputs, systems, approvals, artifacts).
  • Digitize experimental workflows, stability/validation processes, and change-control elements using structured schema definitions to decompose technical packages into steps an AI can execute with citations.
  • Capture validation/stability/release processes (accuracy/precision/linearity/LOQ, OOT rules, spec checks, CoAs) and translate them into checklists, schemas, and templates.
  • Build templates, SOPs, and checklists to standardize data capture and automate documentation steps.
  • Integrate workflows into R&D digital platforms (LIMS/ELN, MS Teams, cloud environments).
• Compliance, Quality & Data Integrity
  • Implement compliance-by-design digital processes that align with regulated environment expectations with emphasis on 21 CFR Part 11 and ALCOA+ data integrity requirements.
  • Ensure audit trails, metadata capture, permissions, and electronic signature routing are robust and inspection-ready.
  • Maintain interoperability and version control across tools, instruments, and datasets.
• Cross-Functional Collaboration & Technical Leadership
  • Serve as the technical lead for connecting scientific workflows to data/ML infrastructure.
  • Train scientists on digital tools, data capture best practices, and ML-enabled analysis workflows.
  • Translate scientific, regulatory, and business requirements into practical, scalable technical solutions.

PERFORMANCE MEASUREMENTS

• Year-over-year increase in number of instrumentation successfully integrated into digital data pipelines
• Uptime and reliability of d