Principal Laboratory Automation Scientist

Johnson & Johnson Innovative Medicine • Spring House, PA

3d • Hybrid

About The Position

The Principal Laboratory Automation Scientist sets the scientific automation architecture for integrated discovery platforms. The role defines how scientific requests, sample states, consumables, automated methods, data provenance, UAT, metadata, exception logic, and user adoption should work across interconnected workcells. The Principal Scientist will serve as a domain expert for advanced laboratory automation, multi-workcell orchestration, scientific workflow transformation, data provenance, metadata strategy, and AI-ready laboratory execution. They will define the scientific workflow architecture that enables autonomous discovery platforms to generate trustworthy, reproducible, model-usable data. This role is paired with the Principal Automation and Robotics Engineer. The scientist owns scientific and data architecture for automated workflows; the engineer owns robotics, controls, safety, physical automation, serviceability, and machine architecture. Together they provide the core architectural partnership for self-driving laboratories.

Requirements

• A minimum of a Bachelor's degree in Life Science, Analytical Science, Engineering, Computer Science, Laboratory Automation, or a related field is required.
• Bachelor's degree with 10+ years, Master's degree with 8+ years, or PhD with 5+ years of relevant laboratory automation, scientific automation, analytical automation, high-throughput discovery, or workcell integration experience.
• Demonstrated understanding of laboratory workflows, sample handling, plate/tube formats, assay execution, consumable preparation, run documentation, and data capture.
• Hands-on or practical experience with automated liquid handlers or integrated laboratory automation equipment appropriate to the level of the role.
• Ability to translate scientific workflows into executable automated methods, worklists, run states, exception pathways, and user-facing procedures.
• Working knowledge of sample identity, labware identity, reagent identity, consumable state, barcode traceability, metadata capture, and downstream data requirements.
• Ability to collaborate with scientists, automation engineers, IT, data science, AI, EHS, facilities, vendors, and laboratory operations.
• Recognized expertise in laboratory automation, integrated workcells, assay automation, workcell orchestration, scientific workflows, and data flow.
• Ability to define architecture, standards, interface models, recovery logic, and data requirements across multidisciplinary teams.

Nice To Haves

• An advanced degree is strongly preferred.
• Experience with SDLs, digital twins, FAIR data, AI-ready metadata, SiLA 2, OPC UA, APIs, event-driven architectures, microservices, workflow engines, and Tier 1 integrator models.
• Experience with biologics discovery, small molecule discovery, protein engineering, high-throughput screening, analytical sciences, NGS, cell-based assays, and model feedback workflows.
• Record of scientific or technical innovation through platform deployments, invention disclosures, patents, publications, or external collaborations.

Responsibilities

• Defining cross-workcell laboratory automation architecture spanning scientific requests, instruments, workcells, sample stores, consumable stores, scheduling layers, data systems, user interfaces, and model feedback loops.
• Creating standards for labware, sample identity, reagent identity, consumable identity, state models, method versioning, interface control, recovery logic, run context, data provenance, and operational handover.
• Establishing design patterns for request intake, automated worklist generation, queue management, workflow routing, workcell handoffs, exception handling, and data feedback into scientific decision making.
• Defining acceptance criteria for nominal workflows, edge cases, failure modes, recovery scenarios, operator interventions, data completeness, and lights-out readiness from a scientific workflow perspective.
• Defining scientific and technical requirements for integrated automation platforms supporting biologics discovery, small molecule discovery, protein engineering, screening, analytical sciences, NGS, cell-based assays, biophysics, protein characterization, and translational workflows.
• Resolving complex issues spanning scientific method performance, liquid handling, assay suitability, automation constraints, orchestration, data flow, sample logistics, software behavior, facilities constraints, biosafety, and vendors.
• Leading cross-functional investigations and improvement programs that increase reliability, scalability, data quality, throughput, scientific adoption, and model feedback utility.
• Advising senior partners on workflow maturity, automation readiness, manual versus automated boundaries, standardization opportunities, and scientific risks before major investment decisions.
• Leading throughput modeling, queue modeling, workflow simulation, digital twin strategy, AI-ready metadata, experimental provenance, sample lineage, failure annotations, and data contracts.
• Shaping roadmaps for autonomous execution, physical AI integration, closed-loop experimentation, human oversight, human intervention guardrails, and model feedback.
• Defining data quality expectations that connect instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.
• Mentoring laboratory automation scientists and influence communities of practice around method robustness, data provenance, automated workflow design, and adoption.
• Support technology evaluations, proof-of-concept studies, workflow readiness assessments, user demonstrations, and vendor evaluations for future automation capabilities.
• Contribute to laboratory automation standards, workflow templates, data handoff expectations, training materials, and playbooks.
• Support audit, inspection, or quality documentation requests where automated scientific workflows intersect with regulated or quality driven environments.
• Participate in lessons learned and continuous improvement activities to improve future automation delivery and adoption.

Benefits

• This role supports the transition from isolated automation projects to integrated, reliable, AI-native, and ultimately self-driving discovery laboratory systems.
• The work helps connect scientific intent with robust physical execution, reproducible data, model feedback, and scalable operational capability.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal