Principal Laboratory Automation Scientist

Johnson & Johnson Innovative Medicine•Spring House, PA

9d•Hybrid

About The Position

The Principal Laboratory Automation Scientist sets 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 work across interconnected workcells. As a domain expert in advanced laboratory automation, multi-workcell orchestration, scientific workflow transformation, data provenance, metadata strategy, and AI-ready laboratory execution, the scientist defines workflow architecture that enables autonomous discovery platforms to generate trustworthy, reproducible, model-usable data. The role partners with the Principal Automation and Robotics Engineer: the scientist owns scientific and data architecture for automated workflows, while the engineer owns robotics, controls, safety, physical automation, serviceability, and machine architecture. Together they provide the architectural partnership for self-driving laboratories. The role supports the transition from isolated automation projects to integrated, reliable, AI-native, and ultimately self-driving discovery laboratory systems, connecting scientific intent with physical execution, reproducible data, model feedback, and scalable operations. It is located in Spring House, Pennsylvania. Travel may be required for vendor assessments, FAT/SAT activities, site visits, system builds, workshops, training, technology scouting, or cross-site deployment support.

Requirements

Bachelor's degree in Life Science, Analytical Science, Engineering, Computer Science, Laboratory Automation, or a related field; an advanced degree is strongly preferred.
Relevant experience requirement: Bachelor's degree with 10+ years, Master's degree with 8+ years, or PhD with 5+ years in laboratory automation, scientific automation, analytical automation, high-throughput discovery, or workcell integration.
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 role.
Ability to translate scientific workflows into executable automated methods, worklists, run states, exception pathways, and user-facing procedures.
Working knowledge of sample, labware, reagent, and consumable identity and 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.
Travel may be required for vendor assessments, FAT/SAT activities, site visits, system builds, workshops, training, technology scouting, or cross-site deployment support.

Nice To Haves

Advanced degree.
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

Define cross-workcell laboratory automation architecture spanning scientific requests, instruments, workcells, sample and consumable stores, scheduling layers, data systems, user interfaces, and model feedback loops.
Create standards for labware, sample, reagent, and consumable identity; state models; method versioning; interface control; recovery logic; run context; data provenance; and operational handover.
Establish design patterns for request intake, automated worklist generation, queue management, workflow routing, workcell handoffs, exception handling, and data feedback into scientific decision-making.
Define scientific-workflow acceptance criteria for nominal workflows, edge cases, failure modes, recovery scenarios, operator interventions, data completeness, and lights-out readiness.
Define scientific and technical requirements for integrated automation platforms supporting biologics and small-molecule discovery, protein engineering, screening, analytical sciences, NGS, cell-based assays, biophysics, protein characterization, and translational workflows.
Resolve complex issues involving scientific method performance, liquid handling, assay suitability, automation constraints, orchestration, data flow, sample logistics, software behavior, facilities constraints, biosafety, and vendors.
Lead cross-functional investigations and improvement programs to increase reliability, scalability, data quality, throughput, scientific adoption, and model feedback utility.
Advise senior partners on workflow maturity, automation readiness, manual-versus-automated boundaries, standardization opportunities, and scientific risks before major investment decisions.
Lead throughput and queue modeling, workflow simulation, digital twin strategy, AI-ready metadata, experimental provenance, sample lineage, failure annotations, and data contracts.
Shape roadmaps for autonomous execution, physical AI integration, closed-loop experimentation, human oversight, human-intervention guardrails, and model feedback.
Define data quality expectations connecting instrument outputs, sample context, run metadata, environmental context, and scientific interpretation.
Mentor laboratory automation scientists and influence communities of practice on 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.