Principal Data Scientist - DDSAI - Agentic Lab Automation
About The Position
Johnson & Johnson Innovative Medicine is seeking a Principal Scientist, Agentic Lab Automation to help build our drug discovery labs of the future in Spring House, PA. This is an opportunity to be one of the early experts shaping a next-generation DMTA discovery engine that integrates robotic lab execution, real-time data pipelines, intelligent orchestration, and agentic AI workflows to accelerate how we discover and develop new medicines. In this role, you will work at the intersection of scientific experimentation, lab automation, AI systems, and digital infrastructure, helping transform today's human-in-the-loop processes into more scalable, connected, and intelligent discovery workflows. You will partner across Therapeutics Discovery, Data Science, and IT to enable laboratory systems that generate high-value data, improve scientific learning with every cycle, and unlock new speed, quality, and strategic advantage for our pipeline. This role is ideal for a technically deep and creatively ambitious scientist/engineer who wants to help build our future discovery engine, not just automate steps, but architect how scientific and physical AI come together to steer design and execution in the lab. This is a rare chance to build a closed-loop AI driven discovery engine in Spring House, PA, one of JnJ’s key Discovery hubs. The role will contribute to a broader shift toward automation engineering and AI-enablement that can improve the quality and speed of our molecules to strengthen long-term competitive advantage.
Requirements
- Ph.D. (or equivalent advanced degree) in Engineering, Automation, Robotics, Computer Science, Biomedical Engineering, Chemical Engineering, Systems Engineering, Computational Biology, or a related field.
- Significant industry experience building, integrating, or scaling laboratory automation, AI-enabled experimentation platforms, or autonomous workflow systems in pharma, biotech, advanced R&D, or adjacent technical environments.
- Demonstrated expertise in one or more of the following: robotic lab systems, scientific workflow orchestration, laboratory informatics, experimental platform integration, automation software, or cyber-physical systems for R&D.
- Experience designing or supporting real-time or near-real-time data pipelines and integrating heterogeneous scientific data and instrument outputs.
- Ability to work directly with experimental scientists and translate scientific objectives into practical, scalable technical solutions.
- Strong communication, problem-solving, and stakeholder management skills, with comfort operating in ambiguity and shaping new capabilities at scale.
Nice To Haves
- Experience applying agentic AI, AI/ML, optimization, or intelligent decision systems to scientific or laboratory workflows.
- Familiarity with MLOps/DevOps, workflow engines, and production-grade monitoring/observability.
- Understanding of FAIR data, ontologies, semantic models, lineage/provenance, or AI-ready scientific data standards.
- Experience in drug discovery domains such as small molecules, biologics, peptide discovery, high-throughput experimentation, imaging, or human-relevant model systems.
Responsibilities
- Translate scientific priorities into automation and AI roadmaps, connecting tactical platform work to long-term discovery goals.
- Design, configure, integrate, and continuously improve the AI execution of robotic lab systems that support high-throughput data generation.
- Build or oversee the development of real-time pipelines connecting automation software, data stores, models, and compute environments.
- Partner with IT and platform teams to implement resilient APIs, observability, versioning, and workflow orchestration for end-to-end discovery processes.
- Design and implement laboratory workflows and experiments optimized for AI-driven learning, not just throughput or task automation.
- Collaborate with AI/ML scientists to enable closed-loop feedback between each step in the DMTA process, ensuring experimental outputs improve downstream models and decision-making.
- Identify opportunities to apply agentic AI and intelligent automation to compress cycle time, reduce manual intervention, and improve reproducibility.
- Ensure data generated through automated workflows is high-quality, traceable, interoperable, and AI-ready, with strong metadata, provenance, lineage, and governance practices.
- Collaborate with data engineers, ontology/knowledge engineers, and platform teams to standardize assay outputs, semantically enrich datasets, and support discoverability across modalities.
- Contribute to architectures that support vectorized, semantically searchable, and reusable scientific data assets across discovery workflows.
- Serve as a senior technical partner across Discovery, Data Science, and IT to align scientific goals with Agentic orchestration.
- Help quickly shape standards, priorities, and new ways of working.
- Evaluate emerging vendors, tools, and external capabilities relevant to robotics, AI orchestration, and AI-enabled lab execution.
Benefits
- Vacation –120 hours per calendar year
- Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
- Holiday pay, including Floating Holidays –13 days per calendar year
- Work, Personal and Family Time - up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52-week rolling period
- 10 days Volunteer Leave – 32 hours per calendar year
- Military Spouse Time-Off – 80 hours per calendar year
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
Ph.D. or professional degree
