Principal Data Scientist - DDSAI - Agentic Lab Automation

At Johnson & J&N, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

Learn more at

As guided by Our Credo, Johnson & J&N is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & J&N, we respect the diversity and dignity of our employees and recognize their merit.

Data Analytics & Computational Sciences

Data Science

Scientific/Technology

Spring House, Pennsylvania, United States of America

Johnson & J&N Innovative Medicine is recruiting for Principal Data Scientist, Agentic Lab Automation. The primary location for this position is Spring House, PA.

J&N Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market - from patients to practitioners and from clinics to hospitals.

To learn more about Johnson & J&N Innovative Medicine visit

Johnson & J&N 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.

• 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…