Job Function:
Data Analytics & Computational Sciences

Job Sub Function:
Data Science

Job Category:
Scientific/Technology

All Job Posting Locations:
Spring House, Pennsylvania, United States of America

Job Description:

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at https://www.jnj.com/innovative-medicine

Johnson & Johnson Innovative Medicine is recruiting for a Principal Scientist, AI & Autonomous Discovery Systems, to advance AI-powered drug discovery through human-guided autonomous and agentic discovery platforms and help accelerate drug discovery by creating AI-enabled Design-Make-Test-Learn ecosystems that improve scientific decision quality, increase experimental throughput, reduce cycle times, and advance high-value therapeutic programs. The primary location for this position is Spring House, PA. (No remote option.)

The Data, Data Science, and AI team develops AI solutions across therapeutic areas, including oncology, cardiovascular and metabolic disorders, immunology, neuroscience, and infectious disease. We are seeking a technical leader at the intersection of AI/ML, drug discovery, laboratory automation, and scientific platform architecture.

This role will help shape the future of AI-enabled research at Johnson & Johnson by creating intelligent systems that connect models, data, experiments, laboratories, and scientists into continuously learning discovery ecosystems. These capabilities will accelerate the discovery and development of transformational medicines across therapeutic areas.

Key Responsibilities

• Build AI-enabled discovery workflows that connect target discovery, molecular/property prediction, molecular design, experiment planning, assay optimization, and data interpretation across modalities, therapeutic areas, and functional groups.
• Define the technical vision, long-term strategy, and scalable multi-agent architecture for AI-driven laboratory orchestration and human-guided autonomous discovery systems, coordinating scientific reasoning, knowledge retrieval, experiment planning, model execution, workflow automation, and decision support while influencing investments across discovery platforms, therapeutic areas, and enterprise AI initiatives.

• Connect AI models, scientific applications, data platforms, laboratory systems, instruments, robotics, cloud services into coherent Design-Make-Test-Learn workflows that continuously learn from experimental outcomes, assay results, and scientific feedback.

• In collaboration with generic agentic system providers, develop intelligent scientific systems for drug discovery and translational research that integrate experimental data, scientific literature, domain knowledge, and predictive multimodal AI models to advance hypothesis generation, experimental design, causal reasoning, active learning, multi-objective optimization, and scientific decision-making.

• In collaboration with broader teams, contribute to the establishment of rigorous standards for AI governance, safety, observability, reliability, auditability, data governance, security, and responsible AI, and evaluate systems against scientific, operational, and business outcomes through experimental validation and real-world deployment.

• Partner with scientists and engineers across drug discovery, data science, automation, and technology organizations to deliver production-grade AI capabilities.

• Mentor scientists and engineers; communicate technical strategy and results through internal presentations, publications, patents, conferences, and collaborations.

Qualifications

• Ph.D. in Biomedical / Electrical / Chemical Engineering, Computer Science, Machine Learning, Computational Biology, Bioinformatics, Computational Chemistry, or a related quantitative discipline.

• At least 3 years of post-graduate experience applying advanced AI/ML technologies in scientific, pharmaceutical, biotechnology, healthcare, or industrial research settings.

• Strong expertise in agentic AI, scientific computing, and modern software engineering, machine learning, generative AI, foundation models, multimodal AI, with proficiency in Python and frameworks such as PyTorch, TensorFlow, JAX, or equivalent platforms.

• Demonstrated ability to architect enterprise-scale AI platforms, scientific software systems, or production-grade AI applications.

• Experience with drug discovery or clinical development, with ability to partner effectively with domain scientists and experimental teams.

• Excellent written and verbal communication skills; ability to work independently and collaboratively in a matrixed organization.

Preferred Qualifications

• Experience designing human-guided autonomous or closed-loop scientific workflows that connect active learning, laboratory automation, robotics, scientific data platforms, laboratory information systems, knowledge repositories, cloud infrastructure, and external scientific services.

• Experience leveraging biological, molecular, multimodal, or scientific foundation model platforms for drug discovery, including model adaptation, fine-tuning, evaluation, and deployment in production scientific workflows.

• Demonstrated experience defining modular architectures, interoperability standards, governance patterns, and production engineering approaches for enterprise scientific AI platforms, including retrieval-augmented generation, knowledge graphs, MLOps or LLMOps, cloud computing, distributed systems, APIs, and integration across computational and laboratory environments.

• Experience with analytical instrumentation used in pharmaceutical or process chemistry settings, including LC, LC/MS, and NMR, as well as familiarity with laboratory automation platforms, including LIMS, ELN, and scientific data management systems.

Training Opportunities

• Gain experience advancing AI and autonomous discovery within the pharmaceutical industry.

• Lead cross-functional work across data science, discovery science, technology, and therapeutic area organizations.

• Build external visibility through scientific publications, patents, conferences, and collaborations.

• This position will be based in Spring House, PA. (No fully remote option.)

The anticipated base pay range for this position is $117,000 to $201,250. The Company maintains highly competitive, performance-based compensation programs. Under current guidelines, this position is eligible for an annual performance bonus in accordance with the terms of the applicable plan. The annual performance bonus is a cash bonus intended to provide an incentive to achieve annual targeted results by rewarding for individual and the corporation's performance over a calendar/performance year. Bonuses are awarded at the Company's discretion on an individual basis. Employees and/or eligible dependents may be eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance.

Employees may be eligible to participate in the Company's consolidated retirement plan (pension) and savings plan (401(k)).

Employees are eligible for the following time off benefits:

Vacation - up to 120 hours per calendar year

Sick time - up to 40 hours per calendar year

Holiday pay, including Floating Holidays - up to 13 days per calendar year of Work, Personal and Family Time - up to 40 hours per calendar year

Additional information can be found through the link below. https://www.careers.jnj.com/employee-benefits

The compensation and benefits information set forth in this posting applies to candidates hired in the United States. Candidates hired outside the United States will be eligible for compensation and benefits in accordance with their local market.

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Required Skills:

Preferred Skills:
Advanced Analytics, Coaching, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Organizing, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis