Senior Scientist, AI Lead

Johnson & Johnson•Malvern, PA

3d•Hybrid

About The Position

Johnson & Johnson is seeking a Senior Scientist, AI Lead to join its Cell and Gene Therapy Team in Malvern, Pennsylvania. The role will help shape the Cell and Gene Therapy R&D organization by identifying scientific challenges and connecting them to appropriate AI solutions. It calls for a strong scientific background in chemical engineering, biochemistry, or a related field, together with broad knowledge of AI approaches and their responsible use. The AI solution lead begins with the scientific need rather than a predetermined technical tool, and works across internal and external networks to advance how the organization develops its Cell and Gene Therapy portfolio. The role leads applied AI initiatives from use-case definition through implementation, partnering with scientific, data science, technology, business, and external teams. It covers solution design and evaluation, adoption into scientific workflows and platforms, responsible AI practices, portfolio and roadmap contributions, and measurement and communication of scientific and operational impact. Domestic and international travel of up to 15% is expected.

Requirements

A minimum of a BS degree in a core science or engineering field, including biology, chemistry, chemical or biochemical engineering, biomedical engineering, or a related discipline, with at least 4 years of relevant experience; OR an MS or equivalent with at least 2 years of relevant experience; OR a PhD or equivalent with 0–1 year of relevant experience.
At least 2 years of academic or industry experience delivering AI, machine learning, data science, or AI solution architecture initiatives.
Demonstrated experience leading complex projects from use-case definition through implementation.
Working knowledge of multiple AI approaches, such as supervised and unsupervised learning, deep learning, generative AI, large language models, agentic AI, natural language processing, computer vision, knowledge graphs, reinforcement learning, or hybrid modeling.
Ability to translate scientific questions into AI solution requirements and technical outputs into actionable scientific or business decisions.
Strong interpersonal and communication skills, with the ability to influence diverse audiences.
Strategic insight, strong personal leadership skills, attention to detail, and a record of effective collaboration with diverse team members.

Nice To Haves

Experience applying AI, machine learning, data science, or AI solution architecture approaches to pharmaceutical or life sciences problems.
Hands-on experience developing or prototyping AI/ML solutions using Python, R, or comparable tools.
Experience with generative AI, retrieval-augmented generation, AI agents, multimodal models, or scientific knowledge graphs.
Experience with process modeling, mechanistic or hybrid modeling, predictive analytics, optimization, or AI-enabled digital twins.
Experience integrating AI with laboratory data, scientific software, connected equipment, or automated workflows.
Experience assessing model performance, data quality, explainability, limitations, and fit-for-purpose use.
Experience working in biotechnology, pharmaceuticals, Cell and Gene Therapy, biologics development, or a related scientific environment.

Responsibilities

Lead cross-functional AI projects supporting Cell and Gene Therapy development priorities.
Define project scope, plans, resources, milestones, decision points, and measurable success criteria.
Coordinate cross-functional teams and external partners; manage risks, dependencies, decisions, and stakeholder communication.
Lead the design, prototyping, testing, and iteration of AI solutions, including machine learning, generative and agentic AI, natural language processing, computer vision, knowledge graphs, predictive analytics, and AI-enabled digital twins.
Evaluate solutions for model performance, scientific validity, usability, reliability, and business value.
Integrate successful solutions into scientific workflows, data platforms, digital twins, and automated laboratory environments, with reuse and scale in mind.
Embed responsible AI and lifecycle practices, including fit-for-purpose validation, transparency, privacy, security, governance, human oversight, performance monitoring, and clear communication of model limitations.
Partner with process development, analytical, manufacturing, and business teams to identify scientific and operational challenges that could benefit from AI.
Translate domain needs into clear AI use cases, user requirements, data needs, and expected outcomes.
Assess use cases based on scientific value, business impact, data readiness, technical feasibility, risk, and scalability.
Serve as a key interface between scientific subject matter experts, data science, and technology teams.
Build productive relationships with internal data science teams, technology partners, academic collaborators, and vendors.
Bring external developments and best practices into the organization while avoiding fragmented or duplicative solutions.
Contribute to prioritization and evolution of the Cell and Gene Therapy AI roadmap.
Define and track adoption, scientific impact, cycle-time, quality, and efficiency measures.
Share outcomes, lessons learned, and reusable practices to accelerate organizational adoption.
Promote a culture in which AI augments scientific expertise and accelerates learning while preserving appropriate human judgment.