Data Scientist, Associate Director (Remote)

Johnson & Johnson Innovative Medicine is currently seeking a Principal Scientist, Generative Deep Learning to join our In Silico Proteins team within the Therapeutics Discovery organization, with a preference for this individual to be located at one of our sites in Spring House, PA, or Cambridge, MA. Remote work options in the US may be considered on a case-by-case basis. At Johnson & Johnson, 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 MedTech, 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 https://www.jnj.com/.For more than 130 years, diversity, equity & inclusion (DEI) has been a part of our cultural fabric at Johnson & Johnson and woven into how we do business every day. Rooted in Our Credo, the values of DEI fuel our pursuit to create a healthier, more equitable world. Our diverse workforce and culture of belonging accelerate innovation to solve the world’s most pressing healthcare challenges.We know that the success of our business – and our ability to deliver meaningful solutions – depends on how well we understand and meet the diverse needs of the communities we serve. Which is why we foster a culture of inclusion and belonging where all perspectives, abilities and experiences are valued and our people can reach their potential.At Johnson & Johnson, we all belong.This role presents a great opportunity to spearhead our molecular design and simulation initiatives, supporting and accelerating our drug discovery and development pipeline of protein-based therapeutics while collaborating with a passionate team of scientists and engineers. Your work will be pivotal in building, evaluating, refining, and applying sophisticated computational approaches and infrastructures. Your efforts will drive the discovery of complex molecules and expedite the development of protein therapeutics across various modalities and indications.Key Responsibilities:In this pivotal role, you will collaborate extensively with interdisciplinary teams across the organization to develop and refine deep learning methodologies to support the design of ground breaking therapeutic molecules for a broad spectrum of clinical indications across all therapeutic areas and protein-based modalities. The preferred candidate will have an extensive background in deep learning and a desire to apply this knowledge to protein design.• Lead the development, refinement, and fine-tuning of generative models, such as RFDiffusion, ProteinMPNN, and EvoDiff to enhance our capabilities in protein design.• Implement new structure-based protein design methods using the latest advancements in AI - including diffusion, flow-matching, Graph Neural Networks (GNNs), Variational Autoencoders (VAEs), and other deep learning architectures.• Integrate Multi-Property Optimization techniques into various generative methods to improve the efficacy and specificity of target molecules.• Employ contrastive learning techniques to improve model adaptability and performance, particularly in differentiating functional protein structures.• Collaborate with multi-functional teams to translate sophisticated scientific challenges into actionable strategies and ensure the alignment of AI tools with scientific goals.• Maintain up-to-date knowledge of the latest trends in AI/ML, particularly in generative and discriminative modeling techniques, evaluating their potential impact on our research and development efforts.• Work with external innovation partners to collaborate on pioneering developments in generative protein design.