Vice President, R&D Data Science – Discovery, Product Development and Supply (DPDS)

• Strategic Leadership: Design, deploy and grow data science and AI capabilities within DPDS that support and accelerate the overall vision and objectives of the division. Collaborate closely with DPDS teams in Therapeutic Discovery (TD) Preclinical Sciences and Translational Safety (PSTS) as well as Therapeutics Development & Supply (TDS) to outline both short-term and long-term data science projects, and accountable for prioritizing and deploying needed capabilities with a clear roadmap (that includes data strategy, AI/ML, gen AI, and AI research) to support their mission.
• Servant Leadership: Work with the DPDS senior leadership team to pinpoint critical areas where data science can lead to substantial business value. Adapt model of support and capability mix to the unique needs and data readiness of the divisions, ensuring scientific impact and alignment with DPDS goals.
• Data Science and Technology Interface: Foster and grow strong collaboration between quantitative scientists within the DSDH and DPDS teams, maintaining accountability for the data science portfolio within DPDS, including project prioritization, resource allocation, and deliverables. Integrate and synchronize the technology roadmap with the data science and AI roadmap in support of the business needs.
• Biological Insights: Partner with PSTS scientists to enable them to derive biological insights and data analytics by integrating molecule attribute data alongside biological data related to on- and off-target pathways, pharmacokinetics (PK), and in silico, in vitro, and in vivo clinical data to inform translational PK-PD and safety.
• Therapeutics Development Insights: Partner with TDS scientists to drive therapeutic development, platform and delivery insights, optimizing process development and scale-up, clinical supply from across DPDS and IMSC to enhance product knowledge, optimize processes, and control supply chains adaptively.
• External Collaborations: Engage with the DPDS and DSDH External Innovation and Alliance teams to identify, evaluate, and partner with academic and external entities to leverage cutting-edge data and analytic methodologies.
• Therapeutic Program Delivery:  Partner with data science leaders in DSDH and DPDS to advance therapeutic programs to clinical stages and industrialize the processes, utilizing machine-learning-driven optimization techniques throughout each phase.
• Matrix Leadership: Lead within a highly matrixed environment involving cross-functional teams, including DPDS and TA teams, scientists, data scientists, engineers, and external partners. Establishing a strategic partnership and model of collaboration with R&D IT is critical.
• Community Engagement: Assume a leadership position within the Johnson & Johnson IM R&D data science community.

• A minimum of 14 years of relevant experience in discovery and development within the biopharma industry or academia is required.
• Proven vision for transforming discovery, development, and supply through predictive biology, including target identification, validation, and understanding disease mechanisms using computational modeling and AI/ML techniques. Familiarity with technical processes in drug discovery and development are required.
• A PhD in Computational Biology, Chemistry, Computational Chemistry, Machine Learning or Translational Medicine or similar field of study is required
• A track record of participation or leadership in multiple active discovery/preclinical candidate program teams, working alongside leaders across various disciplines.
• Experience in hiring, managing, and mentoring teams focused on target discovery and development activities within a matrixed structure is essential.
• In-depth understanding and proven experience with drug discovery, development, and manufacturing processes.
• Strong project leadership capabilities and the ability to influence and engage with strategic and technical/scientific stakeholders.
• A distinguished history of collaboration and leadership within matrix organizations.
• Exceptional communication, interpersonal, and writing skills.
• Comprehensive knowledge of machine learning and AI algorithms (supervised, unsupervised learning, deep neural networks, large language models, etc.) within biomedical or healthcare research environments.

• This role focuses on discovery and product development; and industry experience is expected. Familiarity with clinical development, post-marketing data science, or data analytics and infrastructure is preferred, though a strong appreciation for data handling in regulated environments is key.
• Broad knowledge in AI/ML de-novo & applied algorithmic development.
• Expertise in contemporary AI/ML methodologies, including various deep learning architectures (e.g., Transformers, encoder-decoder models, CNNs), generative AI for different modalities (NLP, imaging, genomics), graph ML, causal inference, alongside techniques such as one-shot learning, transfer learning, diffusion models, reinforcement learning, systems modeling, and active learning as it relates to target discovery and development.

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Johnson & Johnson is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin or protected veteran status and will not be discriminated against on the basis of disability.

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