Senior Scientist, Data (AI) Scientist, Translational Safety

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 jnj.com As guided by Our Credo, Johnson & Johnson 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 & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: Scientific/Technology All Job Posting Locations: Cambridge, Massachusetts, United States of America, Horsham, Pennsylvania, United States of America, New Brunswick, New Jersey, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America Job Description: About Innovative Medicine 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 Position Summary Senior Scientist – Data (AI) Scientist, Translational Safety to join the Data, Data Science & Artificial Intelligence (DDSAI) – OCMO (Office of Chief Medical Officer) organization helping accelerate drug safety prediction across all stages of drug discovery and development, using advanced AI/ML analytics and multimodal modeling of biological (preclinical and clinical) and RWE data. The Senior Translational AI Scientist will develop predictive models that identify translational-biomarkers and flag compounds with high translational-safety risk, enabling optimal risk minimization supporting patient benefit/risk decisions. One exciting opportunity will be to support the development of AI-enabled reasoning capabilities and Foundation model s that connect discovery, preclinical, clinical, and real-world evidence domains to accelerate translational safety predictions. This role will partner closely with pharmaceutical scientists across all phases as well as other Data Scientists from Knowledge Engineering and Data Products to transform harmonized, AI-ready data assets into actionable scientific insights that improve decision-making across the R&D lifecycle. Mission Develop scientifically credible AI and machine learning capabilities and models that enable earlier prediction of safety and efficacy outcomes and support closed-loop learning across drug discovery and development. Strategic rationale (why this role matters) Builds the capability for AI-driven, translationally-focused predictive models that identify safety biomarkers and flag high translational-risk compounds earlier in the R&D lifecycle in the forward direction, and also support reverse-translation of AE (Adverse event) Signals from RWE by feeding back causal inference insights to preclinical and clinical. Directly supports faster, evidence-based go/no‑go and risk‑minimization decisions, increasing program productivity and protecting patient safety. Enables development of cross-domain Foundation models and AI reasoning that connect discovery → preclinical → clinical → RWE, creating reusable IP and accelerating future projects. Key responsibilities Design, build, validate and deploy AI/ML solutions for translational safety prediction using multimodal data across discovery, preclinical, clinical and real‑world evidence (RWE) domains. Develop predictive models and AI‑reasoning frameworks for translational safety, biomarker identification, mechanistic inference and clinical outcome prediction; evaluate traditional ML, deep learning, causal inference and foundation‑model approaches. Integrate heterogeneous, high‑dimensional datasets (e.g., high‑content imaging, phenomics, transcriptomics, proteomics, EHR, claims) to derive novel biological insights that de‑risk safety signals and inform portfolio decisions. Establish scientific validation : assess biological plausibility, benchmark model performance, produce explainability and validation packages suitable for regulatory and cross‑functional review. Prototype and advance Foundation‑model connectors and AI‑enabled reasoning to link discovery → preclinical → clinical → RWE and support closed‑loop learning across R&D. Collaborate closely with translational scientists, toxicology, clinical safety, PV, Knowledge Engineering, Data Products and external partners to prioritize use cases, operationalize models and drive productization. Communicate complex technical methods and results clearly to diverse audiences and stakeholders; maintain reproducible code, documentation and up‑to‑date versioned repositories. Qualifications Education: Ph.D. preferred in Computational Biology, Bioinformatics, Biomedical Informatics, Computer Science, Statistics, Applied Mathematics or a related quantitative discipline; or equivalent experience. Experience: demonstrated experience applying AI/ML in life‑sciences settings (industry or post‑doc); typically 2+ years post‑PhD or ~3–5 years relevant industry experience. Domain expertise: track record in translational science, biomarker discovery, safety assessment or related drug‑discovery applications. Publication history or demonstrated contributions to top‑tier conferences/journals preferred. Technical skills: Strong programming proficiency, preferably Python, and experience with AI frameworks (PyTorch or TensorFlow). Deep knowledge of ML/DL methods (Transformers, CNNs, graph networks, self‑supervised and multi‑instance learning), causal inference and graph analytics. Experience with multimodal representation learning, foundation models, LLMs/GraphRAG and multimodal data fusion. Practical experience analyzing imaging/microscopy, multi‑omics and real‑world clinical data at scale. Capabilities & behaviors: excellent analytical thinking, scientific rigor, strong written and oral communication, collaborative cross‑functional influence, and the ability to translate domain questio