Overview Sr Principal Scientist – AI and Data Scientist for Safety Sciences at Johnson & Johnson Innovative Medicine. Join to apply for the Sr Principal Scientist – AI and Data Scientist for Safety Sciences role at Johnson & Johnson Innovative Medicine. At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation enables us to pursue breakthroughs across Innovative Medicine and MedTech, delivering smarter, less invasive treatments and personal solutions. Job Description We are seeking a Senior Principal Scientist with expertise in artificial intelligence (AI), machine learning (ML), advanced analytics and data integration to join our Translational Safety Pharmacology team within Preclinical Sciences & Translational Safety. The candidate will develop, deploy and implement AI/ML and data science approaches to analyze and integrate diverse preclinical datasets including but not limited to safety pharmacology (in vitro/ex-vivo/in vivo, clinical), off/on target pharmacology, toxicology, biomarkers, Pharmacokinetics/Pharmacodynamics, etc. The Senior Principal Scientist will deploy AI/ML and data science solutions to enable safer molecule design earlier, support hypothesis generation from multi-modal data, and accelerate translational insights across preclinical and clinical safety functions as well as across drug discovery. You will empower scientific research as a technology authority, collaborating with scientists, engineers and business partners to meet business needs in the preclinical environment by developing and leading innovative AI strategies and solutions. Key Responsibilities Develop and deploy AI/ML models and data science solutions to integrate and analyze multi-parametric preclinical data (e.g., in vitro, ex vivo, in vivo pharmacology, toxicology, clinical, ADME, efficacy datasets). Propose new technical strategies addressing business challenges. Independently develop solutions for preclinical data aggregation, interpretation, processing, reporting and visualization of results and analytics. Deploy quality control measures and encourage user adoption of developed solutions. Build end-to-end AI workflows and solutions that identify safety signals, mechanistic liabilities, or translational risk patterns across diverse data sources; design algorithms to extract signals from complex biological datasets. Collaborate cross-functionally to align AI/ML initiatives with project goals and drive impactful decision-making. Partner with IT Data Science teams to craft data architecture, ontologies, and data pipelines into an ML-ready format. Translate scientific and regulatory challenges into AI solutions and drive validation strategies for model performance; ensure technical feasibility of proposed models. Contribute to regulatory documentation by generating mechanistic insights into safety hazards/risks and predictive safety assessments using AI tools that meet regulatory standards. Lead evaluations of novel computational methods (e.g., GenAI, multimodal findings) for preclinical safety applicability. Provide expertise and mentorship on data science methodologies and AI/ML algorithm development. Stay at the forefront of AI/ML applications in drug discovery and safety science and represent the function at internal and external meetings. Qualifications Master’s degree in Computer Science or related field (Computational Biology, Bioinformatics, Data Science, AI, Bioengineering or related discipline) with programming/data science experience required. PhD in Computer Science or related field is highly preferred, with experience in programming and data science (preferably in the pharmaceutical field). Experience and Skills Minimum of Master’s with 8 years of proven data science experience in industry or PhD with 5+ years postdoctoral experience; track record in applying and managing ML, deep learning, or NLP in pharmaceutical (preferable) or similar settings. Proficiency in programming (e.g., Python, SQL, SAS, JavaScript) and hands-on experience with AI/ML and statistical/analytical modeling (e.g., regression, clustering, neural networks). Proficiency in data visualization and dashboarding frameworks (e.g., Spotfire, Tableau, R, R-Shiny, Power BI). Understanding of drug discovery and development workflows, especially preclinical safety pharmacology and toxicology data, in vitro assays and in vivo models is advantageous. Strong ability to translate data needs into technical solutions aligned with portfolio questions. Experience with data integration from multiple sources and working with large, disparate datasets (including text mining of reports and external datasets). Excellent communication skills and a collaborative team mindset. Preferred Experience in Biology or Preclinical Pharma industries; familiarity with biomedical terminology. Experience with regulatory submissions (e.g., SEND) and healthcare informatics is preferable. Additional Seniority level: Not Applicable Employment type: Full-time Job function: Education and Training Industries: Pharmaceutical Manufacturing EEO and other required statements remain as applicable. #J-18808-Ljbffr