Nonclinical Safety Computational Toxicologist, Distinguished Scientist

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Discovery & Pre-Clinical/Clinical Development

Nonclinical Safety

Scientific/Technology

San Diego, California, United States of America;
Spring House, Pennsylvania, United States of America

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We are searching for the best talent for a Nonclinical Safety Computational Toxicologist, Distinguished Scientist, in Spring House, PA or La Jolla, CA.

The Nonclinical Safety Computational Toxicologist, Distinguished Scientist, in the Preclinical Sciences and Translational Safety (PSTS) organization within J&J Research & Development, will lead application of data science and machine learning (ML) methodologies to the assessment of nonclinical safety and generation of submissions deliverables. This role emphasizes using next generation data science tools and systems to guide decisions, improve efficiency, and ensure regulatory compliance throughout the drug discovery and development pipeline.

• Global lead for nonclinical safety and submissions data science and ML initiatives for Department of Nonclinical Safety & Submissions (NCSS), ensuring strong project management and collaboration with partners within and external to PSTS (eg IT Data Science), and alignment of data science/ML initiatives with overall strategic project goals of PSTS.
• Identify, assess, recommend, and develop proposals for new data science opportunities to drive next generation nonclinical safety tools, submissions, and strategy to predict, derisk, and/or understand potential toxicities and production of high quality, compliant submission documents.
• Create innovative visualizations and user interfaces to help scientists access & interpret data and translate complex data from diverse sources into actionable insights.
• Support development of new approach methodologies (NAMs) and computational methods to achieve 3
  
  Rs in drug development.
• Understand specific business needs and translate the scientific and regulatory challenges and opportunities into smarter data innovation solutions, and drive validation strategies for model performance.
• Drive rollout, re‑enforcement, monitoring, and continuous improvement of implemented data science initiatives.
• Mentor and provide training on the use and utility of predictive ML approaches in drug safety and submissions.

• Minimum of PhD in biomedical sciences, toxicology, pharmacology, computational biology, bioinformatics, data science, or advanced degree in a relevant field is required.

• A minimum of 8 years of relevant experience is required in nonclinical drug development and submissions supporting diverse modalities, geographies, and therapeutic areas.
• Extensive experience (6+ years) in data science with a focus on machine learning, predictive modeling, data mining, statistics, deep learning, and data visualization/dashboarding frameworks.
• Proven track record of implemented data science tools…