Senior Scientist, Data; AI) Scientist, Translational Safety

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Data Analytics & Computational Sciences

Data Science

Scientific/Technology

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

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.

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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.

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.

• 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,…