Scientist, Safety Pharmacology

Merck Merck · Pharma · PA

The Scientist, Safety Pharmacology role at Merck involves analyzing physiologic signals using signal processing, machine learning, and AI to develop new in vivo analysis tools and contribute to risk assessment evaluations for drug development. The role focuses on building and refining analysis tools and workflows for nonclinical data within the healthcare domain.

What you'd actually do

  1. Analyzing physiologic signals to contribute to risk assessment evaluations and developing data visualizations for scientific exploration
  2. Building new in vivo analysis tools (signal processing, machine learning, AI) for nonclinical data and refining or developing new endpoints
  3. Working with external vendors to identify and incorporate potential solutions
  4. Collaborating with internal data science and IT teams to implement data tools
  5. Integrating new analysis workflows into SEP

Skills

Required

  • Algorithm Development
  • Data Interpretations
  • MATLAB
  • Pharmacology
  • Physiological Characteristics
  • Python (Programming Language)
  • R Programming
  • Safety Pharmacology
  • Signal Analysis

Nice to have

  • nonclinical data acquisition
  • machine learning and/or artificial intelligence tools and workflows
  • statistical and data visualization tools and libraries
  • cloud computing environments
  • cardiovascular, neuroscience, or respiratory endpoints
  • data science best practices and reproducibility
  • multidisciplinary environment within the Pharmaceutical Industry
  • external consortiums, publications, and/or scientific presentations

What the JD emphasized

  • Experience with nonclinical signal analysis and/or algorithm development
  • Proficiency with programming tools (Python, MatLab, R) for physiological signal analysis, feature extraction, and data interpretation
  • Experience with machine learning and/or artificial intelligence tools and workflows

Other signals

  • Building new in vivo analysis tools (signal processing, machine learning, AI) for nonclinical data and refining or developing new endpoints
  • Analyzing physiologic signals to contribute to risk assessment evaluations and developing data visualizations for scientific exploration
  • Integrating new analysis workflows into SEP