Scientist – Quantitative Pharmacology and Machine Learning

Job Description

Job Summary:

• The Quantitative Pharmacology (QP) group at Client is seeking a Data Science contractor to develop and enhance Pharmacokinetics (PK)/Pharmacodynamics (PD) modeling, data analysis, and decision-support tools for drug discovery and development.
• The successful candidate will support the development and enhancement of quantitative pharmacology tools, including PK/PD models, interactive applications using Python and Shiny for Python, automated analytical workflows, model diagnostics and visualization, and agentic AI-enabled workflows to streamline scientific analysis and decision making.
• The role will also involve data analysis and the development of mathematical and machine learning models to support compound prioritization and early drug development decisions.
• This may include integrating molecular structures, compound descriptors, experimental data, and other relevant information to predict pharmacokinetic and pharmacological properties of small molecules.
• The successful candidate will work closely with QP scientists to develop robust, validated, reproducible, and user-friendly computational solutions including exploring agentic approaches to automate and orchestrate data analysis, model execution, interpretation, and reporting.
• The QP group supports multiple therapeutic areas and research platforms within the broader R&D organization.

Education & Experience:

• Bachelor’s degree or higher in Computer Science, Engineering, Data Science, Applied Mathematics, or a related quantitative field, with a strong background in software development and scientific computing.
• 1-3 years of experience.
• Proficiency in Python, with some experience developing interactive applications using Shiny for Python or related frameworks.
• Experience with scientific data analysis, visualization, and mathematical/statistical modeling; familiarity with PK/PD modeling, dynamical systems, time-series, or longitudinal data is a plus.

Knowledge, Skills, and Abilities:

• Familiarity with software development practices including Git, testing, documentation, and reproducible workflows.
• Familiarity with machine learning model development, evaluation, and validation, using libraries and frameworks such as Scikit-learn, PyTorch, TensorFlow, or Keras.
• Familiarity with agentic and AI-enabled workflows for automating and orchestrating data analysis, model execution, scientific interpretation, and reporting is a plus.
• Ability to work effectively in a matrixed and global environment.