Sanofi's contingent workforce program, FLEXT Direct, is seeking a Data Scientist for a 6-month contract supporting Sanofi's Quantitative Pharmacology (QP) group. Position Overview The Quantitative Pharmacology (QP) group 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 work closely with QP scientists to develop robust, validated, reproducible, and user-friendly computational solutions. This role combines Python programming, scientific data analysis, mathematical and statistical modeling, machine learning, and scientific software development. Key areas of work may include: Developing and enhancing PK/PD models and quantitative pharmacology tools Performing scientific data analysis, visualization, and model diagnostics Developing interactive applications using Python and Shiny for Python Building automated and reproducible analytical workflows Developing mathematical and machine learning models to support compound prioritization and early drug development decisions Integrating molecular structures, compound descriptors, experimental data, and other relevant information to predict pharmacokinetic and pharmacological properties of small molecules Exploring AI-enabled and agentic workflows to automate and orchestrate data analysis, model execution, interpretation, and reporting Supporting computational solutions across multiple therapeutic areas and research platforms within Sanofi's broader R&D organization Qualifications Bachelor's degree or higher in Computer Science, Engineering, Data Science, Applied Mathematics, or a related quantitative field Strong background in software development and scientific computing 1-3 years of relevant professional experience Proficiency in Python Some experience developing interactive applications using Shiny for Python or related frameworks Familiarity with software development practices, including Git, testing, documentation, and reproducible workflows Experience with scientific data analysis, visualization, and mathematical/statistical modeling Familiarity with machine learning model development, evaluation, and validation Experience with or familiarity with machine learning libraries/frameworks such as scikit-learn, PyTorch, TensorFlow, or Keras Ability to work effectively in a matrixed and global environment Preferred Qualifications Familiarity with PK/PD modeling Experience with dynamical systems, time-series, or longitudinal data Experience working with molecular structures, compound descriptors, or experimental drug-development data Familiarity with AI-enabled or agentic workflows for automating and orchestrating data analysis, model execution, scientific interpretation, and reporting Experience in pharmaceutical, biotechnology, drug discovery, or related scientific research environments