Associate Director, Data Science

Overview

In this role you lead AI-augmented modelling to bridge biology and clinical outcomes, shaping data-driven decision-making across discovery and clinical development. You will build hybrid ML-mechanistic pipelines and translate modelling insights into actionable decisions. You’ll collaborate across PKS M&S, Translational Medicine, and project teams to scale reproducible data science workflows. The opportunity sits at the intersection of biology, software, and translational medicine, driving innovation in AI-enabled drug discovery.

Pay / Benefits

• performance-based cash incentive
• equity eligibility
• comprehensive benefits package
• 401(k) with company match
• generous time off including vacation and holidays
• health, life and disability benefits

Responsibilities

• Shape AI-driven MIDD by integrating mechanistic modelling with machine learning
• Design and implement hybrid modelling pipelines that use mechanistic simulations as ML features
• Translate model-derived biomarkers into clinically relevant predictions and decision-support tools
• Advance robust, interpretable AI approaches to improve mechanistic understanding
• Develop scalable, reproducible workflows combining data science, modelling, and in-house tools
• Define project-specific in silico modelling and data strategies aligned with decision questions
• Link molecular structure, ADME properties, and pharmacological outcomes across modalities using advanced analytics
• Promote adoption of in silico models to accelerate decision-making
• Collaborate with PKS, Translational Medicine, and Data & Digital teams to integrate diverse datasets
• Communicate modelling insights to influence translational decisions
• Stay current with AI/ML advances and evaluate their application to ADME, PK/PD, and drug development
• Contribute to translational programs across disease areas and support strategy and innovation

Key requirements

• PhD with 5+ years or MSc with 8+ years in drug discovery or development
• Strong expertise in machine learning, statistics, and data science methods
• Experience applying reproducible data science approaches to drug discovery or development
• Experience combining mechanistic modelling and data-driven approaches is strongly preferred
• Strong understanding of ADME, PK/PD, and translational modelling concepts
• Proficiency in Python and/or R with software development best practices
• Experience with ML libraries such as scikit-learn, PyTorch, or Keras
• Strong data visualization and exploratory data analysis skills
• Ability to translate complex analytical concepts into clear insights
• Strong collaboration and communication skills across multidisciplinary teams
• Fluency in English (oral and written)

• Cross-functional collaboration
• Clear scientific communication
• Strategic thinking
• Python
• R
• scikit-learn

…