Associate Director, Data Science

Job Description Summary #LI-Hybrid Internal

Title:

Associate Director

Location:

Cambridge, MANovartis is a leader in data science and model-informed drug development. We are seeking an experienced Data Science leader to advance data-driven drug discovery and development by integrating advanced analytics, machine learning, and mechanistic modelling  this role, you will partner with Pharmacokinetic Sciences (PKS) Modeling & Simulation (M&S), Translational Medicine, and multidisciplinary project teams to transform large-scale experimental datasets into actionable insights.

You will develop and apply hybrid approaches that combine machine learning with mechanistic modelling (e.g., PK/PD, QSP) to support decision-making from discovery through clinical development.

You will contribute to departmental strategy, drive innovation in AI-augmented modelling approaches, and ensure the proactive use of data science and in silico methods to guide compound progression, prioritization, and clinical decision-making.

This role reports to the Head of Data Science in the PKS M&S team within Translational Medicine in Biomedical Research.

Job Description

Key responsibilities:

Shape and advance AI-driven MIDD by integrating mechanistic modelling and machine learning to bridge biology and clinical outcomes.

Design and implement hybrid modelling pipelines where mechanistic simulations generate features for machine learning models.

Translate model-derived biomarkers and mechanistic states into clinically relevant predictions and decision-support tools.

Drive scientifically grounded AI approaches that enhance mechanistic understanding, ensuring rigor, interpretability, and robustness.

Develop scalable, reproducible workflows integrating data science, mechanistic modelling, and in-house tools.

Define and implement project-specific in silico modelling and data strategies aligned with key decision questions.

Apply and advance currently available data mining and advanced analytics to link molecular structure, ADME properties, and pharmacological outcomes across modalities.

Drive adoption and effective use of in silico models, tools, and data to accelerate decision-making.

Collaborate with PKS, Translational Medicine, and Data & Digital teams to integrate diverse datasets (preclinical, clinical, external).Contribute to translational programs across disease areas and communicate modelling insights to influence decision-making.

Stay current with advances in AI/ML and their application to ADME, PK/PD, and drug discovery and development, and proactively evaluate and bring appropriate innovation into practice to improve efficiency and scientific impact.

Essential requirements

Advanced degree in life sciences or quantitative discipline (e.g., data science, computational biology, pharmacometrics, bioinformatics, computational chemistry, biomedical engineering or related field).PhD with 5+ years or MSc with 8+ years of relevant experience in drug discovery or development.

Strong expertise in machine learning, statistics, and data science methods.

Demonstrated 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/or translational modelling concepts.

Proficiency in Python and/or R, including software development best practices (version control, testing, documentation).

Experience with machine learning libraries such as scikit-learn, PyTorch, or Keras.

Strong data visualization and exploratory data analysis skills.

Ability to translate complex analytical concepts into clear, actionable insights.

Strong collaboration and communication skills across multidisciplinary teams.

Fluency in…