Associate Director, Data Science
About The Position
Novartis 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 approaches. In 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.
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 English (oral and written).
Nice To Haves
- Artificial Intelligence (AI)
- Biostatistics
- Business Value Creation
- Change Management
- Curious Mindset
- Data Governance
- Data Literacy
- Data Quality
- Data Science
- Data Visualization
- Deep Learning
- Graph Algorithms
- Learning Agility
- Machine Learning (ML)
- Machine Learning Algorithms
- Nlp (Neuro-Linguistic Programming) And Genai
- Organization Awareness
- Pandas (Python)
- Python (Programming Language)
- R (Programming Language)
- Sql (Structured Query Language)
- Stakeholder Engagement
- Statistical Analysis
- Time Series Analysis
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.
Benefits
- health, life and disability benefits
- a 401(k) with company contribution and match
- a variety of other benefits
- generous time off package including vacation, personal days, holidays and other leaves
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
