The Senior Data Scientist & Scientific Software Engineer will join the Modeling & Simulation Data Science team within the Translational Medicine Unit to advance data-driven drug discovery. The role focuses on transforming large-scale ADME, PK, and related experimental datasets into actionable insights through advanced analytics, machine learning, and robust software engineering. Partnering closely with pharmacokinetic sciences (PKS) scientists, Data & Digital teams, and cross-functional stakeholders, this position drives the development of scalable analytical applications, reusable computational workflows, and decision-enabling in silico solutions that accelerate lead optimization and strengthen scientific decision making.
Over 200 experimental datasets are generated daily in the Pharmacokinetic Sciences (PKS) department at Novartis Biomedical Research. This role is central to unlocking the value of these data for decision making and shaping the future of medicine through advanced data science.
Tasks
- Act as a Modeling & Simulation Data Science representative on discovery and lead optimization programs, contributing scientific input to project discussions and decisions.
- Partner with Data & Digital and PKS wet- and dry-lab teams to identify priority gaps, clarify business needs, and translate them into analytical and computational solutions with scientific and operational impact.
- Design, build, and maintain scalable applications, workflows, and data pipelines that support scientific analysis and decision making across projects and modalities.
- Develop, evaluate, and deploy machine learning and statistical models to uncover relationships between chemical structure and molecular or pharmacokinetic properties.
- Apply data mining, visualization, and exploratory analysis to derive insight from complex experimental datasets and communicate findings clearly to diverse stakeholders.
- Create and implement project- or modality-specific in silico models and data approaches that accelerate and streamline compound progression decisions.
- Write production-quality code following strong software engineering practices, including version control, testing, documentation, and maintainability standards.
- Contribute to and coordinate cross-functional initiatives across data science, software engineering, and laboratory teams to deliver high-quality solutions.
- Promote the adoption and effective use of in-house tools, applications, and data science methods across projects and teams to maximize scientific impact.
- Stay current with advances in AI/ML, statistics, and computational methods relevant to ADME, PK/PD, and drug discovery, and help bring appropriate innovation into practice within projects and workflows.
Requirements
- Advanced degree in a relevant scientific or quantitative field such as cheminformatics, bioinformatics, biomedical engineering, computational biology, computational chemistry, AI/ML in life sciences, or a related discipline.
- Relevant work experience applying data science in drug discovery, translational research, or related scientific environments, with a strong record of independent technical contribution and expert delivery in collaborative settings.
- Strong expertise in machine learning, statistics, and reproducible data science workflows, with demonstrated ability to apply them independently to real scientific problems.
- Proficiency in Python and/or R, with solid software development practices including version control, testing, documentation, and production-quality coding standards.
- Experience designing, developing, and deploying robust analytical applications, computational workflows, or machine learning systems in collaborative environments.
- Demonstrated ability to work across multidisciplinary teams and translate complex analytical concepts into clear, actionable insights for scientists and stakeholders.
- Strong communication and collaboration capabilities, with the ability to influence project decisions in cross-functional settings.
- Good understanding of drug discovery processes, with particular relevance to ADME, pharmacokinetics, pharmacodynamics, or related translational data domains, and ability to apply data science effectively to relevant scientific problems.
- Experience with discovery-stage PK modeling, ADME data interpretation, or relating preclinical properties to in vivo pharmacokinetic behavior (desirable).
- Familiarity with modern machine learning approaches such as deep learning, generative algorithms, or explainable AI in drug discovery contexts (desirable).
- Experience with modern scientific software or web application development frameworks, including JavaScript-based front-end technologies (e.g., Svelte) (desirable).
- Knowledge of SQL, databases, Linux-based environments, and scalable data engineering patterns for scientific workflows (desirable).
- Experience working with small molecules, peptides, RNAs, or other modalities in discovery-stage data analysis (desirable).
Benefits
- Expected annual base salary range: Senior Expert: 102,200.00 - 189,800.00 CHF; Senior Principal: 129,500.00 - 240,500.00 CHF.
- Base salary determined based on relevant skills, competencies, and experience in accordance with Novartis pay setting policy and reviewed periodically.
- Eligibility for a performance-based bonus depending on certain performance parameters.
- Competitive benefits in kind, including insurance plans, retirement plans, wellbeing resources, and global recognition programs.
- Flexible and hybrid working options, where possible.
- Minimum 14 weeks paid parental leave.
- Long-term equity awards granted at group level may also be part of the package.
- Pay equity is a fundamental principle of employment policy.
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Über uns
Das Team
The Modeling & Simulation Data Science team is part of the Translational Medicine Unit and collaborates closely with Data & Digital and PKS wet- and dry-lab teams to advance data-driven drug discovery.