Senior Expert / Senior Principal PKS Data Scientist & Scientific Software Engineer (Dual level posting)

CHF102,200.00 - CHF189,800.00 @@HD@@  Job Description Summary@@/HD@@ #LI-Hybrid Location: Basel, Switzerland Internal Title: Senior Expert II Data Science or Senior Principal Data Science The Senior Data Scientist & Scientific Software Engineer will join the Modeling & Simulation Data Science team within the Translational Medicine Unit to help advance data-driven drug discovery. The role is focused 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 will drive the development of scalable analytical applications, reusable computational workflows, and decision-enabling in silico solutions that accelerate lead optimization and strengthen scientific decision making. @@HD@@  Job Description@@/HD@@ 200+ experimental datasets are generated every day in the Pharmacokinetic Sciences (PKS) department at Novartis Biomedical Research. This role sits at the center of efforts to unlock the value of these data for decision making and to shape the future of medicine through advanced data science.   As Senior Expert II / Senior Principal Data Scientist & Scientific Software Engineer, you will join the Modeling & Simulation Data Science team in the Translational Medicine Unit and work at the intersection of data science, software engineering, and drug discovery. You will apply machine learning, statistics, and modern software development practices to harness ADME and PK data, uncover structure - property relationships, and translate scientific and business needs into scalable, strategically aligned solutions.   You will collaborate closely with Data & Digital and PKS wet- (ADME and bioanalytical) and dry-lab (modeling) teams to   identify   opportunities where data, applications,   and in   silico methods can meaningfully improve decision quality, efficiency, and scientific impact. In addition to hands-on technical delivery, you will help shape and champion fit-for-purpose solutions that are robust, reusable, and maintainable in a production setting.   This role is ideal for a scientifically grounded data scientist who combines strong analytical depth with practical software engineering   expertise , and who is motivated by delivering measurable impact in drug discovery through high-quality data products, machine learning, and computational innovation.

Act as a senior Modeling & Simulation Data Science representative on discovery and lead optimization programs, contributing scientific and strategic input to project discussions and decisions, and helping shape data science direction across programs.

Act as a key Modeling & Simulation Data Science representative on discovery and lead optimization programs, providing expert scientific input and contributing 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 strategically aligned analytical and computational solutions with broad scientific and operational impact.   Own the    •

Partner with Data & Digital and PKS wet- and dry-lab teams to   identify   priority gaps, clarify business needs, and translate them into high-impact analytical and computational solutions for projects and platforms.   • 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 strategies that accelerate and streamline compound progression decisions while informing broader scientific direction and capability development.   •

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.   •

Lead and shape cross-functional initiatives across data science, software engineering, and laboratory teams, driving strategic alignment and organizational impact.   •

Contribute to and coordinate cross-functional initiatives across data science, software engineering, and laboratory teams to deliver high-quality solutions.   •

Promote and champion the broad adoption of in-house tools, applications, and data science methods, helping shape best practices and strengthen data science capabilities across Translational Medicine and PKS.   •

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 proactively evaluate and bring   appropriate innovation   into practice to improve efficiency and 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.

PhD with 5+ years or MSc with 8+ years of relevant work experience applying data science in drug discovery, translational research, or related scientific environments, with   a track record   of leading complex initiatives and influencing scientific direction.   •

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 to complex scientific problems and guide others in their effective use.   •

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, including shaping reusable solutions and technical direction in collaborative environments.   •

Experience designing, developing, and deploying robust analytical applications, computational workflows, or machine learning systems in collaborative environments.   •

Demonstrated   ability to work across multidisciplinary teams, influence stakeholders, and translate complex analytical concepts into clear, actionable insights for scientists, partners, and leaders.   •

Demonstrated   ability to work across multidisciplinary teams and translate complex analytical concepts into clear, actionable insights for scientists and stakeholders.   •

Strong communication , collaboration, and leadership capabilities, with   demonstrated   ability to influence decisions and help shape direction in cross-functional settings.   •

Strong communication   and collaboration capabilities, with the ability to influence project decisions in cross-functional settings.   •

Excellent understanding of drug discovery processes, with   particular relevance   to ADME, pharmacokinetics, pharmacodynamics, or related translational data domains, and ability to articulate the impact and value of data science in strategic terms.   •

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.

Novartis is committed to building an outstanding, inclusive work environment and diverse teams’ representative   of the patients and communities we serve.

Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to receive more detailed information about the essential functions of a  position, please send an e-mail to diversity.inclusion_ch@novartis.com and let us know the nature of your request and your contact information. Please include the job requisition number in your message. @@HD@@  Skills Desired@@/HD@@ Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Logistic Regression Model (Inactive), Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis