Senior Expert / Senior Principal PKS Data Scientist & Scientific Software Engineer (Dual level [...]

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ppbSalary Range: /b CHF102,200.00 - CHF189,800.00 /p h3Job Description Summary /h3 pThe 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.

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. /p h3Job Description /h3 p200+ 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. /p pAs Associate Director 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. /p pYou 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. /p pThis role is ideal for a scientifically grounded data leader 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. /p h3Key Responsibilities /h3 ul liAct as a key Modeling Simulation Data Science representative on discovery and lead optimization programs, contributing scientific and strategic input to project discussions and decisions. /li liPartner 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. /li liDesign, build, and maintain scalable applications, workflows, and data pipelines that support scientific analysis and decision making across projects and modalities. /li liDevelop, evaluate, and deploy machine learning and statistical models to uncover relationships between chemical structure and molecular or pharmacokinetic properties. /li liApply data mining, visualization, and exploratory analysis to derive insight from complex experimental datasets and communicate findings clearly to diverse stakeholders. /li liCreate and implement project- or modality-specific in silico models and data strategies that accelerate and streamline compound progression decisions. /li liWrite production-quality code following strong software engineering practices, including version control, testing, documentation, and maintainability standards. /li liLead or contribute to cross-functional initiatives spanning data science, software engineering, and laboratory teams, ensuring delivery of fit-for-purpose solutions. /li liPromote the adoption and effective use of in-house tools, applications, and data science methods to maximize impact across Translational Medicine and PKS. /li liStay 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. /li /ul h3Essential Requirements /h3 ul liAdvanced degree in a relevant scientific or quantitative field such as cheminformatics, bioinformatics, biomedical engineering, computational biology, computational chemistry, data science, AI/ML in life sciences, or a related discipline. /li liPhD with 5+ years or MSc with 8+ years of relevant work experience applying data science in drug discovery, translational research, or related scientific environments. /li liStrong expertise in machine learning, statistics, and reproducible data science workflows, with demonstrated ability to apply them to real scientific problems. /li liProficiency in Python and/or R, with solid software development practices including version control, testing, documentation, and production-quality coding standards. /li liExperience designing, developing, and deploying robust analytical applications, computational workflows, or machine learning systems in collaborative environments. /li liDemonstrated ability to work across multidisciplinary teams and translate complex analytical concepts into clear, actionable insights for scientists and stakeholders. /li liStrong communication, collaboration, and leadership capabilities, with the ability to influence decisions in cross-functional settings. /li liGood understanding of drug discovery processes, with particular relevance to ADME, pharmacokinetics, pharmacodynamics, or related translational data domains. /li /ul h3Desirable Requirements /h3 ul liExperience with discovery-stage PK modeling, ADME data interpretation, or relating preclinical properties to in vivo pharmacokinetic behavior. /li liFamiliarity with modern machine learning approaches such as deep learning, generative algorithms, or explainable AI in drug discovery contexts. /li liExperience with modern scientific software or web application development frameworks, including JavaScript-based front-end technologies (e.g., Svelte). /li liKnowledge of SQL, databases, Linux-based environments, and scalable data engineering patterns for scientific workflows. /li liExperience working with small molecules, peptides, RNAs, or other modalities in discovery-stage data analysis. /li /ul h3Benefits Rewards /h3 pExpected Annual Base Salary Range for role: Senior Expert: 102,200.00 - 189,800.00 CHF Annual /p pThe base salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically. /p pYou may be eligible for a performance-based bonus depending on certain performance parameters.

Long-term equity awards granted at group level may also be part of your package. /p pBenefits include insurance plans, retirement plans, wellbeing resources, global recognition programs, flexible and hybrid working options, and a minimum 14 weeks paid parental leave. /p h3Commitment to Diversity and Inclusion / EEO /h3 pNovartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve. /p h3Accessibility and accommodation /h3 pNovartis is committed to working with and providing reasonable accommodation to all individuals.

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.

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 and let us know the nature of your request and your contact information.

Please include the job requisition number in your message. /p h3Skills Desired /h3 ul liArtificial Intelligence (AI) /li liBiostatistics /li liChange Management /li liCurious Mindset /li liData Governance /li liData Literacy /li liData Quality /li liData Science /li liData Visualization /li liDeep Learning /li liGraph Algorithms /li liLearning Agility /li liLogistic Regression Model /li liMachine Learning (ML) /li liMachine Learning Algorithms /li liNLP /li liGenAI /li liPandas (Python) /li liPython (Programming Language) /li liR Programming /li liStakeholder Engagement /li liStatistical Analysis /li liStructured Query Language (SQL) /li liTime Series Analysis /li /ul /p #J-18808-Ljbffr