Data Scientist

Flexsis is a company of the Interiman Group, one of the leading providers of personnel services in Switzerland. Thanks to our solid expertise as well as the competencies within the Interiman Group, we offer tailored solutions in personnel consulting.

For our client Novartis in Basel, we are looking for a reliable and motivated Data Scientist (m/f/d)

Duration: 6 month - maternity cover

Workload: 80%

Key Responsibilities:

• Applying established and novel approaches to model multi-modal clinical data to identify biomarkers predictive of disease progression and treatment response, and to nominate novel therapeutic target hypotheses
• Developing and applying a portfolio of pre-clinical models derived from internal and public data to departmental projects
• Working closely with a core team of Translational Research & Data Science scientists fluent in wet and dry lab practices
• Actively participating on interdisciplinary teams to model biology and guide impactful decision-making in drug discovery and early development
• Actively participating in alliances with external partners at top healthcare institutions, including the opportunity to publish your findings

Essential Requirements:

• Life sciences industry experience modeling biomedical research data
• Demonstrated working knowledge and application of statistics and data modeling to large scale life-sciences data • Hands-on experience in integration of large multi-omics data (genomics, transcriptomics, proteomics and metabolomics) to answer key scientific questions related to understanding disease
• Hands-on industry experience with clinical data and its responsible handling
• Hands-on experience applying AI and machine learning methods to scientific or R&D data
• Experience using AI tools, including large language models and agentic workflows, to accelerate analysis and decision-making
• Proficiency in R/Python and competency working in a Linux environment
• Reproducible research practitioner with demonstrated visualization and reporting skills
• A team-oriented, practical approach to problem solving

Must haves:

• Working knowledge and application of statistics and data modeling to large scale life-sciences data
• Hands-on experience in integration of large multi-omics data (genomics, transcriptomics, proteomics and metabolomics) to answer key scientific questions related to understanding disease
• Proficiency in R/Python and competency working in a Linux environment
• Hands-on experience applying AI and machine learning methods to scientific or R&D data.
• Experience using AI tools, including large language models and agentic workflows, to accelerate analysis and decision-making.