Team Leader in Data Science, Disease Area X

Job Description Summary

The Team Leader in Data Science, Disease Area X at Novartis will lead and contribute to high-impact data science programs that transform complex biological, translational, and multi-omics data into decision-driving insights for drug discovery. This role will combine scientific leadership, hands-on computational expertise, and people leadership to advance target identification, biomarker discovery, mechanism-of-action understanding, and portfolio decisions.

The successful candidate will lead a multidisciplinary team of data scientists and partner closely with biology, translational research, data sciences, IT, and discovery platform teams. They will help define and operationalize AI/ML strategy for discovery applications, including generative and agentic AI. This leader will also contribute significantly to data generation, curation, and engineering strategies that enable scalable use of proprietary and public datasets. The role reports to the Head of Data Science, Disease Area X.

 

Job Description

Internal Job Title: Senior Principal Scientist or Associate Director

Position Location: Cambridge, MA Hybrid

Key responsibilities:

• Lead data science strategy and execution for hypothesis-driven discovery programs, including study design, analysis of experiments, and interpretation of complex biological datasets.

• Drive multi-omics analytics across genomics, transcriptomics, proteomics, single-cell, spatial, imaging, clinical, and other relevant data modalities to support target and biomarker portfolios.

• Translate scientific questions into computational strategies, selecting fit-for-purpose statistical, machine learning, AI, and bioinformatics approaches.

• Operationalize responsible use of generative and/or agentic AI tools in drug discovery workflows, ensuring scientific rigor, data governance, and appropriate human oversight.

• Contribute hands-on technical work in scientific software development, data engineering, workflow automation, reproducible analysis, and scalable analytical pipelines.

• Partner cross-functionally with wet-lab scientists, translational researchers, platform teams, and senior stakeholders to shape experimental design and accelerate decision-making.

• Prioritize resources and capabilities across multiple projects, adapting to evolving portfolio needs and balancing strategic impact with delivery timelines.

• Lead, coach, and develop direct reports, creating a collaborative, inclusive, scientifically rigorous, and high-performing team environment.

• Communicate scientific findings and recommendations clearly through internal presentations, governance discussions, publications, posters, and external scientific forums.

• Promote FAIR data practices, reproducible research, high-quality documentation, project tracking, and scalable analytical standards across the team.

Essential Requirements:

• Advanced degree (PhD preferred) in Data Science, Computational Biology, Bioinformatics, Computational Science, Molecular Biology, Genetics, Biochemistry, Engineering, or a related quantitative or life sciences discipline.

• 6+ years of relevant experience applying computational biology, bioinformatics, AI/ML, statistics, or data science to drug discovery, translational research, biotechnology, pharmaceutical R&D, technology, or academic research.

• Experience leading or managing internal data scientists, computational biologists, bioinformaticians, or machine learning scientists in a matrix management environment as well as external collaborators

• Demonstrated ability to lead complex, hypothesis-driven scientific analyses using biological, multi-omics, or translational datasets, including RNA-seq, single-cell RNA-seq, proteomics, genomics, spatial biology, and/or imaging.

• Strong practical experience with scientific software development, reproducible analysis, workflow orchestration and collaborative development practices; experience in Python and/or R, with familiarity in tools such as GitHub, HuggingFace, workflow managers, Jupyter notebooks, containers

• Deep experience with cloud-based or enterprise-scale compute platforms, high-performance computing

• Significant experience influencing and collaborating across diverse scientific teams, including wet-lab biology, translational research, engineering, and computational functions.

• Familiarity with modern AI/ML methods and their application to biological or biomedical data (i.e. generative, agentic AI)

• Experience acquiring, curating, and engineering proprietary and public