Scientific AI Platform Lead, Data Science, Disease Area X
Novartis
Summary
The Scientific AI Platform Lead will join the Disease Area X (DAx) Data Science team at Novartis to build enterprise-grade, AI analysis platforms and data products to accelerate drug discovery. This role will lead and partner on multidisciplinary project teams, operationalize generative and agentic AI solutions, shape DAx AI data strategy, and collaborate with enterprise IT teams. This role reports to the Head of Data Science, DAx.
Job description
Job Description Summary
The Scientific AI Platform Lead will join the Disease Area X (DAx) Data Science team at Novartis to build enterprise-grade, AI analysis platforms and data products to accelerate drug discovery. This role will lead and partner on multidisciplinary project teams, operationalize generative and agentic AI solutions, shape DAx AI data strategy, and collaborate with enterprise IT teams. This role reports to the Head of Data Science, DAx.
Responsibilities
- Translate scientific and operational AI needs into impactful, decision-enabling solutions.
- Hands-on design, build, and sustain scalable AI-focused omics pipelines, APIs, and integrated data products.
- Scale AI workflows using reproducible, transparent, secure, and FAIR data practices.
- Prioritize resources against evolving research needs.
- Establish rigorous project evaluation, deployment, lifecycle, and governance practices.
- Mentor, train and coach wet lab scientists, data scientists, engineers, and cross-functional teams.
- Provide oversight and onboarding of external vendors and service providers.
- Effectively communicate strategy, implementation details and results through internal and external presentations, posters, publications, and technical documentation.
Requirements
- PhD in AI/ML, data science/engineering, computational biology, bioinformatics, computer science/engineering, mathematics, physics, or a related discipline with equivalent practical experience.
- We will also consider qualified MS degree applicants with 5+ years of relevant post-graduate experience.
- 3+ years of hands-on experience in scientific data science or engineering, AI/ML, computational research within biopharma and/or technology industry.
- Experience building and operating production-grade multimodal data platforms for scientific analysis: omics data, data modeling, patient metadata, lineage, access controls, and reproducible workflows.
- Experience with omics analysis pipelines (i.e. genomics, transcriptomics), MCP, LLM patterns, biological foundational models, agent orchestration, and deep learning frameworks (i.e. Pytorch) is preferred.
- Familiarity with current agentic frameworks such Claude Science, Biomni, Gemini or other open-source co-scientist platforms.
- Demonstrated record of scientific or technical impact through publications, conference proceedings, open-source contributions, patents, or deployed data products.
- Deep practical experience in software or data engineering (i.e. agile), including Python, SQL, version control, testing, workflow orchestration, cloud platforms, and data lakes (i.e. Snowflake, Databricks, Palantir Foundry, AWS).
- Experience leading and supporting cross-functionally across diverse science or engineering teams, influencing key stakeholders across a matrix organization.
- Strong communication, interpersonal, ethical, self-awareness, and customer service skills.
Requirements
- PhD in AI/ML, data science/engineering, computational biology, bioinformatics, computer science/engineering, mathematics, physics, or a related discipline with equivalent practical experience
- MS degree with 5+ years of relevant post-graduate experience
- 3+ years of hands-on experience in scientific data science or engineering, AI/ML, computational research within biopharma and/or technology industry
- Experience building and operating production-grade multimodal data platforms for scientific analysis: omics data, data modeling, patient metadata, lineage, access controls, and reproducible workflows
- Experience with omics analysis pipelines (genomics, transcriptomics), MCP, LLM patterns, biological foundational models, agent orchestration, and deep learning frameworks (Pytorch) preferred
- Familiarity with current agentic frameworks such as Claude Science, Biomni, Gemini or other open-source co-scientist platforms
- Demonstrated record of scientific or technical impact through publications, conference proceedings, open-source contributions, patents, or deployed data products
- Deep practical experience in software or data engineering (agile), including Python, SQL, version control, testing, workflow orchestration, cloud platforms, and data lakes (Snowflake, Databricks, Palantir Foundry, AWS)
- Experience leading and supporting cross-functionally across diverse science or engineering teams, influencing key stakeholders across a matrix organization
- Strong communication, interpersonal, ethical, self-awareness, and customer service skills
Benefits
- Health insurance
- Life insurance
- Disability benefits
- 401(k) with company contribution and match
- Performance-based cash incentive
- Annual equity awards
- Generous time off package
Company overview
Novartis is a global pharmaceutical company headquartered in Basel, Switzerland, dedicated to reimagining medicine to improve and extend people's lives. As an innovative medicines company, Novartis focuses on developing high-value treatments that address society's greatest disease burdens through technology leadership in research and development. The company operates across five core therapeutic areas: cardiovascular, renal and metabolic diseases; immunology; neuroscience; oncology; and hematology. With a workforce of approximately 75,883 employees worldwide, Novartis reaches nearly 300 million patients across 118 countries. The company leverages advanced technology platforms including targeted protein degradation, cell therapy, gene therapy, radioligand therapy, and xRNA to develop breakthrough treatments. Novartis maintains a strong portfolio of oncology drugs and generics while continuously expanding its pipeline through strategic acquisitions and internal research. The company's commitment extends beyond drug development to ensuring global access to medical innovations through novel access approaches and patient assistance programs.