Scientific AI Platform Lead, Data Science, Disease Area X

Band

Level 4

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.

Job Description

Internal Job Title: Senior Expert I/II, Data Science

Position Location : Cambridge, MA, hybrid

** This role is based in Cambridge, MA. Novartis is unable to offer relocation support for this role: please only apply if this location is accessible for you.

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.

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.

Compensation & Benefits:

The salary for this position is expected to range between $126,000 and $234,000 USD annually for Senior Expert I, Data Science, and $138,600 and $257,400 USD annually for Senior Expert II, Data Science. The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.

Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.

US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.

To learn more about the culture, rewards and benefits we offer our people click here .

EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.

https://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings

Salary Range

$126,000.00 - $234,000.00

Skills Desired

Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis