Scientific AI Platform Lead, Data Science, Disease Area X

Cambridge Source: WhatJobs

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Overview

In this role you lead the Scientific AI Platform efforts within DAx Data Science to deliver scalable AI analyses and data products that accelerate drug discovery. You partner with multidisciplinary teams to operationalize AI solutions and shape the DAx data strategy while collaborating with enterprise IT. You translate scientific needs into impact-driven platforms and mentor cross-functional colleagues. This is a hands-on, strategic role at the intersection of science, engineering, and product delivery, with the chance to influence enterprise AI practices at Novartis.

Pay / Benefits

comprehensive benefits package (health, life, disability)
401(k) with company contribution and match
vacation, personal days, holidays, leaves
performance-based cash incentive
eligibility for annual equity awards
flexible work arrangements (hybrid in Cambridge)

Responsibilities

Translate scientific and operational AI needs into decision-enabling solutions
Design, build, and sustain scalable AI-focused omics pipelines, APIs, and integrated data products
Scale AI workflows with 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
Communicate strategy, implementation details and results through internal and external presentations, posters, publications, and technical documentation

Key requirements

PhD in AI/ML, data science/engineering, computational biology, bioinformatics, computer science/engineering, mathematics, physics, or 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 (genomics, transcriptomics), MCP, LLM patterns, biological foundational models, agent orchestration, and deep learning frameworks (e.g. PyTorch) is preferred
Familiarity with 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 (e.g. agile), including Python, SQL, version control, testing, workflow orchestration, cloud platforms, and data lakes (e.g. Snowflake, Databricks, Palantir Foundry, AWS)
Experience leading and supporting cross-functionally across diverse science or engineering teams, influencing stakeholders in a matrix organization
Strong communication, interpersonal, ethical, self-awareness, and customer service skills