Research Science Specialist – Scientific AI

Overview

In this role you apply scientific expertise at the intersection with artificial intelligence to advance client research strategies and develop scalable AI-enabled assets. You will work within McKinsey’s Life Sciences practice on interdisciplinary problems, shaping innovations and contributing to knowledge assets. Expect to lead domain-specific research, mentor junior colleagues, and translate complex science for client teams. The opportunity centers on building capabilities, delivering high-impact insights, and driving science-driven value for clients in diverse sectors.

Pay / Benefits

• competitive salary
• comprehensive benefits package
• continuous learning and apprenticeship culture
• global community with colleagues in 65+ countries
• opportunity to learn from diverse backgrounds

Responsibilities

• conduct domain-specific and landscape research to inform product development and asset creation
• analyze diverse data sources and apply advanced analytics to drive science-driven innovation
• provide scientific and technical leadership in creating and disseminating proprietary knowledge and assets
• support manuscript drafting/publication and contribute to product roadmaps at cellular or system levels
• mentor junior team members and contribute to capability growth
• collaborate with cross-functional product development teams to translate insights into solutions
• shape problem framing and hypotheses through ideation sessions and experimentation

Key requirements

• PhD preferred with 1+ year or Master’s with 3+ years in biology, chemistry, bioengineering or computer science
• deep domain expertise and familiarity with key data sources, databases, and cutting-edge methodologies
• ability to translate technical publications and published code to assess suitability
• strong publication record or evidence of scientific thought leadership
• excellent project management and ability to drive cross-functional initiatives
• professionalism, integrity, independence, and adaptability in fast-paced environments
• ability to articulate complex technical concepts to non-technical audiences

• leadership and mentorship
• clear communication
• collaboration and teamwork
• interdisciplinary analytics
• machine learning and AI (generative AI, foundation models, causal inference)
• computational biology

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