Applied AI and Machine Learning Scientist (Director)

About the role

The successful candidate for the Applied AI / ML Scientist position leads the technical evaluation, development, and application of AI across the Internal Medicine Research Unit (IMRU), translating advances in foundation models, agentic systems, multimodal AI, and related methods into reusable capabilities that strengthen scientific decision-making end-to-end.

Responsibilities

• Provide AI/ML technical leadership for AIM2 and define a clear roadmap for how large language models, agentic systems, multimodal AI, and related methods will be applied to high-value scientific problems across Internal Medicine Research Unit (IMRU).
• Lead the technical evaluation and development of the AI capabilities in the AIM2, identifying, prioritizing, and shaping opportunities so that AIM2 focuses on areas where technically credible, reusable AI capabilities can create meaningful scientific or operational leverage.
• Provide senior technical and scientific direction across AIM2 Discovery Center, ensuring that proposed solutions are methodologically sound, fit for purpose, and grounded in biological, translational, and drug discovery context.
• Guide the development of reusable AI-enabled capabilities that strengthen scientific decision-making end-to-end, with emphasis on scientific rigor, technical quality, reproducibility, and practical utility across IMRU lines.
• Establish governance and evaluation standards for AI-built capabilities, including expectations for provenance, validation, guardrails, responsible use, and appropriate human oversight.
• Partner closely with IMRU Integrative Biology, IMRU line teams, MLCS, and Digital partners to ensure that AI efforts remain tightly aligned to real scientific needs and can be deployed in ways that are trusted, scalable, and adopted in day-to-day work.
• Shape and manage selected external partnerships relevant to AIM2 priorities, helping evaluate emerging technologies and collaborators while ensuring that external engagements remain aligned to Pfizer priorities and IMRU needs.
• Articulate the value and impact of the AI capabilities within IMRU to senior stakeholders, including technical differentiation, adoption trajectory, and return on investment of key initiatives to support strategic planning and decision making.
• Build a strong technical culture within AIM2 and across IMRU, fostering scientific curiosity, high standards, collaboration, and continuous learning, while helping raise confidence in the responsible application of AI across IMRU.

Qualifications

• Advanced degree in computer science, machine learning, artificial intelligence, computational biology, bioinformatics, statistics, engineering, life sciences, or a related quantitative or scientific field preferred.
• Demonstrated experience leading complex, cross-functional initiatives in applied AI, computational science, data science, digital transformation, or related domains, ideally with responsibility for strategy, portfolio prioritization, and value realization.
• Strong hands-on understanding of LLMs, foundation models, generative AI, machine learning, and related AI approaches, with the technical credibility to guide decisions, assess trade-offs, and challenge weak approaches even when not serving as the primary builder.
• Demonstrated ability to identify, prioritize, and shape high-value use cases in ambiguous environments, translating scientific or stakeholder needs into practical, reusable solutions with measurable impact.
• Experience building and scaling reusable workflows, methods, products, or platforms rather than delivering isolated one-off analyses.
• Demonstrated ability to develop strategy, shape AI portfolios, and communicate impact and return on investment to senior stakeholders in a clear and credible way.
• Strong matrix leadership, communication, and influence skills, including the ability to align senior stakeholders, provide technical and strategic direction, and drive adoption without relying solely on formal authority.
• Sound judgment regarding methodological rigor, evaluation, provenance, model limitations, risk, and the appropriate role of human oversight in AI-enabled scientific workflows.