Associate Director, Intelligence Systems Lab

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This role leads research into the principles of intelligence and architects advanced agentic AI systems grounded in rigorous evaluation science. The Associate Director is responsible for advancing the labs understanding of reasoning planning memory and generalization in AI agents while establishing benchmarking methodologies that set the standard for agent evaluation. This position is pivotal in shaping the strategic direction of intelligence systems research and implementing advanced frameworks that accelerate insight generation across Commercial Manufacturing Clinical Development and Research functions.

Key Responsibilities and Major Duties:

• Research Leadership and Framework Architecture:

  • Lead research initiatives exploring foundational principles of intelligence including reasoning planning memory and generalization in AI systems.

  • Architect frameworks for agent cognition abstraction goal decomposition and adaptive planning; establish design principles for multi-agent systems.

  • Define research hypotheses and experimental agendas that advance the labs understanding of intelligent behavior in artificial systems.

  • Synthesize academic literature and industry advances to shape the labs technical roadmap for reasoning and cognitive architectures.

  • Publish findings and represent the labs research at internal forums and external conferences.

• Benchmarking and Evaluation Science Leadership:

  • Lead the development of benchmarking and evaluation science for AI agents establishing rigorous methodologies to assess reasoning planning and generalization.

  • Define evaluation frameworks and success criteria for multi-agent systems across diverse enterprise use cases.

  • Oversee the creation of test harnesses evaluation datasets and measurement standards.

  • Ensure experimental rigor and reproducibility across all benchmarking activities.

  • Set the strategic direction for how the organization measures and validates agent intelligence.

• Agentic AI Development and Implementation:

  • Lead the design and development of multi-agent AI systems using frameworks such as Strands LangGraph DSPy and similar agentic architectures.

  • Oversee development of RAG applications knowledge graphs and conversational AI solutions informed by research insights.

  • Drive the implementation of autonomous workflows and decision systems that demonstrate advances in agent reasoning.

  • Ensure solutions reflect the latest research in planning memory and generalization capabilities

• Rapid Prototyping and Innovation:

  • Translate research concepts into working prototypes and production-ready demonstrations.

  • Apply ROI-first evaluation approaches to prioritize high-impact research initiatives.

  • Identify opportunities to deploy next-generation reasoning and planning capabilities across the enterprise.

  • Lead documentation efforts for research findings experimental results and technical methodologies.

• Cross-Functional Collaboration:

  • Partner with analytics and business stakeholders across Commercial GPS/Manufacturing Clinical Development and Research functions to translate research into business value.

  • Collaborate with IT data engineering and platform teams to ensure research prototypes are deployable on enterprise infrastructure.

  • Translate complex AI research insights into actionable recommendations for technical and non-technical stakeholders.

  • Represent the Intelligence Systems Lab in enterprise-wide AI initiatives and strategic planning.

• Team Development and Best Practices:

  • Mentor and develop data scientists and researchers within the team; build a culture of scientific rigor and intellectual curiosity.

  • Establish governance frameworks and best practices for agent evaluation benchmarking science and research methodology.

  • Lead knowledge sharing through internal presentations publications and collaborative research initiatives.

  • Shape team capabilities and hiring strategies to advance the labs research mission.

Qualifications/Degree/Certification/Licensure:

• Educational Background: BA/BS required (quantitative area of study such as Computer Science Data Science Statistics Mathematics Cognitive Science or Engineering preferred). Ph.D. or other graduate degree preferred.

• Technical Proficiency: Proficiency in Python (R or Julia) with experience in ML frameworks (Scikit-Learn TensorFlow PyTorch etc.) Experience with agentic AI frameworks (Strands LangGraph AutoGen DSPy CrewAI or similar). Experience with LLMs prompt engineering and RAG architectures. Strong background in benchmarking and evaluation methodologies for AI systems. Proficiency in SQL and familiarity with cloud-based environments (AWS Azure GCP). Experience with Git MLOps practices and containerization technologies. Strong foundation in experimental design statistical analysis and research methodology.

• Communication Skills: Excellent communication and presentation skills with a proven ability to explain complex research and analyses to both technical and non-technical stakeholders. Experience presenting at conferences or publishing research preferred.

• Experience: A minimum of 5 years of hands-on experience in data science machine learning AI development or AI research. Experience leading research initiatives or technical teams preferred. Experience with pharmaceutical or life sciences industry preferred.

• Cross-Functional Collaboration: Proven experience in leading complex research or development projects with multi-functional team members.

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