Associate Director, Intelligence Systems Lab

Role Overview

• 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 lab's understanding of intelligent behavior in artificial systems.
• Synthesize academic literature and industry advances to shape the lab's technical roadmap for reasoning and cognitive architectures.
• Publish findings and represent the lab's research at internal forums and external conferences.
• 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.
• 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
• 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.
• 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.
• 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.

Requirements

• 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.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health Coverage: Medical, pharmacy, dental, and vision care.
• Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
• Financial Well-being and Protection: 401(k) plan, short
• and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
• Work-life benefits include: Paid Time Off US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees) Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays Based on eligibility*, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day.