Associate Director, Intelligence Systems Lab
Bristol Myers Squibb
4 hours ago
Remote friendly (Bothell, WA)
United States
IT
Description:
- Leads research into principles of intelligence and architects advanced agentic AI systems grounded in rigorous evaluation science.
- Advances the lab’s understanding of reasoning, planning, memory, and generalization in AI agents.
- Establishes benchmarking methodologies that set the standard for agent evaluation.
- Shapes strategic direction for intelligence systems research and implements advanced frameworks that accelerate insight generation across Commercial, Manufacturing, Clinical Development, and Research functions.
Responsibilities:
- Research Leadership and Framework Architecture
- Lead research on 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.
- Synthesize academic/industry advances to shape the technical roadmap.
- Publish findings and represent the lab at internal/external venues.
- Benchmarking and Evaluation Science Leadership
- Develop benchmarking and evaluation science for AI agents.
- Define evaluation frameworks and success criteria for multi-agent systems across enterprise use cases.
- Oversee test harnesses, evaluation datasets, and measurement standards.
- Ensure experimental rigor and reproducibility.
- Set strategy for how the organization measures and validates agent intelligence.
- Agentic AI Development and Implementation
- Lead design/development of multi-agent AI systems using Strands, LangGraph, DSPy (or similar).
- Oversee RAG applications, knowledge graphs, and conversational AI solutions.
- Drive autonomous workflows and decision systems demonstrating advances in agent reasoning.
- Ensure solutions reflect latest research in planning, memory, and generalization.
- Rapid Prototyping and Innovation
- Translate concepts into prototypes and production-ready demonstrations.
- Use ROI-first evaluation to prioritize high-impact initiatives.
- Identify opportunities to deploy next-generation reasoning/planning capabilities enterprise-wide.
- Lead documentation of findings, results, and methodologies.
- Cross-Functional Collaboration
- Partner with analytics/business stakeholders across Commercial, GPS/Manufacturing, Clinical Development, and Research to translate research into business value.
- Collaborate with IT, data engineering, and platform teams to enable enterprise deployment.
- Translate AI insights into actionable recommendations for technical and non-technical stakeholders.
- Represent the lab in enterprise-wide AI initiatives and strategic planning.
- Team Development and Best Practices
- Mentor data scientists/researchers; build a culture of rigor and curiosity.
- Establish governance and best practices for agent evaluation and benchmarking methodology.
- Lead knowledge sharing via presentations, publications, and collaborative initiatives.
- Shape team capabilities and hiring strategies.
Qualifications:
- Educational Background: BA/BS required (quantitative field such as CS, Data Science, Statistics, Mathematics, Cognitive Science, Engineering). Ph.D. or other graduate degree preferred.
- Technical Proficiency:
- Python (R or Julia); ML frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.).
- Agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar).
- LLMs, prompt engineering, and RAG architectures.
- Benchmarking/evaluation methodologies for AI systems.
- SQL; cloud environments (AWS, Azure, GCP).
- Git, MLOps practices, containerization.
- Experimental design, statistical analysis, and research methodology.
- Communication Skills: Strong communication/presentation; ability to explain complex work to technical and non-technical stakeholders. Conferences/publishing preferred.
- Experience: Minimum 5 years hands-on experience in data science, ML, AI development, or AI research. Leading initiatives/teams preferred. Pharmaceutical/life sciences experience preferred.
- Cross-Functional Collaboration: Proven experience leading complex projects with multi-functional teams.
Compensation (explicitly listed):
- Bothell, WA: $184,290–$223,314
- Princeton, NJ: $167,540–$203,013
- San Diego, CA: $184,290–$223,314
- Seattle, WA: $184,290–$223,314
- Additional incentive cash/stock may be available (eligibility-dependent).
Benefits (explicitly listed):
- Health Coverage: medical, pharmacy, dental, vision.
- Wellbeing Support Programs (e.g., BMS Well-Being Account, EAP).
- Financial Well-being and Protection (e.g., 401(k), disability, life/accident insurance, legal support, survivor support).
- Work-life benefits: Paid Time Off (includes flexible time off/paid vacation and paid national holidays depending on employee location/category; additional items based on eligibility such as paid sick time, volunteer days, leaves of absence, and annual Global Shutdown).
Application instructions:
- If the role interests you but doesn’t perfectly match your resume, apply anyway.
- Leads research into principles of intelligence and architects advanced agentic AI systems grounded in rigorous evaluation science.
- Advances the lab’s understanding of reasoning, planning, memory, and generalization in AI agents.
- Establishes benchmarking methodologies that set the standard for agent evaluation.
- Shapes strategic direction for intelligence systems research and implements advanced frameworks that accelerate insight generation across Commercial, Manufacturing, Clinical Development, and Research functions.
Responsibilities:
- Research Leadership and Framework Architecture
- Lead research on 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.
- Synthesize academic/industry advances to shape the technical roadmap.
- Publish findings and represent the lab at internal/external venues.
- Benchmarking and Evaluation Science Leadership
- Develop benchmarking and evaluation science for AI agents.
- Define evaluation frameworks and success criteria for multi-agent systems across enterprise use cases.
- Oversee test harnesses, evaluation datasets, and measurement standards.
- Ensure experimental rigor and reproducibility.
- Set strategy for how the organization measures and validates agent intelligence.
- Agentic AI Development and Implementation
- Lead design/development of multi-agent AI systems using Strands, LangGraph, DSPy (or similar).
- Oversee RAG applications, knowledge graphs, and conversational AI solutions.
- Drive autonomous workflows and decision systems demonstrating advances in agent reasoning.
- Ensure solutions reflect latest research in planning, memory, and generalization.
- Rapid Prototyping and Innovation
- Translate concepts into prototypes and production-ready demonstrations.
- Use ROI-first evaluation to prioritize high-impact initiatives.
- Identify opportunities to deploy next-generation reasoning/planning capabilities enterprise-wide.
- Lead documentation of findings, results, and methodologies.
- Cross-Functional Collaboration
- Partner with analytics/business stakeholders across Commercial, GPS/Manufacturing, Clinical Development, and Research to translate research into business value.
- Collaborate with IT, data engineering, and platform teams to enable enterprise deployment.
- Translate AI insights into actionable recommendations for technical and non-technical stakeholders.
- Represent the lab in enterprise-wide AI initiatives and strategic planning.
- Team Development and Best Practices
- Mentor data scientists/researchers; build a culture of rigor and curiosity.
- Establish governance and best practices for agent evaluation and benchmarking methodology.
- Lead knowledge sharing via presentations, publications, and collaborative initiatives.
- Shape team capabilities and hiring strategies.
Qualifications:
- Educational Background: BA/BS required (quantitative field such as CS, Data Science, Statistics, Mathematics, Cognitive Science, Engineering). Ph.D. or other graduate degree preferred.
- Technical Proficiency:
- Python (R or Julia); ML frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.).
- Agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar).
- LLMs, prompt engineering, and RAG architectures.
- Benchmarking/evaluation methodologies for AI systems.
- SQL; cloud environments (AWS, Azure, GCP).
- Git, MLOps practices, containerization.
- Experimental design, statistical analysis, and research methodology.
- Communication Skills: Strong communication/presentation; ability to explain complex work to technical and non-technical stakeholders. Conferences/publishing preferred.
- Experience: Minimum 5 years hands-on experience in data science, ML, AI development, or AI research. Leading initiatives/teams preferred. Pharmaceutical/life sciences experience preferred.
- Cross-Functional Collaboration: Proven experience leading complex projects with multi-functional teams.
Compensation (explicitly listed):
- Bothell, WA: $184,290–$223,314
- Princeton, NJ: $167,540–$203,013
- San Diego, CA: $184,290–$223,314
- Seattle, WA: $184,290–$223,314
- Additional incentive cash/stock may be available (eligibility-dependent).
Benefits (explicitly listed):
- Health Coverage: medical, pharmacy, dental, vision.
- Wellbeing Support Programs (e.g., BMS Well-Being Account, EAP).
- Financial Well-being and Protection (e.g., 401(k), disability, life/accident insurance, legal support, survivor support).
- Work-life benefits: Paid Time Off (includes flexible time off/paid vacation and paid national holidays depending on employee location/category; additional items based on eligibility such as paid sick time, volunteer days, leaves of absence, and annual Global Shutdown).
Application instructions:
- If the role interests you but doesn’t perfectly match your resume, apply anyway.