Associate Director, Intelligence Systems Lab
Bristol Myers Squibb
2 hours ago
Remote friendly (San Diego Metropolitan Area)
United States
IT
Key Responsibilities and Major Duties
- 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; define research hypotheses/experimental agendas; synthesize academic and industry advances; publish findings and represent the lab at forums and conferences.
- Benchmarking and Evaluation Science Leadership: Develop benchmarking/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 rigor and reproducibility; set strategic direction for measuring/validating agent intelligence.
- Agentic AI Development and Implementation: Design/develop multi-agent systems using agentic frameworks (e.g., Strands, LangGraph, DSPy); oversee RAG applications, knowledge graphs, and conversational AI; drive autonomous workflows and decision systems; ensure solutions reflect latest capabilities in planning, memory, and generalization.
- Rapid Prototyping and Innovation: Translate research into prototypes/demonstrations; apply ROI-first evaluation; identify deployment opportunities for reasoning/planning capabilities; document research and methodologies.
- Cross-Functional Collaboration: Partner with analytics and business stakeholders (Commercial, GPS/Manufacturing, Clinical Development, Research); collaborate with IT/data engineering/platform for deployable prototypes; translate research into actionable recommendations; represent the lab in enterprise AI initiatives.
- Team Development and Best Practices: Mentor team members; establish governance and best practices; lead knowledge sharing; shape capabilities and hiring.
Qualifications / Required & Preferred
- Education: BA/BS required in a quantitative area (CS, Data Science, Statistics, Math, Cognitive Science, or Engineering preferred); Ph.D./graduate degree preferred.
- Technical: Python (R or Julia); ML frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.); agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar); LLMs, prompt engineering, RAG architectures; benchmarking/evaluation methodologies; SQL; cloud (AWS/Azure/GCP); Git, MLOps, containerization; experimental design, statistical analysis, research methodology.
- Communication: Strong communication/presentation; explain complex work to technical and non-technical stakeholders; conference/publishing experience preferred.
- Experience: 5+ years hands-on data science/ML/AI development or AI research; experience leading initiatives/teams preferred; pharmaceutical/life sciences preferred.
- Cross-functional collaboration: Proven experience leading complex projects with multi-functional teams.
- 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; define research hypotheses/experimental agendas; synthesize academic and industry advances; publish findings and represent the lab at forums and conferences.
- Benchmarking and Evaluation Science Leadership: Develop benchmarking/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 rigor and reproducibility; set strategic direction for measuring/validating agent intelligence.
- Agentic AI Development and Implementation: Design/develop multi-agent systems using agentic frameworks (e.g., Strands, LangGraph, DSPy); oversee RAG applications, knowledge graphs, and conversational AI; drive autonomous workflows and decision systems; ensure solutions reflect latest capabilities in planning, memory, and generalization.
- Rapid Prototyping and Innovation: Translate research into prototypes/demonstrations; apply ROI-first evaluation; identify deployment opportunities for reasoning/planning capabilities; document research and methodologies.
- Cross-Functional Collaboration: Partner with analytics and business stakeholders (Commercial, GPS/Manufacturing, Clinical Development, Research); collaborate with IT/data engineering/platform for deployable prototypes; translate research into actionable recommendations; represent the lab in enterprise AI initiatives.
- Team Development and Best Practices: Mentor team members; establish governance and best practices; lead knowledge sharing; shape capabilities and hiring.
Qualifications / Required & Preferred
- Education: BA/BS required in a quantitative area (CS, Data Science, Statistics, Math, Cognitive Science, or Engineering preferred); Ph.D./graduate degree preferred.
- Technical: Python (R or Julia); ML frameworks (Scikit-Learn, TensorFlow, PyTorch, etc.); agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar); LLMs, prompt engineering, RAG architectures; benchmarking/evaluation methodologies; SQL; cloud (AWS/Azure/GCP); Git, MLOps, containerization; experimental design, statistical analysis, research methodology.
- Communication: Strong communication/presentation; explain complex work to technical and non-technical stakeholders; conference/publishing experience preferred.
- Experience: 5+ years hands-on data science/ML/AI development or AI research; experience leading initiatives/teams preferred; pharmaceutical/life sciences preferred.
- Cross-functional collaboration: Proven experience leading complex projects with multi-functional teams.