Principal Research AI Innovation Lead
Bristol Myers Squibb
August 23, 2026
Remote friendly (Lawrence, NJ)
United States
Clinical Research and Development
Principal Research AI Innovation Lead
Responsibilities
- Partner with scientists to identify high-impact AI opportunities to improve speed, quality, consistency, traceability, and decision-making.
- Shape multi-year GenAI strategy; lead workstreams; build reusable components (agentic frameworks, evaluation harnesses, retrieval/grounding, tool servers, prompt/policy libraries, provenance).
- Architect and implement agentic “system-of-systems” for long-horizon research workflows (target evidence, indication rationale, biomarker interpretation, translational synthesis, evidence triangulation, decision support), including coordination, state/memory, verification, recovery, and production governance.
- Define scientifically rigorous evaluation/benchmarking/reliability standards (curated datasets, reference standards, rubrics, hallucination/grounding metrics, uncertainty calibration, longitudinal monitoring, regression gating).
- Incorporate expert feedback, scientific rationale, provenance, and research context to improve over time.
- Collaborate with engineering/data/IT/security/legal/vendor/platform teams for governance, integration, and scalability.
- Communicate AI opportunities/risks/limitations/evidence quality/results to scientific, technical, and executive audiences.
Basic Qualifications
- Bachelor’s + 8 years; or Master’s + 6 years; or PhD + 4 years academic/industry experience.
Preferred Qualifications (selected)
- Advanced degree (MS/PhD/PharmD or equivalent) in scientific/computational/AI field.
- Experience in drug discovery/translational research areas (e.g., target/indication/biomarkers/clinical evidence/portfolio decisions).
- Ability to assess whether AI outputs are scientifically grounded, defensible, and useful for real decisions.
- Hands-on architecture depth in modern LLM/agentic methods (multi-agent orchestration, long-horizon execution, GraphRAG, deep research/reasoning models).
- Experience evaluating/benchmarking AI systems; building domain-specific deep learning models.
- Innovation-lab/rapid-prototyping environment experience.
Benefits (explicitly listed)
- Health coverage (medical, pharmacy, dental, vision); wellbeing support (EAP, wellbeing programs); financial protection (401(k), disability, life/accident insurance, etc.).
Work-life benefits include paid time off (flexible time off/annual vacation and national holidays, eligibility-based).
Responsibilities
- Partner with scientists to identify high-impact AI opportunities to improve speed, quality, consistency, traceability, and decision-making.
- Shape multi-year GenAI strategy; lead workstreams; build reusable components (agentic frameworks, evaluation harnesses, retrieval/grounding, tool servers, prompt/policy libraries, provenance).
- Architect and implement agentic “system-of-systems” for long-horizon research workflows (target evidence, indication rationale, biomarker interpretation, translational synthesis, evidence triangulation, decision support), including coordination, state/memory, verification, recovery, and production governance.
- Define scientifically rigorous evaluation/benchmarking/reliability standards (curated datasets, reference standards, rubrics, hallucination/grounding metrics, uncertainty calibration, longitudinal monitoring, regression gating).
- Incorporate expert feedback, scientific rationale, provenance, and research context to improve over time.
- Collaborate with engineering/data/IT/security/legal/vendor/platform teams for governance, integration, and scalability.
- Communicate AI opportunities/risks/limitations/evidence quality/results to scientific, technical, and executive audiences.
Basic Qualifications
- Bachelor’s + 8 years; or Master’s + 6 years; or PhD + 4 years academic/industry experience.
Preferred Qualifications (selected)
- Advanced degree (MS/PhD/PharmD or equivalent) in scientific/computational/AI field.
- Experience in drug discovery/translational research areas (e.g., target/indication/biomarkers/clinical evidence/portfolio decisions).
- Ability to assess whether AI outputs are scientifically grounded, defensible, and useful for real decisions.
- Hands-on architecture depth in modern LLM/agentic methods (multi-agent orchestration, long-horizon execution, GraphRAG, deep research/reasoning models).
- Experience evaluating/benchmarking AI systems; building domain-specific deep learning models.
- Innovation-lab/rapid-prototyping environment experience.
Benefits (explicitly listed)
- Health coverage (medical, pharmacy, dental, vision); wellbeing support (EAP, wellbeing programs); financial protection (401(k), disability, life/accident insurance, etc.).
Work-life benefits include paid time off (flexible time off/annual vacation and national holidays, eligibility-based).