Principal Research AI Innovation Lead

Bristol Myers Squibb

6d•$145,020 - $175,728•Remote

About The Position

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. When you join BMS, you are joining a high-achieving team united by a common mission. The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning—across the full lifecycle of drug discovery and development and across all therapeutic areas at BMS. We do this in close partnership with scientific and clinical experts in the field, both inside and outside the company. We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers. Here, you’ll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You’ll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma. Principal Research AI Innovation Lead We are seeking a Principal Research AI Innovation Lead to design, prototype, and scale AI-enabled capabilities that accelerate scientific research. This role will work across Research, AI, data, product, engineering, and enterprise technology teams to identify high-value opportunities, build practical LLM-enabled solutions, evaluate scientific quality, and create reusable capability patterns that improve how research teams use AI. The ideal candidate combines hands-on AI product and prototyping experience with working fluency in drug discovery, translational science, or a related research domain. They can assess whether an AI output is scientifically sound, appropriately grounded, and useful for real research decisions - not merely technically complete. They are comfortable engaging with scientists on topics such as target evidence, indication selection, biomarker interpretation, translational rationale, or clinical evidence, and equally comfortable partnering with AI engineers to turn those needs into scalable systems. This role’s impact is measured by scientific outcomes: faster and better-evidenced research decisions, higher-quality AI-assisted workflows, and reusable capabilities that compound across programs.

Requirements

Bachelor's Degree 8+ years of academic / industry experience
Or Master's Degree 6+ years of academic / industry experience
Or PhD 4+ years of academic / industry experience

Nice To Haves

Advanced degree, such as MS, PhD, PharmD, or equivalent experience in a scientific, computational, or AI-related field.
Direct experience in one or more research areas such as target identification and evaluation, indication expansion, drug repurposing, biomarker discovery, translational research, clinical evidence review, or portfolio decision support.
Ability to interpret scientific evidence, assess analytical quality, and evaluate whether AI-generated scientific outputs are grounded, appropriately caveated, and defensible.
Substantive hands-on architectural depth in modern AI and large language model methods, including agentic workflows, multi-agent orchestration, long-horizon task execution, GraphRAG, Model Context Protocol (MCP) auth patterns, and deep research workflows with reasoning models, with the depth to define reference architectures and architectural standards rather than to integrate vendor APIs.
Experience building AI systems that reason across heterogeneous scientific evidence, including genetic associations, clinical outcomes, literature, omics data, assay data, real-world data, or other biomedical data sources.
Experience designing AI evaluation frameworks, benchmark datasets, human-reviewed reference standards, rubric-based assessments, or scientific quality metrics.
Experience developing or adapting deep learning models for biological, biomedical, or translational research applications, including fine-tuning biology-focused large language models, multimodal generative models, protein or sequence foundation models, representation-learning models, or other domain-specific AI systems.
Experience working in innovation-lab, accelerator, startup, skunkworks, or rapid-prototyping environments.

Responsibilities

Partner with scientists and research leaders to identify high-impact opportunities where AI can improve research speed, quality, consistency, traceability, and decision-making.
Help shape multi-year GenAI strategies, lead workstreams, and establish reusable building blocks - agentic frameworks, evaluation harnesses, retrieval and grounding components, tool servers, prompt and policy libraries, and provenance infrastructure - on which research programs build.
Architect and personally implement the agentic system-of-systems that executes complex, long-horizon scientific workflows across research, including target evidence assembly, indication rationale construction, biomarker interpretation, translational synthesis, literature and evidence triangulation, and decision support, with explicit attention to inter-agent coordination, state and memory management, verification, recovery from intermediate failure, and lifecycle governance of agents in production.
Establish the scientifically rigorous evaluation, benchmarking, and reliability standards that critical research AI systems expected to meet, including curated benchmark datasets, expert-reviewed reference standards, rubric-based assessments, hallucination and grounding metrics, calibration of uncertainty, longitudinal monitoring, regression gating in production, and the governance under which those standards are applied.
Design mechanisms for incorporating expert feedback, scientific rationale, provenance, and research context into AI workflows so that systems and institutional knowledge improve over time.
Collaborate with engineering, data, IT, security, legal, vendor, and platform teams to ensure prototypes are designed with appropriate governance, integration paths, and scalability in mind.
Communicate AI opportunities, risks, limitations, evidence quality, and results clearly to scientific, technical, and executive audiences.
Stay current with emerging AI methods, tools, vendors, and industry practices, and assess where they can create practical value for Research.

Benefits

wellbeing support
retirement and financial protection benefits
insurance offerings (medical, dental, vision, life and disability)
Flexible Time Off (FTO)
11 paid company holidays
160 hours of paid vacation annually
3 optional holidays
paid sick leave
up to two paid volunteer days per year
summer hours flexibility
leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs
annual Global Shutdown between Christmas Day and New Year's Day