Jobs at Bristol Myers Squibb

Hands-on individual contributor designing and delivering scalable data and AI analytics for Global Patient Operations. Build ETL pipelines, data models, semantic layers, dashboards (Power BI/Tableau), and AI/automation solutions using SQL, Python, dbt, Databricks and cloud platforms. Lead requirements, UAT, production rollouts, data quality, governance, and provide technical code-review leadership.

As a Senior Data Scientist, you will lead AI product hypotheses and experimentation, working with cross-functional teams to develop and evaluate AI solutions across various company functions. Responsibilities include designing datasets, building analytical features, and measuring product impacts using AWS tools and industry-best practices.

As Associate Director, lead the DocGen platform's engineering lifecycle, oversee rearchitecting projects, ensuring security and scalability while collaborating with teams to implement best practices and innovative solutions.

Lead global regulatory strategy for Neuroscience development and marketed products. Serve as primary interface with health authorities, build global regulatory plans, advise cross-functional teams, evaluate regulatory landscape, support due diligence and submissions, and guide regional regulatory staff to align development and commercialization strategies.

The Manager will develop analytics and AI tools to answer business strategy questions and support decision-making within the Product Development and Supply team at Bristol Myers Squibb.

The Senior Application Engineer will develop cloud-native applications and APIs, focusing on AI products, utilizing AWS technologies, and engaging in Agile project cycles.

As a Senior Frontend Engineer, you'll lead frontend technical delivery and build React-based applications, collaborating with AI engineers and other teams to create trusted enterprise-ready product experiences.

Lead and develop a therapeutic-area team of scientific writers, applying advanced regulatory documentation expertise to create content libraries, standardize processes and tools, and drive document automation initiatives. Provide managerial, scientific, and customer-focused leadership to ensure consistent regulatory documentation across development and lifecycle management globally.

As a Principal AI Engineer, lead AI Accelerator projects, manage a multi-disciplinary team, and deliver generative AI solutions across various business processes.

The Senior Manager will drive advanced analytics using data science tools to support business strategy and decision-making in drug development and product launch processes. Responsibilities include building machine learning models, ensuring coding best practices, and collaborating with business leaders to provide data-driven insights.

Design and maintain reusable GenAI architectures, templates, and components; enable and embed with AI Accelerator pods to solve complex technical problems; provide technical leadership, mentorship, and promote responsible AI practices for scalable enterprise adoption.

Design, build, and operate secure AWS-native backend services, APIs, and agent runtimes for agentic AI products. Implement retrieval, semantic layers, CI/CD, observability, sandboxed execution, and infrastructure-as-code while partnering across cross-functional pods to deliver and scale MVPs.

Manage the Manufacturing Optimization Analytics team to develop data analytics tools and AI for business operations, enabling effective product development, manufacturing, and distribution decisions. Collaborate with stakeholders to address key business questions and enhance team productivity through standardized methodologies.

Coordinate, curate, validate, and maintain high-quality, reproducible research datasets for translational and clinical studies. Manage vendor/CRO data transfers, perform QC and data queries, develop data management plans and AI-ready datasets, collaborate with scientists and data engineers, and support data governance, standardization, and tooling to enable analytics and digital transformation across R&D.

Lead and execute non-interventional retrospective oncology research using large datasets; develop protocols, perform statistical programming and analyses, communicate results to stakeholders, provide methodological leadership, mentor staff, and collaborate with HEOR, medical, and market access teams to support value-focused outcomes research.