Data Scientist III

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. Roles and Responsibilities Collaborate with business to design, develop, and deploy advanced data science and AI solutions leveraging machine learning, large language models (LLMs), and modern data platforms. Work with large-scale structured and unstructured datasets including clinical data, publications, real-world evidence, and other healthcare datasets. Build predictive models and advanced analytics solutions to generate actionable insights and support data-driven decision making. Develop LLM-powered applications such as AI assistants, knowledge-grounded reasoning systems, and Retrieval-Augmented Generation (RAG) pipelines. Develop and maintain semantic layers, ontologies, and contextual data frameworks that enable consistent interpretation of data across AI systems and analytics workflows. Implement evaluation frameworks for AI systems, response quality assessment, hallucination mitigation, and benchmark-driven validation. Partner with data engineering and platform teams to ensure scalable pipelines, high-quality data availability, and production-ready AI systems. Communicate analytical findings and AI-driven insights clearly to both technical and non-technical stakeholders through reports, visualizations, and presentations. Mentor junior data scientists and contribute to knowledge sharing and adoption of modern AI development practices. Skills and Competencies Strong expertise in machine learning, and predictive modeling techniques. Strong programming proficiency in Python for data science and AI system development Experience working with modern data platforms such as Databricks (Spark, MLflow, Delta Lake). Experience building LLM-based applications, including Retrieval-Augmented Generation (RAG), AI copilots, and knowledge-grounded AI systems. Experience implementing embedding pipelines and vector database architectures (e.g., FAISS, Pinecone, Weaviate, OpenSearch Vector, pgvector). Familiarity with agentic AI frameworks such as LangGraph, LangChain, CrewAI, or similar orchestration frameworks. Experience designing multi-step AI workflows, including tool calling, context management, and agent-to-agent interaction. Understanding of LLMOps practices, including model deployment, monitoring, prompt engineering, evaluation workflows, and experiment tracking. Experience developing and deploying AI or ML services using FastAPI or similar frameworks for scalable APIs.