Applied AI Analyst II

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. The Applied AI Analyst II, Enabling Functions will help evolve Enabling Functions Analytics from static reporting toward intelligent, conversational, and reusable analytics products. The role combines practical AI research, context engineering, semantic modelling, and hands-on prototyping to connect governed enterprise data with large language models and modern analytics platforms. The individual will investigate how LLMs process context and generate responses, translate those learnings into reliable solution patterns, and build AI-enabled experiences using Claude, Databricks, Power BI, and related technologies. The role includes building text-to-SQL workflows, tool-using agents, and multi-agent systems that answer business questions reliably and transparently Roles & Responsibilities Apply working knowledge of concepts and procedures to a variety of analytics and AI assignments, using judgement to solve problems with a moderate level of guidance. Own well-defined research, experimentation, and prototyping workstreams, documenting assumptions, decisions, results, and limitations. Research and test LLM behaviours, reasoning patterns, context-window usage, tool use, structured outputs, and failure modes, and convert findings into practical design guidance and reusable patterns for analytics use cases. Design and optimise system instructions, business context, examples, metadata, retrieval strategies, and tool interfaces so AI responses are grounded, relevant, and consistent. Manage context quality, freshness, and token efficiency. Build text-to-SQL workflows that translate natural-language questions into governed queries, grounded in certified semantic models and metric definitions, with validation steps that check generated SQL before execution. Design tool-using agents that call approved functions and data services (such as SQL execution, metric lookup, and visualization generation) with clearly defined tool schemas, permissions, and fallback behaviour. Design and prototype multi-agent workflows in which specialist agents (for example, query planning, SQL generation, validation, and insight narration) coordinate under an orchestrator, with clear handoffs, state management, and loop and cost limits. Prototype chat-with-data and chat-with-document experiences that let users explore governed business information in natural language, while preserving traceability to approved data and metric definitions.