Applied AI Analyst II

Bristol-Myers Squibb; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Bristol Myers Squibb is seeking an Applied AI Analyst II to advance Enabling Functions Analytics from static reporting to intelligent, reusable analytics products. The role blends practical AI research, context engineering, semantic modelling, and prototyping with governance.

You will explore how LLMs process context, build AI-enabled experiences, and connect governed data with modern analytics platforms using Claude, Databricks, and Power BI.

Qualifications

• Strong problem-solving with data-driven decisions.
• Understanding of how large language models work and token usage.
• Experience with analytics pipelines and AI prototyping.

Responsibilities

• Apply concepts to analytics and AI tasks with moderate guidance.
• Own research and prototyping workstreams, document results.
• Test LLM behaviours and translate findings into guidance.
• Design and optimise instructions, metadata, and retrieval strategies.
• Build text-to-SQL workflows with validated governance.

Skills

Analytical thinking

Structured problem-solving

LLM basics

Context engineering

Tools

Claude

Databricks

Power BI

Job description

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.
• Design and improve business-ready semantic models covering grain, dimensions, measures, relationships, hierarchies, synonyms, descriptions, and certified definitions, ensuring the same logic supports Power BI, Databricks, and AI experiences.
• Evaluate when to use prompting, retrieval-augmented generation, tool calling, multi-agent workflows, or traditional analytics, and define fit-for-purpose architectures rather than applying LLMs where deterministic methods are more appropriate.
• Create test cases and evaluation datasets for SQL accuracy, tool-call correctness, agent task completion, groundedness, latency, and cost. Investigate hallucinations, retrieval or semantic failures, and agent loops, and recommend corrective actions.
• Build proof-of-value solutions using Python, SQL, APIs, Claude, and Databricks, integrating AI capabilities with existing analytics assets and, where appropriate, Power BI or lightweight application experiences.
• Break ambiguous business questions into clear problem statements, hypotheses, data needs, and solution options. Rapidly learn unfamiliar domains and communicate trade-offs to technical and non-technical stakeholders.
• Document patterns, reusable components, guardrails, and lessons learned, and contribute to internal standards for context engineering, semantic models, LLM evaluation, and responsible AI adoption.
• Partner with business analysts, BI developers, data engineers, AI/ML specialists, architects, and governance teams to move successful prototypes toward sustainable products.
• Balance innovation with responsible AI, data governance, security, privacy, quality, and production-readiness expectations, including human-in-the-loop controls for consequential actions.

Skills and competencies

• Strong analytical thinking, curiosity, and structured problem-solving, including comfort with ambiguous questions and incomplete information.
• Practical understanding of how large language models work, including tokenisation, embeddings, attention, contex