Sr Principal Data, Analytics & AI Engineer

Eli Lilly and Company • Us, IN

20h • $133,500 - $224,400 • Onsite

About The Position

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. We are seeking an experienced Senior Principal Data, Analytics & AI Engineer to join the Global DIA team within MQ Tech at Lilly. This role sits at the intersection of data engineering, supply chain domain expertise, and applied AI. You will architect the near-real-time data foundations that AI agents reason over and lead the design of the agents themselves — multi-agent workflows that read across manufacturing and supply chain systems, correlate signals that today sit in disconnected platforms, and put clear, actionable insight in front of the people running the line and the network. This is a senior principal, hands-on delivery role with technical leadership expectations. You will own solutions end to end, set technical direction for correlating supply chain and manufacturing data through data modernization and agentic AI, and mentor engineers on the team. The solutions you build reach production and are used every shift, bringing transparency and decision support to Lilly's Supply Chain and Manufacturing operations.

Requirements

• Bachelor’s in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or related field with 8+ years in data engineering, analytics, or AI/ML, with hands-on production Gen-AI/agentic app delivery.
• Experience in supply chain/manufacturing data, hands-on SAP (S/4HANA/ECC, BW/4HANA) , SHARP, MES and related data and analytics experience.
• Hands-on advanced statistical, machine learning, LLM/agentic app development: prompt engineering, tool/function calling, RAG, vector search/embeddings, multi-step workflows.
• Deep knowledge of agent frameworks (LangChain, LangGraph, or equivalent) and production LLM APIs (OpenAI, Azure OpenAI, Anthropic, Bedrock); Azure/AWS/Databricks.
• Advanced Python (preferred); SQL,Java/Scala/R a plus, PowerBi/Tablue, Web Apps etc
• Experience in end-to-end developing production grade analytics systems.

Nice To Haves

• Masters in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or related field with 5+ years in data engineering, analytics, or AI/ML, with hands-on production Gen-AI/agentic app delivery.
• Experience in pharmaceutical, life sciences, or other regulated manufacturing supply chains.
• Track record of taking multiple AI or agentic solutions through to production, including evaluation, monitoring, and iteration after go-live.
• Strong command of manufacturing processes and metrics (OEE, downtime, yield, batch performance, right-first-time) alongside supply chain metrics.
• Experience with supply chain planning software (SAP IBP, Kinaxis, o9) and IoT/streaming operational data.
• Exposure to manufacturing data sources such as historians (OSI PI), MES/TrakSYS, PMX, or LIMS.
• Experience working in a GxP-regulated environment, including CSV and validation practices.
• Experience with data visualization tools (Power BI preferred).
• Applied knowledge of responsible AI practices: model risk, bias, explainability, and data privacy.
• Experience leading globally distributed teams or vendor partners.
• Databricks, AWS or Azure certifications.

Responsibilities

• Own the technical design and architecture for agentic AI and data modernization solutions across the Global Supply Chain and MQ landscape, from problem framing through production support.
• Set standards and reusable patterns for cloud based data modeling, pipeline design, agent orchestration, and evaluation that other engineers build on.
• Mentor and provide technical guidance to junior and mid-level engineers; lead code and design reviews.
• Partner with senior business stakeholders and IT leadership to shape roadmaps, sequence delivery, and make build/buy trade-offs; direct and hold accountable external vendor and partner teams.
• Develop and apply deep working knowledge of Global Supply Chain data domains — material master, BOM and recipe, procurement, inventory and stock movements, production orders and batch execution, planning and scheduling, warehouse management, transportation management, distribution and logistics, quality and deviations.
• Work fluently across SAP (S/4HANA, ECC, BW/4HANA — master data, MM, PP, QM, SD, and IBP) and SHARP , translating source-system semantics into trusted, business-ready data models.
• Bring an informed point of view on end-to-end supply chain processes — S&OP, MRP, plan-to-produce, source-to-pay, order-to-cash, capacity and inventory management — and the analytics that support them, including service level, inventory turns, cycle time, schedule adherence, and supply risk.
• Correlate supply chain and manufacturing data — linking ERP, planning, MES, historian, and quality data across systems and time — to produce integrated business solutions that answer questions no single system can, using data modernization and agentic AI.
• Closely collaborate with the business & other global teams on agentic solution design, requirement gathering, solution development and delivery to drive meaningful business value
• Design, build, and deploy AI agents and multi-agent workflows that automate analysis and decision support across manufacturing and supply chain use cases — including prompt engineering, tool and function calling, agent orchestration, and retrieval-augmented generation (RAG) over technical documents and operational data.
• Define how agent performance is measured and improved: test scenarios, evaluation criteria, accuracy and hallucination monitoring, user feedback loops, agent observability, and guardrails that keep agent behavior safe, grounded, and within scope.
• Implement, test, and deploy machine learning and predictive models into production, ensuring performance, scalability, and interpretability.
• Design, build, and maintain scalable, reliable data pipelines using modern ETL/ELT tooling to ingest, process, and transform large datasets from diverse operational and enterprise sources in support of advanced analytics, reporting, and agentic AI.
• Lead data modernization efforts — migrating and re-platforming legacy supply chain reporting and data assets onto modern lakehouse and cloud architectures.
• Monitor and troubleshoot data quality and agent behavior issues, ensuring data integrity and reliability across all platforms.
• Develop and maintain documentation for data architecture, data flows, agent designs, prompts, model choices, and deployments to support maintainability, validation, and GxP/CSV requirements.
• Ensure compliance with all relevant data privacy, security, and responsible AI requirements.
• Stay abreast of the fast-moving agentic AI and data engineering landscape and bring practical, proven advances into the team's work.

Benefits

• company bonus (depending, in part, on company and individual performance)
• company-sponsored 401(k)
• pension
• vacation benefits
• medical, dental, vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Principal