Associate Director — PR&D Delivery & Operations

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. At Lilly, the data and digital capability that underpins every regulatory submission, batch investigation, and process decision across CMC (Chemistry, Manufacturing, and Controls) is being rebuilt rather than maintained. Time-to-insight for scientists and process engineers, audit- and inspection-readiness, and the cost of running PR&D's platforms all depend on these capabilities. This role is the mechanism by which the portfolio moves from sustaining the current estate to reshaping it: from request-taking to business partnership, from manual data and process work to AI- and agent-supported execution, and from vendor dependence to retained Lilly capability. The Associate Director, PR&D Delivery & Operations leads broader delivery teams across CMC data platform modernization (structured and unstructured data, platform operations), scientific workflow automation (LIMS, MES, ELN), data governance and business alignment, and AI & Analytics (AI-use-case delivery) — accountable for delivery, technical direction, team capability and measurable business outcomes across all three. The role sits where CMC scientific and data process meets engineering: credible enough to lead a design review and challenge an architecture, fluent enough in regulated CMC data work to shape how a scientific or process team actually works. Split roughly 60% delivery and technical leadership and 40% strategy, business partnership and vendor leadership, it owns the demand relationship with PR&D business owners, directs the partner teams delivering a material share of the work, and supports the portfolio's AI and agentic capability agenda as one part of a broader delivery mandate. Key Responsibilities Portfolio Delivery Ownership — own delivery across the portfolio — scope, schedule, quality, dependencies, release readiness and production health — for CMC data and semantic infrastructure (knowledge graph, ontology, document and structured-data ingestion), platform modernization and operations, and AI-use-case delivery, directing the broader teams accountable for each track. Run delivery on real signals: flow, predictability, defect escape and adoption by scientific and process teams, not status reporting. Technical Direction & Engineering Standards — set and defend the technical direction of the portfolio, contributing to how CMC data platforms support the goals of the wider PR&D business area; review architecture and design, challenge weak decisions, and own standards for APIs, data contracts, ontology and knowledge-graph modeling (RDF/OWL/SPARQL, property graphs), and tool-calling interfaces (MCP) across CMC data platforms. Raise the engineering bar on test automation, CI/CD, observability and the deliberate reduction of technical debt, staying close enough to the work to judge trade-offs credibly. AI & Agentic Delivery Support — support delivery of AI and agentic use cases already on the portfolio roadmap — document and schema intelligence, retrieval over CMC content, and knowledge-graph-powered Q&A or summarization agents — ensuring these initiatives get the same delivery rigor, resourcing and release discipline as any other workstream. Help the team bring new use cases forward for evaluation, and support them through validation and into production with appropriate guardrails and human-in-the-loop review. Build AI-assisted engineering into the team's everyday practice, in step with the broader AI strategy set elsewhere in the portfolio. Business Partnership & Stakeholder Management — act as the technology partner for PR&D business owners and domain product managers across the portfolio's data, platform and AI-use-case tracks — the person they call first, and the person who tells them when an idea will not work. Capture and challenge incoming demand, agree priorities against capacity, and keep one honest view of the roadmap and what has been traded away. Translate CMC and scientific need into technical options and constraint, risk and cost into language a business owner can act on; set a rhythm of business reviews and early warning so there are no surprises at release; and build the case for modernization investment, with the benefit measurement that follows it. Vendor & Partner Delivery Management — get the work done through partner teams: define scope, outcomes and acceptance criteria, and hold suppliers to the same engineering, quality and GxP standards as internal teams. Shape the sourcing mix for the portfolio, plan partner ramp-up and ramp-down against the roadmap, manage supplier performance on predictability, quality and knowledge retention, and work with sourcing, legal and finance on statements of work, capacity models and change requests. Integrate partner engineers into one team, one backlog and one set of standards, and protect institutional knowledge through documentation, code ownership and cross-training. Quality, Risk & GxP Compliance — deliver in a GxP-regulated environment — computer system validation, 21 CFR Part 11, data integrity, inspection and audit readiness. Design for security and data privacy from the start, meet architecture and security review obligations early rather than at release, and own incident response and service levels for the products the teams run. People Leadership & Capability Building — manage, coach and grow broader delivery teams spanning multiple sub-teams and disciplines: clear expectations, specific feedback, honest check-ins and a development plan each person recognises as their own. Hire for the capability the portfolio needs next — engineering depth across data, knowledge-graph and AI-use-case delivery, product thinking, CMC domain fluency — address underperformance without delay, build bench strength and succession, and remove single points of failure. Hold high standards and psychological safety at the same time across a distributed team. Decision-Making Authority — make design, tooling, sequencing and sourcing decisions within the guidelines set by architecture, engineering and validation standards; challenge product, engineering or vendor approaches where delivery or compliance risk exists; forecast resource needs across the portfolio and manage the allocated budget against plan; contribute to PR&D's business-area plans by translating the modernization roadmap into resourced, executable delivery cycles; and escalate policy-level or cross-platform decisions with a recommendation rather than a question. Technical & Capability Areas CMC Data Systems (LIMS, MES, ELN) · Data Engineering & Data Platforms · Knowledge Graph & Ontology Engineering (OWL/RDF/SPARQL, Neo4j/property graphs) · Semantic Layer & Retrieval (Vector Databases, Hybrid/Graph-RAG) · AI/ML & Agentic Systems (LLMs, Multi-Agent Orchestration, Model Context Protocol) · Modern Software Development Practices (SDLC, CI/CD, Test Automation) · Cloud Platforms (Azure/AWS) · API & Integrat