AD/Lead Consultant- Data Strategy and 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.

Company Overview

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees work to discover and bring life-changing medicines to those who need them, improve disease management, and give back to our communities through philanthropy and volunteerism. We bring our best to our work — and we put people first. We’re looking for individuals who are determined to make life better for people around the world.

Responsibilities

The GOSO Data Strategy & AI Lead will report to the GOSO DSO Lead and play a critical role in defining and operationalizing GOSO’s centralized data and AI strategy. This role will architect the AI-ready data foundation required for agentic AI systems, machine learning pipelines, and advanced analytics across GOSO — with particular attention to the complexity and scale of US and International Business Unit (IBU) markets, affiliate data ecosystems, and cross-market harmonization challenges. This role will also lead and mentor a small team — developing talent across data engineering, applied ML, and AI operations.

Key Accountabilities

- Define and operationalize the GOSO-wide data and AI strategy in partnership with Tech@Lilly and GOSO Functional Teams (SFO, IC, DT), ensuring the platform supports next-generation AI/ML workloads including agentic AI, RAG, and semantic reasoning layers — while accounting for IBU-specific multi-market data heterogeneity and varying data maturity across geographies.
- Translate functional and business requirements into scalable Data Spine implementations on AWS/Databricks that support feature stores, semantic layers, and data contracts required by downstream ML and agentic AI systems — designing for IBU scalability with repeatable, templatized onboarding of new affiliate markets without bespoke rework.
- Architect data structures, semantic ontologies, and API-accessible data services that enable agentic AI capabilities — prioritizing use cases that address IBU-specific pain points like multi-market reconciliation and cross-geography performance benchmarking.
- Drive the design and deployment of production ML pipelines — from feature engineering and model training through deployment, monitoring, and continuous improvement — establishing MLOps patterns (experiment tracking, model versioning, drift detection, automated retraining) that scale across both HQ and IBU workloads.
- Lead the design of multi-agent architectures where specialized AI agents operate within an orchestrated ecosystem with well-defined tool-use patterns, memory management, and human-in-the-loop escalation — ensuring agent designs are adaptable to US/IBU contexts where business rules and decision thresholds vary by market.
- Serve as the primary point of contact for T@L on all data and AI-related initiatives, ensuring aligned priorities, transparent execution, and a unified roadmap that accounts for platform evolution, emerging AI capabilities, and enterprise MLOps standards — while representing IBU requirements in enterprise-level planning.
- Drive AI literacy and adoption enablement across GOSO functional stakeholders — including IBU affiliate teams where AI readiness may vary significantly — ensuring predictable onboarding of new data assets, ML models, and agentic workflows into the GOSO ecosystem.
- Identify opportunities to reduce manual interventions and accelerate insight generation through ML-driven data quality monitoring, self-healing pipelines, and intelligent automation — with particular focus on eliminating repetitive market-by-market manual processes common in US/IBU operations.
- Develop, mentor, and coach a small team of analysts, data engineers, and AI practitioners — ensuring capability development across data engineering, applied ML, and AI operations, with cross-functional exposure to data and AI domains.

Basic Qualifications / Technical Skills

- Bachelor’s Degree (preferably in Computer Science, Engineering, Data Science, AI/ML, or related fields); advanced degree preferred.
- 10+ years of experience in pharmaceutical commercial data engineering, data platforms, analytics ecosystems, and/or applied AI/ML.
- Strong understanding of cloud-based data architectures and pharma commercial ecosystems — with a clear grasp of how data foundations enable ML and agentic AI at scale.
- Practical experience with Databricks (including Unity Catalog, MLflow, Feature Store, and/or Agent Bricks), AWS services (S3, EC2, RDS, Redshift), and modern data engineering/AI best practices.
- Expertise in SQL; proficiency in Python (including ML libraries such as scikit-learn, PyTorch, or LangChain/LangGraph for agentic frameworks).
- Strong understanding of ETL/ELT processes, semantic layer design, and experience with orchestration platforms such as AWS Glue or Databricks workflows.
- Hands-on MLOps experience — model versioning, experiment tracking, deployment pipelines, monitoring, and drift detection.
- Experience designing or deploying agentic AI systems, multi-agent orchestration, or tool-use agent architectures in a production or near-production setting.
- Ability to quickly assess upstream/downstream impacts for data, model, and system changes across interconnected analytics and AI workloads.
- Strong stakeholder management and prior experience operating in a matrixed global environment.

Additional Skills / Preferences

IBU / International Commercial Domain (Strongly Preferred):

- Prior experience with International Business Unit or multi-affiliate pharma commercial operations — understanding of how data flows, vendor ecosystems, and commercial models differ across US and ex-US markets (Japan, Europe, Emerging Markets).
- Familiarity with IBU-specific data challenges: multi-vendor sourcing (IQVIA, local vendors), cross-country definition variability, affiliate-level sales/demand structures and market-specific compliance requirements.
- Experience with IBU/US commercial analytics domains such as incentive compensation, sales force operations, dynamic targeting, or demand forecasting in a multi-market context.
- Understanding of global vs. local data ownership models in a matrixed IBU environment — and how to design AI systems that respect affiliate context while enabling centralized intelligence

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

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