AD/Lead Consultant- Data Strategy and Operations

Overview

Lead the definition and operationalization of GOSO's centralized data and AI strategy, architecting the AI-ready data foundation for agentic AI, machine learning pipelines and advanced analytics across US and International Business Unit (IBU) markets, while leading and mentoring a small team across data engineering, applied ML and AI operations.

What you'll do

• Define and operationalize the GOSO-wide data and AI strategy in partnership with Tech@Lilly and GOSO Functional Teams (SFO, IC, DT), supporting next-generation AI/ML workloads including agentic AI, RAG, and semantic reasoning layers, 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, designing for 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 IBU-specific use cases such as 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 HQ and IBU workloads.
• Lead the design of multi-agent architectures with specialized AI agents in an orchestrated ecosystem, including tool-use patterns, memory management, and human-in-the-loop escalation, 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, a unified roadmap, and representation of IBU requirements in enterprise-level planning.
• Drive AI literacy and adoption enablement across GOSO functional stakeholders, including IBU affiliate teams, ensuring predictable onboarding of new data assets, ML models, and agentic workflows.
• Identify opportunities to reduce manual interventions and accelerate insight generation through ML-driven data quality monitoring, self-healing pipelines and intelligent automation, eliminating repetitive market-by-market manual processes.
• 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.

What you'll need

• Bachelor's Degree in Computer Science, Engineering, Data Science, AI/ML, or related fields
• 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, including how data foundations enable ML and agentic AI at scale
• Practical experience with Databricks (including Unity Catalog, MLflow, Feature Store, and/or Agent Bricks)
• Practical experience with AWS services (S3, EC2, RDS, Redshift)
• Expertise in SQL
• Proficiency in Python, including ML libraries such as scikit-learn, PyTorch, or LangChain/LangGraph
• Strong understanding of ETL/ELT processes and semantic layer design
• 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

Nice to have

• Advanced degree
• Prior experience with International Business Unit or multi-affiliate pharma commercial operations, including 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

Details

• Location: Lilly Bengaluru, India
• Reports to the GOSO DSO Lead