Organization SummaryThe CMC Portfolio Analytics & Insights (PAI) team is an integral part of CMC Project Management within Lilly Research Laboratories' Product Research and Development organization. The CMC Project Management team is responsible for leading the design and execution of integrated CMC strategies from portfolio entry through global submissions and approvals.
The PAI is a newly established, strategic team dedicated to leveraging data analytics to transform CMC project management. This team will transform CMC portfolio data into a strategic asset for decision-making-tracking PRD portfolio metrics, driving PM tool development, and setting data standards. As a key enabler for data-driven decision making, this team has significant opportunities to create substantial impact by providing actionable insights, optimizing portfolio performance, and supporting strategic initiatives across our CMC operations. Join us at this pivotal moment and play a key role in shaping the future of CMC project management.
Position SummaryWe are seeking a highly motivated and skilled AI/ML Product Owner to join our CMC Portfolio Analytics & Insights Team. As a Sr. Advisor - AI/ML Product Owner, you will lead the product strategy, roadmap, and delivery of innovative AI/ML-driven solutions that transform our pharmaceutical processes and accelerate drug discovery and development. You will bridge the gap between business needs and technical execution, ensuring the successful realization of high-impact AI/ML products.
ResponsibilitiesData Product Ownership- Own the CMC Operational Data Product end-to-end - definitions, data contracts, quality standards, and lifecycle.
- Partner with the Data Foundation workstream to enforce naming and field standards, automate pipelines, and eliminate manual reconciliation across PRD.
- Define and track data-product health KPIs (freshness, completeness, lineage, adoption) and report on them.
- Make and document the governance decisions that keep the product trustworthy - which source becomes the authoritative record, which duplicate reports and trackers are retired, and how changes are communicated to users.
AI/ML Architecture - Design and build the AI interactive layer on top of the governed Data Hub and Power BI semantic model, engineering it so agent queries execute under the requesting user's authenticated identity.
- Build the AI accuracy/validation framework needed to move capability from today's "AI mindset" (data prep, summarization, pattern-spotting, always human-owned) to tomorrow's core capability (predictive risk models, early-warning signals, ask-the-portfolio).
- Prototype and iterate AI capability against real use cases already in flight, and report accuracy against validated outcomes.
Hands-On Technical Build- Independently architect and build core components - pipelines, semantic-model extensions, AI/agent interfaces - this is a working architect role, not a pure oversight role.
- Set technical standards, review practices, and reusable architecture patterns that the wider data engineering and analytics/visualization functions build against.
Cross-Team Leadership, AI Storytelling & IT Partnership- Turn technical AI and data work into compelling stories - translate pipelines, model behavior, and architecture decisions into narratives that make AI capability tangible, credible, and easy to back for non-technical leadership.
- Act as a constructive challenger of the status quo - question legacy manual processes, parallel trackers, and entrenched ways of working, and make the case for change even when it's uncomfortable or meets resistance.
- Coordinate day-to-day with the Data Engineer and Insights Lead on the PAI team so the AI layer stays aligned with the data foundation and the insights being surfaced.
- Partner directly with IT/Tech[redacted] to take the Data Hub and AI layer from MVP to enterprise-grade.
Stakeholder Engagement & Governance- Represent the team's AI and data architecture in cross-functional governance forums and on a cross-functional data and AI advisory board.
- Translate technical architecture and AI capability into a business narrative.
- Provide credible technical grounding for roadmap and leadership review materials.
- Ensure compliance with all relevant industry regulations, data privacy standards, and company policies.
Basic Requirements- Ph.D. or Master's degree preferred or Bachelor's degree required in Computer Science, Engineering, Data Science, Bioinformatics, or a related quantitative field with the following years of relevant experience: PhD with 6+ years, Master's with 9+ years or Bachelor's with 12+ years.
- 8-10+ years' experience building production data and AI/ML architectures
- Proven track record of successfully defining, launching, and scaling AI/ML-powered products from conception to widespread adoption.
- Strong understanding of various machine learning algorithms, deep learning techniques, natural language processing (NLP), and computer vision.
- Experience with AI/ML platforms, tools, and cloud services (e.g., AWS SageMaker, Google AI Platform, Azure ML).
- Demonstrated ability to translate complex technical concepts into business terms and effectively communicate with both technical and non-technical stakeholders.
- Excellent analytical, problem-solving, and decision-making skills.
- Demonstrated willingness to challenge existing processes, tools, or decisions and drive change without waiting for formal authority to do so.
- Strong stakeholder communication skills and comfort presenting technical tradeoffs to senior and executive leadership.
- Experience partnering with IT/enterprise infrastructure teams to move a solution from pilot or MVP into supportable, production-grade scale - cloud hosting, security review, and ongoing operations.
- Experience with data governance, MLOps, and responsible AI practices.
Preferred Qualifications- 3-5 years' experience designing solutions on governed enterprise data - row-level security, identity-based access control, and semantic modeling.
- Strong leadership, interpersonal, and communication skills, with the ability to influence and inspire cross-functional teams.
- Experience working in an agile development environment (Scrum, Kanban).
- Familiarity with the pharmaceutical industry, drug discovery, clinical development, or life sciences research is highly desirable.
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$144,000 - $231,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for 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; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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