Director, Software Product Management – Discovery Research Platforms

Position Description

Eli Lilly is seeking an experienced Product Management leader to help shape the strategy and development of custom software platforms supporting discovery research, with a primary focus on computational drug design and optimization for large molecules. You will be part of a cross-functional product area alongside engineering, computational biology, and research stakeholders – collaborating to build sophisticated systems that orchestrate multi-objective optimization workflows, integrate data from Next Generation Sequencing (NGS), high-throughput assays, and in silico modeling, and bring agentic AI capabilities into the hands of discovery scientists.

Primary Position Responsibilities

• Product strategy for computational drug design & optimization (40%)
  • Contribute to and help drive the product strategy, roadmap, and vision for computational drug design and optimization platforms, translating scientific research objectives into a coherent product direction that aligns with Lilly’s broader discovery and AI strategies
  • Deeply understand how computational biologists design, execute, and iterate on multi-objective optimization (MOO) campaigns for therapeutic candidates, and translate those needs into platform capabilities that standardize and accelerate these workflows
  • Identify and prioritize opportunities to integrate agentic AI capabilities into discovery workflows, including intelligent pipeline composition, automated experimental recommendations, and conversational interfaces for scientists to interact with complex computational systems
  • Establish and track key metrics to measure product success, adoption, and scientific impact, using data to inform investment decisions and communicate value to leadership
  • Explore adjacencies across broader lab and research platform areas where shared infrastructure, data integration, or agentic capabilities could extend the impact of the product area
• Technical translation & engineering partnership (35%)
  • Work as part of a cross-functional product team with engineering, computational biology, and research stakeholders to translate complex scientific requirements into clear product specifications and execution plans, requiring understanding of: Next Generation Sequencing (NGS) data structures, quality metrics, and bioinformatics pipeline architectures; Protein structure prediction algorithms (AlphaFold, ESMFold) and molecular dynamics simulation principles; High-throughput screening data analysis and dose-response modeling; Vector database architectures for biological sequence similarity search; Workflow orchestration patterns for multi-step computational pipelines across heterogeneous compute environments (cloud, GPU clusters, internal platforms)
  • Guide the integration of AI/ML capabilities into drug design and optimization, including deep learning architectures for protein engineering, generative models for antibody sequence design, active learning strategies for experimental optimization, and multi-objective optimization for therapeutic candidate selection
  • Help shape the platform’s approach to agentic capabilities – working with engineering to design systems where AI agents can assist scientists in composing workflows, interpreting results, and suggesting next experiments
  • Assess technical feasibility and complexity when prioritizing features, working closely with engineering to understand implementation trade-offs and architectural implications
  • Guide the integration of AI/ML capabilities into drug design and optimization, including deep learning architectures for protein engineering, generative models for antibody sequence design, active learning strategies for experimental optimization, and multi-objective optimization for therapeutic candidate selection
  • Help shape the platform’s approach to agentic capabilities – working with engineering to design systems where AI agents can assist scientists in composing workflows, interpreting results, and suggesting next experiments
  • Work closely with engineering teams to ensure high-quality, on-time delivery, actively unblocking teams by clarifying requirements, resolving ambiguity, and making priority decisions in real time
• Cross-functional collaboration & stakeholder partnership (25%)
  • Build and maintain strong partnerships with research scientists, protein engineers, computational biologists, lab automation teams, and research leadership across both Indianapolis and San Diego sites
  • Champion user needs and experience while balancing technical feasibility and business value, applying user-centered design principles and scientific workflow analysis
  • Drive product development using agile methodologies, ensuring regular delivery of high-value features through sprint planning, backlog prioritization, and stakeholder alignment
  • Communicate progress, risks, and strategic direction to SPE leadership and broader Lilly stakeholders as part of the cross-functional product area
  • Contribute to the growth of Lilly’s product management discipline through mentorship, community of practice participation, and sharing of best practices from the discovery research domain

Basic Qualifications

• Bachelor’s degree in bioinformatics, computational biology, computer science, biomedical engineering, or biochemistry/molecular biology with demonstrated computational coursework
• 10 years of progressive software product management experience with a proven track record of taking complex technical products from vision through launch and sustained adoption
• 5 + years of experience working in life sciences, laboratory environments, drug discovery, or biopharmaceutical research settings

Additional Skills And Preferences

• Demonstrated success driving product strategy and execution in complex, highly regulated organizational environments at a senior level
• Experience working within cross-functional product teams and influencing without direct authority across matrixed organizations
• Masters or Ph.D. in computational biology, bioinformatics, or related field
• Experience with scientific software platforms or research tools in pharmaceutical R&D
• Deep understanding of AI/ML applications in life sciences and therapeutic development, including emerging agentic and generative AI approaches
• Familiarity with computational drug design and optimization workflows, particularly multi-objective optimization for biologics
• Experience managing products in regulated environments (FDA, GxP)
• Track record of successful cross-functional collaboration in matrix organizations at senior levels
• Experience with Agile/Scrum methodologies and product management tools (Jira)
• Strong technical background with understanding of modern software development processes, cloud architectures, and API-driven systems
• Interest in and aptitude for working across adjacent research and lab platform domains as the portfolio evolves
• Willingness to travel 30–40% between Indianapolis and San Diego to maintain close stakeholder and team relationships