Director, Software Product Management – Discovery Research Platforms

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN +1

Product Management leader to shape strategy and development of custom software platforms for discovery research, focusing on computational drug design and optimization for large molecules. The role involves integrating agentic AI capabilities into research workflows, accelerating design-make-test-learn cycles, and enabling scientists to compose complex computational pipelines through AI-assisted interfaces. Requires partnership with computational biologists, protein engineers, and engineering teams to translate research needs into scalable software platforms.

What you'd actually do

  1. 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
  2. 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
  3. 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:
  4. 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
  5. 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

Skills

Required

  • Product strategy
  • Roadmap development
  • Cross-functional collaboration
  • Translating scientific requirements into product specifications
  • Understanding of computational drug design
  • Understanding of large molecule optimization
  • Experience with AI/ML integration in scientific research
  • Experience with agentic AI systems
  • Experience with workflow orchestration
  • Experience with Next Generation Sequencing (NGS) data
  • Experience with protein structure prediction algorithms
  • Experience with molecular dynamics simulation
  • Experience with high-throughput screening data analysis
  • Experience with dose-response modeling
  • Experience with vector database architectures for biological sequence similarity search
  • Experience with heterogeneous compute environments (cloud, GPU clusters)

Nice to have

  • Domain fluency in medicine discovery
  • Experience with generative models for antibody sequence design
  • Experience with active learning strategies
  • Experience with multi-objective optimization

What the JD emphasized

  • agentic AI capabilities
  • agentic AI capabilities
  • AI agents
  • AI-assisted interfaces
  • agentic capabilities
  • AI agents

Other signals

  • AI agents
  • intelligent automation
  • computational drug design
  • multi-objective optimization
  • discovery research platforms