Director, Software Product Management - Discovery Research Platforms

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 around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism.

We give our best effort to our work, and we put people first. We're looking for people who are determined to make life better for people around the world.

** Director, Software Product Management - Discovery Research Platforms*
* ** Software Product Engineering Overview*
* Software Product Engineering builds and maintains capabilities using pioneering technologies like most prominent tech companies. What differentiates Software Product Engineering is that we create new possibilities through tech to advance Lilly's purpose - creating medicines that make life better for people around the world, like model-driven drug discovery and connected clinical trials. We hire the best technology professionals from a variety of backgrounds, so they can bring an assortment of knowledge, skills, and diverse thinking to deliver innovative solutions in every area of our business.

** 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.

A key dimension of this role is helping to define how AI agents and intelligent automation can transform discovery research - from accelerating design-make-test-learn cycles in biologics optimization to enabling scientists to compose and execute complex computational pipelines through intuitive, AI-assisted interfaces.

You will partner closely with computational biologists, protein engineers, and engineering teams to translate these possibilities into production-ready capabilities. This role seeks both domain fluency in medicine discovery and the product skills to contribute to a multi-year roadmap, influence investment decisions, and drive adoption across discovery research.

The successful candidate will operate at the intersection of science, engineering, and business - translating complex research needs into scalable software platforms that accelerate Lilly's biologics pipeline. While the initial focus is on computational drug design and optimization for large molecules, there are natural opportunities to broaden impact across adjacent lab and research platform areas over time.

The strong preference is for the role to be based in Indianapolis, though may consider San Diego as well, with periodic travel (approximately 30%) between sites to maintain close partnerships with research stakeholders and engineering teams in both locations.

** 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…