Job Description
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. Position Summary The Human Genomics and Translational Data Sciences team within Cardiometabolic Research Data Science is hiring a Bioinformatics Pipeline Engineer to help build, solidify, and scale the analytical pipelines our scientists rely on every day. Our work spans multiple omics workflows, including target discovery and target due diligence, single cell sequencing, genomics, proteomics and, increasingly, AI-assisted workflows that pull these analyses together into faster, more reproducible products for therapeutic area partners across Lilly Research Labs. This role sits at the intersection of two worlds. On one side, we employ classical bioinformatics and statistical genetics pipelines — the kind of robust, reproducible, well-tested workflows that turn messy public and proprietary genomics data into trustworthy answers. On the other, the rapidly evolving stack of AI tooling — large language models like Claude, agentic workflows, building AI-friendly connectors like MCP (Model Context Protocol), and the code that lets scientists query complex datasets in natural language. We want someone who is genuinely curious about both, and keen to use both to improve the value we derive from our datasets to enable target support and novel target discovery. You will not be expected to be a senior expert in either domain on day one. You will be expected to bring strong software engineering instincts, and a keen curiosity and creativity to enhance the value of the tools and datasets at our disposal. You will work closely with statistical geneticists, computational biologists, and other engineers — both within our team and across Lilly — to ship tools that make the science faster and more reliable. Key Responsibilities Pipeline Development and Engineering Support for computational biology workflows, including single cell, spatial, and other multi-omics analysis workflows for clinical and preclinical applications Use modern workflow managers (e.g. Nextflow, Snakemake, or similar) and containerization (Docker, Singularity) to make pipelines portable, testable, and reusable across projects and teams Help build and maintain reproducible analytical pipelines for statistical genetics and bioinformatics workflows Wrap and harden ad-hoc analytical scripts written by scientists into production-quality tools that can be re-run reliably by others Write tests, documentation, and clear examples so the pipelines you build are usable by colleagues with a range of technical backgrounds AI-Enabled Tooling and Workflows Prototype agentic workflows that automate established and routine analytical tasks — for example, pulling target evidence across data sources, generating standardized due-diligence reports, or letting scientists interrogate complex datasets in natural language Build and maintain MCP connectors that expose internal data, public resources, and analytical pipelines to LLM-based agents and tools like Claude Identify and develop use cases where LLMs and agentic AI workflows can improve the speed, quality, consistency, or accessibility of work across therapeutic areas, focusing on end-to-end capabilities rather than isolated task completion Contribute to a shared library of reusable AI tooling, prompt patterns, and integration code that the team can build on. Define technical standards for evaluation, documentation, guardrails, and workflow quality so that AI-based solutions are trusted, reproducible, and suitable for repeated use across teams and projects Know the latest with the AI tooling landscape and bring back ideas the team can put to work. Help improve AI fluency among collaborators by demonstrating practical workflows Collaboration Across Lilly Research Labs Partner closely with statistical geneticists, computational biologists, and software engineers within the Cardiometabolic Data Science group and across other Lilly Research Labs teams Work with therapeutic area partners to understand their analytical needs and translate them into pipeline requirements Coordinate with platform and engineering groups to ensure your pipelines integrate cleanly with broader Lilly infrastructure Contribute to inte