Scientific Lead, Molecular Characterization

Responsibilities

• Design and lead the development of spatial transcriptomics and multi-modal spatial workflows, encompassing tissue optimization, library construction, and end-to-end data generation using platforms such as Visium HD, CosMx, and Xenium.
• Drive the integration of spatial transcriptomics with complementary modalities, including spatial proteomics (e.g., CosMx protein panels, CODEX/PhenoCycler) and single-cell data, to generate comprehensive tissue-level molecular maps.
• Establish and continuously improve tissue processing standards for diverse sample types relevant to Oncology (FFPE, fresh-frozen, bone marrow, cryosections), with a focus on maximizing data quality from challenging or low-input specimens.
• Develop image analysis pipelines in collaboration with discovery informatics, including tissue segmentation, cell type deconvolution, and morphological co-registration using tools such as QuPath, HALO, or equivalent platforms.
• Evaluate emerging spatial technologies on an ongoing basis and translate promising platforms into internal capabilities through systematic feasibility assessment and implementation planning.
• Scale long-read sequencing workflows (PacBio and Oxford Nanopore) for applications including structural variant detection, isoform characterization, epigenetic sequencing (e.g., methylation, Fiber-seq), and custom targeted approaches.
• Contribute to automation of NGS and spatial library preparation protocols in collaboration with automation and histology specialists.
• Develop custom targeted panels and probe/index designs for the spatial platforms to address specific genomic and transcriptomic questions posed by Oncology project teams.
• Establish protocol QC frameworks and performance benchmarks to ensure data integrity across all high-throughput molecular platforms.
• Apply and adapt spatial data analysis tools (e.g., Seurat, Squidpy, Scanpy) to process, visualize, and interpret spatial transcriptomics datasets in close partnership with the discovery informatics team.
• Work with bioinformaticians to design and evaluate computational workflows for long-read data, including isoform quantification, structural variant calling, and base modification detection.
• Serve as the internal scientific authority on spatial and long-read sequencing platforms; advise Oncol

Benefits

Additional Information

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. 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 Scientific Lead position would be the principal architect for spatial biology and long-read genomics within the Molecular Characterization team in Discovery Technologies, responsible for developing, optimizing, and scaling these platforms in support of Oncology drug discovery at Lilly. The Molecular Characterization team sits at the intersection of genomics, proteomics, and emerging molecular technologies, providing cutting-edge platform capabilities across Lilly's discovery portfolio. The central mandate is invention: defining what next-generation spatial and long-read platforms can do, and building the infrastructure to realize their full potential within the group. The successful candidate will possess deep, applied expertise in spatial transcriptomics (e.g., Visium HD, CosMx, Xenium), with working knowledge of long-read sequencing (PacBio/Nanopore) and demonstrated experience in the automation of complex NGS workflows. Of equal importance is an entrepreneurial scientific mindset, a genuine drive to evaluate and deploy emerging tools that have not yet been established as standard practice within the field. This is a hands-on role where direct experimentation and platform development are central to scientific impact, with growing opportunities to shape scientific direction and mentor junior team members as the platforms mature. This position requires close collaboration with Oncology project teams, automation specialists, histology, and discovery informatics to translate novel molecular insights into actionable biology.

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