Scientific Lead, Molecular Characterization
Eli Lilly and Company
August 16, 2026
On-site
New York, NY
Clinical Research and Development
Position Summary
- Lead development, optimization, and scaling of spatial biology and long-read genomics platforms within Molecular Characterization to support Oncology drug discovery.
Key Responsibilities
- Design and lead spatial transcriptomics and multi-modal spatial workflows (tissue optimization, library construction, end-to-end data generation) using Visium HD, CosMx, and Xenium.
- Integrate spatial transcriptomics with spatial proteomics (e.g., CosMx, CODEX/PhenoCycler) and single-cell data to generate tissue-level molecular maps.
- Improve tissue processing standards for FFPE, fresh-frozen, bone marrow, cryosections; maximize data quality from challenging/low-input samples.
- Build image analysis pipelines (segmentation, deconvolution, co-registration) using QuPath/HALO.
- Evaluate emerging spatial technologies; translate promising tools into internal capabilities.
- Scale long-read sequencing (PacBio/Oxford Nanopore) for SV detection, isoform characterization, epigenetic sequencing (e.g., methylation, Fiber-seq), and targeted approaches.
- Contribute to automation of NGS/spatial library prep; develop targeted panel/probe/index designs.
- Establish QC frameworks and benchmarks; apply spatial analysis tools (Seurat, Squidpy, Scanpy).
- Partner with bioinformaticians on long-read computational workflows (isoforms, SV calling, base modifications).
- Serve as internal scientific authority; advise Oncology teams; mentor junior scientists; publish/present; partner with academia/vendors/CROs.
Required Qualifications
- PhD in molecular biology/genomics/genetics or related field; 3+ years hands-on research or platform development.
Preferred Qualifications
- Deep spatial transcriptomics expertise (Visium HD/CosMx/Xenium), tissue handling; long-read sequencing hands-on; NGS/spatial automation via liquid handling; Python/R; multimodal spatial; long-read epigenetics; targeted panel development; multi-omics tools; large dataset processing; regulated/GLP experience.
Benefits (if eligible)
- Bonus potential; 401(k), pension; vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits (e.g., EAP, fitness).
- Lead development, optimization, and scaling of spatial biology and long-read genomics platforms within Molecular Characterization to support Oncology drug discovery.
Key Responsibilities
- Design and lead spatial transcriptomics and multi-modal spatial workflows (tissue optimization, library construction, end-to-end data generation) using Visium HD, CosMx, and Xenium.
- Integrate spatial transcriptomics with spatial proteomics (e.g., CosMx, CODEX/PhenoCycler) and single-cell data to generate tissue-level molecular maps.
- Improve tissue processing standards for FFPE, fresh-frozen, bone marrow, cryosections; maximize data quality from challenging/low-input samples.
- Build image analysis pipelines (segmentation, deconvolution, co-registration) using QuPath/HALO.
- Evaluate emerging spatial technologies; translate promising tools into internal capabilities.
- Scale long-read sequencing (PacBio/Oxford Nanopore) for SV detection, isoform characterization, epigenetic sequencing (e.g., methylation, Fiber-seq), and targeted approaches.
- Contribute to automation of NGS/spatial library prep; develop targeted panel/probe/index designs.
- Establish QC frameworks and benchmarks; apply spatial analysis tools (Seurat, Squidpy, Scanpy).
- Partner with bioinformaticians on long-read computational workflows (isoforms, SV calling, base modifications).
- Serve as internal scientific authority; advise Oncology teams; mentor junior scientists; publish/present; partner with academia/vendors/CROs.
Required Qualifications
- PhD in molecular biology/genomics/genetics or related field; 3+ years hands-on research or platform development.
Preferred Qualifications
- Deep spatial transcriptomics expertise (Visium HD/CosMx/Xenium), tissue handling; long-read sequencing hands-on; NGS/spatial automation via liquid handling; Python/R; multimodal spatial; long-read epigenetics; targeted panel development; multi-omics tools; large dataset processing; regulated/GLP experience.
Benefits (if eligible)
- Bonus potential; 401(k), pension; vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits (e.g., EAP, fitness).