Scientific Lead, Molecular Characterization

Lilly • New York, NY

3d • $138,000 - $224,400 • Onsite

About The Position

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.

Requirements

• PhD in molecular biology, genomics, genetics, or a closely related discipline, with 3+ years of hands-on research or platform development experience in an academic or industry setting.

Nice To Haves

• Deep hands-on expertise in spatial transcriptomics platforms (Visium HD, CosMx, Xenium, or equivalent), from tissue section preparation through library construction and QC.
• Demonstrated experience with tissue optimization and sample handling for spatial applications across diverse and challenging sample types (FFPE, fresh-frozen, bone marrow, cryosections).
• Familiarity with long-read sequencing platforms (Oxford Nanopore and/or PacBio); hands-on experience with library construction, QC, and data interpretation is a strong plus.
• Proven experience automating NGS or spatial library preparation workflows using liquid handling platforms (e.g., Hamilton, Beckman Coulter, or equivalent).
• Working knowledge of spatial data analysis tools (e.g., Seurat, Squidpy, Scanpy) and image analysis platforms (e.g., QuPath, HALO) for tissue-based data.
• Proficiency scripting in Python and/or R to apply, adapt, and troubleshoot single-cell and spatial analysis tools (e.g., Scanpy/Squidpy, Seurat).
• Demonstrated track record of building or deploying new molecular platforms or technologies, not solely operating established protocols.
• Broad NGS experience including RNA-seq, WES, single-cell sequencing, and epigenetic profiling (ATAC-seq, bisulfite sequencing, or equivalent).
• Excellent scientific communication skills; ability to convey complex results clearly to technical and non-technical stakeholders.
• Demonstrated ability to work independently and as part of a cross-functional team in a fast-paced environment with evolving priorities.
• Experience with multimodal spatial platforms integrating transcriptomics and proteomics (e.g., CosMx protein, CODEX/PhenoCycler).
• Familiarity with long-read epigenetic methods such as Fiber-seq or direct methylation detection via Nanopore.
• Experience developing novel targeted sequencing panels, including probe design and index optimization.
• Knowledge of multi-omics platforms such as Nanostring, Quanterix, Luminex, or Fluidigm.
• Experience processing and interpreting large-scale biological datasets; familiarity with Spotfire or similar data visualization tools.
• Experience in GLP environments or regulated laboratory settings.

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 Oncology project teams on platform selection, experimental design, and interpretation.
• Provide mentorship and hands-on coaching to junior scientists; build a team culture grounded in technical rigor, creative problem-solving, and collaborative execution.
• Establish and manage relationships with academic collaborators, technology vendors, and contract research organizations to stay at the leading edge of platform development.
• Prepare and deliver scientific presentations, publications, and study reports to internal and external audiences.
• Maintain up-to-date knowledge of the scientific landscape in spatial biology, long-read genomics, and multi-omics; proactively share emerging opportunities with the broader team.

Benefits

• company bonus (depending, in part, on company and individual performance)
• company-sponsored 401(k)
• pension
• vacation benefits
• medical, dental, and vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree