Scientific Lead, Molecular Characterization

Key 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.

Required Qualifications

• 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.
• 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.