Associate Principal Scientist, Biomarker Discovery, Multiomics Full-time

Experimental And Computational Biologist

We are seeking a highly skilled and experienced experimental and computational biologist with a strong background impacting Immunology Drug Discovery leveraging advanced multimodal and multi-omics technologies to join the Spatial and Single Cell Multiomics (SSM) Team within the Department of Data, AI & Genome Sciences (DAGS) at our Research Laboratories in Cambridge, MA.

Our team leverages cutting-edge Spatial Multiomics (CosMx WTx, Xenium, COMET mIF, Digital Pathology, MSI) and multi-omics single cell approaches (REAP-seq/CITE-seq, CROP-seq/Perturb-Seq, Immune profiling, Multiome ATAC-seq) to drive Biomarker Discovery and innovation across the drug development pipeline, from pre-clinical to clinical stages. SSM collaborates closely with therapeutic areas such as Oncology, Immunology, Neuroscience, and Cardiometabolic Disease as functional areas including Data, AI & Genome Sciences, Data Science & Scientific Informatics, Quantitative Bioscience, and ChemBio within the Discovery, Preclinical and Translational Medicine (DPTM) organization.

Responsibilities:

• Design and execute hands-on laboratory experiments for large-scale studies across single cell and spatial multiomics assays, including single cell RNA-seq, REAP-seq/CITE-seq, CROP-seq/Perturb-seq, immune profiling, Multiome ATAC-seq, and spatial multiomics platforms such as CosMx, Xenium, COMET mIF, and Digital Pathology.
• Support and develop perturbation, cell-culture, and tissue-based profiling studies to enable mechanistic studies and translationally relevant biological investigations.
• Collaborate closely with experimental biologists, automation engineers, computational scientists, data analysts, AIML data scientists, and therapeutic area subject matter experts to address biological questions, develop experimental strategies, and execute project plans.
• Independently drive projects from study design through execution, data interpretation, troubleshooting, and communication of results to stakeholders and senior leadership.
• Evaluate, implement, and scale emerging single cell and spatial technologies, computational approaches, and biological models for use in early discovery and translational research.
• Drive scientific impacts across technical workstreams, helping to close multiple experimental and computational capability gaps on the team.
• Work in a matrixed environment aligned by therapeutic area, with primary support for Immunology and Oncology programs, and collaboration across other DAGS and DPTM efforts as needed.
• Clearly communicate to executive leadership including goals, assay procedures, experimental findings, and biological insights through presentations, written summaries, and cross-functional discussions.

Education Minimum Requirement:

• Ph.D. in molecular and cell biology, bioengineering, cell biology, genetics/genomics, computational biology, biotechnology, or related STEM disciplines with 3-5+ years of industry experience.

Required Skills and Experiences:

• Demonstrated application of single cell and/or spatial multiomics to Immunology.
• Extensive hands-on expertise in molecular biology assays and high-throughput genomic technologies, particularly single cell RNA-seq, REAP-seq/CITE-seq, CROP-seq/Perturb-seq, and/or spatial transcriptomics.
• Demonstrated ability to work across both single-cell and spatial workflows, with enough breadth to contribute outside a narrow technical specialty.
• Experience with perturbation, cell and/or tissue based systems, and the ability to connect assay design to biological and translational questions.
• Proven experience with advanced profiling technologies such as 10x Genomics scRNAseq/STx, Bruker STx/mIF/MSI, Parse scRNAseq or related platforms, including demonstrated execution of these assays at scale.
• Proficiency in using automation tools for high throughput sample processing and data execution, such as Biomek, Hamilton, Leica Bond Rx, or equivalent systems.
• Experience using Linux/Unix OS and high-performance compute (HPC) environments. Expertise in R and/or Python.
• Demonstrated experience in computational analysis and biological interpretation of single cell RNA-seq, Spatial Tx, mIF and/or related multimodal datasets.
• Experience partnering with cross-functional groups including experimental biologists, data scientists, TA biologists and IT Engineering
• Strong understanding of experimental design, scientific rigor, troubleshooting, and documentation, with the ability to independently lead studies.
• Exceptional problem-solving abilities, critical thinking skills, and analytical expertise.
• Excellent communication skills, both written and verbal, with the ability to convey complex technical information to a wide range of audiences and excel working as “One Team” across departments and cross-functional teams.

Preferred Experience and Skills:

• Experience operating effectively in a matrixed team structure with alignment to therapeutic area priorities and collaboration across technical leads.
• Broad scientific versatility and learning agility, with the ability to expand into adjacent workflows or biological domains as project needs evolve.
• A strong publication history in reputable peer-reviewed journals.