Associate Principal Scientist - Immunology Spatial Omics Analytics

Job Description

The Data, AI, and Genome Sciences department is seeking an experienced and talented computational biologist with expertise in computational analyses of spatial multi-omics data to join our Translational Genome Analytics research team based in Cambridge, MA. We are looking for a data scientist who will leverage advanced statistical inference and AI/ML approaches to integrate patient-derived spatially resolved multi-omics data to decode cellular and molecular mechanisms underlying complex immunological diseases to inform novel target identification and mechanisms of drug action.  

In this exciting role, you will:

• Conduct comprehensive computational analyses of spatial multi-omics data generated from patient-derived tissues to decode cell-type specific molecular networks and molecular crosstalk between cells underpinning immunological diseases such as Inflammatory Bowel Disease, Rheumatoid Arthritis, Systemic Sclerosis, etc.

• Utilize advanced statistical methods and AI/ML techniques to interpret spatially-resolved gene and protein expression patterns, cellular interactions, and tissue architecture as measured by spatial biology platforms such as Xenium, Visium, CosMx, MERFISH, etc.

• Identify and establish cutting edge computational frameworks for spatial multi-omics integrative analyses, including spatially variable gene detection, decoding of cellular and transcriptomic niches and cell-cell interactions (e.g. GraphSAGE, SPARK-X, Spatial DE2, MERINGUE, Spacia, etc)

• Employ statistical modeling approaches to integrate spatial multi-omics data with other omics data layers (e.g. scRNAseq, proteomics, metabolomics, etc) to rigorously decipher cellular functions and to link disease relevant molecular alterations to specific cell types.

• Create visualizations and reports that effectively communicate analytical results with computational and experimental scientists while ensuring statistical rigor, data quality and consistency.

• Collaborate with cross-disciplinary teams, including biologists, clinicians, and other scientists, to address complex biological questions and optimize experimental design

• Mentor and provide guidance to other data scientists on best practices in data analysis, pipeline development, and scientific communication.

• Contribute to the strategic direction of research initiatives by identifying opportunities for funding, partnerships, and innovative methodologies.

• Publish research findings in high-impact peer-reviewed journals, present at scientific conferences, and communicate results to both technical and non-technical audiences.

Required Experience and Skills:

• Ph.D. in Bioinformatics, Computational Biology, Molecular Biology, or a related field with a strong focus on spatial transcriptomics and multi-omics data integration and analysis.

• A minimum of 4 years of postdoctoral or industry research experience in bioinformatics, particularly in leveraging spatial transcriptomics and multi-omics analysis.

• Hands-on experience with computational methods for integrating spatial and multi-omics datasets generated using spatial biology technologies (e.g., Visium, Xenium, CosMx, MERFISH, etc).

• Proven track record of applying machine learning algorithms and advanced computational methods to analyze spatial transcriptomics data, single-cell RNA sequencing (scRNAseq) data, leading to the identification of novel patterns, cell populations, and functional insights

• Extensive experience in bioinformatics tools and programming languages (e.g., R, Python) for data analysis, visualization, and pipeline development

• Proficiency in statistical analysis and machine learning techniques for biological data interpretation.

• Excellent analytical and problem-solving capabilities, with meticulous attention to detail.

• Knowledge of databases and data management systems relevant to biological research.

• Familiarity with cloud-based platforms for scalable data processing and analysis.

• Strong communication skills, with a proven ability to convey complex scientific concepts to diverse audiences.

Preferred Experience and Skills:

• Expertise in utilizing network-based analysis frameworks to infer gene regulatory patterns and signaling pathways from next-generation sequencing (NGS) data, enabling the identification of potential therapeutic targets and conducting mechanism of action studies

• Familiarity with high-throughput siRNA/CRISPR/chemical screens in translationally-relevant preclinical models of immunological diseases

• Familiarity with animal models of im