"Bioinformatics Analyst I - Spatial Multi-Omics and Integrative Analysis"

Overview

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington.

With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world's leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world's deadliest diseases and make life beyond cancer a reality.

At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems.

The Bioinformatics Analyst I will provide advanced analytical support for translational and mechanistic studies focused on cancer initiation, progression, and the tumor microenvironment. The position centers on the analysis and integration of spatial multi-omics datasets, particularly those generated using 10x Genomics Xenium in situ transcriptomics and Akoya PhenoCycler-Fusion multiplex proteomics platforms, as well as single cell/single nuclei RNA-seq.

The successful candidate will play a key role in developing and applying computational workflows to extract biological insight from large-scale, high-dimensional datasets at single-cell resolution. Working closely with research staff, postdoctoral fellows, and the principal investigators, the Analyst will help design analytical strategies, interpret findings in biological context, and generate data visualizations and figures for publications and grant applications.

This is an opportunity to contribute to a multidisciplinary research environment that integrates molecular biology, spatial transcriptomics, proteomics, and computational modeling to understand the role of tissue microenvironment in cancer initiation and progression.

Responsibilities

• Process, analyze, and interpret spatial transcriptomics and spatial proteomics data.
• Develop and implement computational workflows for image alignment, cell segmentation, gene/protein quantification, clustering, and spatial domain detection.
• Integrate multi-modal datasets (transcriptomic, proteomic, methylation, scRNA-seq) to identify molecular programs and cell-cell interactions driving cancer initiation and progression.
• Collaborate with lab members and collaborators on study design, data interpretation, and presentation of results.
• Generate high-quality visualizations and figures for internal reports, manuscripts, and grant submissions.
• Maintain documentation, reproducibility, and version control of analysis pipelines (GitHub, Jupyter/R Markdown, Snakemake/Nextflow).
• Participate in lab meetings, contribute to collaborative problem solving, and stay current with emerging computational methods in spatial and single-cell biology.

Qualifications

MINIMUM QUALIFICATIONS:

• Bachelor's degree or higher in Bioinformatics, Computational Biology, Biostatistics, Computer Science, or a related field.
• Minimum 2 years of hands-on experience analyzing high-throughput sequencing or multi-omics data. (Additional education considered in lieu of experience)
• Proficiency in R and/or Python, with experience using Seurat, Scanpy, Squidpy, or Giotto for spatial or single-cell analysis.
• Demonstrated expertise in Xenium, PhenoCycler-Fusion, or related spatial omics platforms.
• Strong foundation in cell and molecular biology, enabling biological interpretation of computational findings.
• Excellent communication and organizational skills, with the ability to manage multiple projects independently and collaboratively.

PREFERRED QUALIFICATIONS:

• Proficient with integrating Xenium and PhenoCycler-Fusion data.
• Proficient with integrating image-based data processing (e.g., QuPath, CellProfiler).
• Knowledge of machine learning or network analysis for spatial data integration.
• Experience contributing to publications, presentations, or grant applications.

The annual base salary range for this position is from $80,172 to $104,250, and pay offered will be based on experience and qualifications.