Bioinformatic analyst (spatial transcriptomics)

Company DescriptionCincinnati Children’s is a leading nonprofit academic medical center affiliated with the University of Cincinnati College of Medicine. Established in 1883, Cincinnati Children’s is internationally recognized for excellence in pediatric care, research, education, and innovation. The institution provides comprehensive clinical care ranging from general pediatrics to advanced treatment for complex pediatric diseases, and it trains more than 600 residents and clinical fellows each year.Cincinnati Children’s is committed to fostering an inclusive, respectful, and collaborative workplace. The institution is an Equal Opportunity Employer and values diversity among its staff, trainees, patients, families, and community.Role DescriptionThe Wang Lab at Cincinnati Children’s is seeking a motivated Bioinformatic Analyst to support computational and translational research in single-cell genomics, spatial transcriptomics, and disease biology.Our lab develops and applies computational methods to understand how cell–cell communication, tissue organization, and gene regulatory programs contribute to disease pathogenesis and progression. The Bioinformatic Analyst will work closely with Dr. Wang, lab members, and clinical and basic science collaborators to analyze high-dimensional genomics datasets, including single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, imaging-linked spatial omics data, and associated clinical and pathology annotations.The ideal candidate will have strong programming skills, experience with genomics data analysis, and an interest in applying computational approaches to biologically and clinically meaningful questions.Key ResponsibilitiesThe Bioinformatic Analyst will be responsible for:Analyzing single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and related omics datasets.Performing quality control, normalization, clustering, cell type annotation, differential expression analysis, pathway analysis, and data visualization.Supporting spatial transcriptomics analyses, including spatial domain identification, cell–cell communication analysis, tissue niche characterization, and integration with histology images.Developing, maintaining, and documenting reproducible analysis pipelines in R and/or Python.Organizing, curating, and managing large-scale genomics datasets and associated metadata.Generating publication-quality figures, summary reports, and analysis outputs for manuscripts, grants, and presentations.Working with lab members and collaborators to interpret computational results in biological and clinical context.Assisting with benchmarking, applying, and refining computational tools developed in the lab.Maintaining clear documentation of code, workflows, parameters, and analysis decisions.Participating in lab meetings, project discussions, and collaborative research planning.Required QualificationsBachelor’s or Master’s degree in bioinformatics, computational biology, biostatistics, computer science, data science, genetics/genomics, biomedical informatics, or a related field.Proficiency in R and/or Python.Experience analyzing high-throughput sequencing data, such as RNA-seq, single-cell RNA-seq, or spatial transcriptomics.Familiarity with commonly used bioinformatics tools and packages, such as Seurat, Scanpy, Squidpy, or similar frameworks.Ability to work with large datasets in a Linux or high-performance computing environment.Strong data visualization skills.Strong organizational skills and attention to reproducibility.Ability to communicate computational results clearly to both computational and biological audiences.Preferred QualificationsExperience with spatial transcriptomics platforms such as 10x Visium, CosMx, MERSCOPE, Xenium, Slide-seq, or related technologies.Experience with cell–cell communication analysis, gene regulatory network analysis, pathway enrichment, or multi-omics integration.Experience working with histology-linked spatial omics data or digital pathology images.Familiarity with machine learning or statistical modeling.Experience using Git/GitHub for version control.Experience with workflow management tools such as Snakemake, Nextflow, or CWL.Experience with containerized environments such as Docker or Singularity/Apptainer.Prior work in immunology, liver disease, fibrosis, developmental biology, inflammatory disease biology, or related areas.Prior experience contributing to manuscripts, grants, or collaborative research projects.