Bioinformatician 2 – Computational Spatial Biology

Responsibilities

• Prioritize and extract data from a variety of sources such as notes, survey results, medical reports, and laboratory data, and maintain its accuracy and completeness.
• Determine additional data collection and reporting requirements.
• Design and customize reports based upon data in the database.
• Oversee and monitor regulatory compliance for utilization of the data.
• Use system reports and analyses to identify potentially problematic data, make corrections, and eliminate root cause for data problems or justify solutions to be implemented by others.
• Create complex charts and databases, perform statistical analyses, and develop graphs and tables for publication and presentation.
• Serve as a resource for non-routine inquiries such as requests for statistics or surveys.
• Test prototype software and participate in approval and release process for new software.
• Provide documentation based on audit and reporting criteria to investigators and research staff.

Requirements

• MS or PhD in Computational Biology, Bioinformatics, Statistics, Computer Science, Biomedical Data Science, or a related quantitative field, with 1 to 2 years of relevant research experience.
• Experience analyzing and interpreting genomic, spatial omics, and proteomic datasets, including data generated from platforms such as Xenium, Visium, CosMx, CODEX, and single-cell RNA-seq, with demonstrated proficiency in single-cell RNA-seq analysis workflows.
• Prior experience analyzing the tumor microenvironment is highly desirable, along with familiarity with resources and databases related to cancer hallmarks, ligand–receptor interactions, and drug response.
• Strong interest in cancer biology and in deriving biologically meaningful insights from computational analyses; experience contributing to peer-reviewed publications is desirable.
• Proficiency in Python and R, including experience with commonly used analysis frameworks such as Seurat, scverse, and Bioconductor.
• Strong foundation in statistics, data analysis, and computational methods; familiarity with machine learning and algorithm development is desirable.
• Experience working in Unix/Linux computing environments, including use of high-performance computing clusters and, ideally, GPU-enabled workflows.
• Experience with GitHub, version control, and repository management for collaborative and reproducible research.
• Ability to work independently under general guidance, manage multiple priorities, and contribute effectively in a collaborative multidisciplinary research environment.
• Excellent oral and written communication skills, with a strong interest in connecting computational analysis to meaningful biological and translational questions.

Qualifications

• Bachelor's degree and three years of relevant experience or combination of education and relevant experience.
• Experience in a quantitative discipline such as economics, finance, statistics or engineering.
• Substantial experience with MS Office and analytical programs.
• Excellent writing and analytical skills.
• Ability to prioritize workload.