Bioinformaticist - Research Scientist

University of Colorado•Aurora, CO

10d•$90,000 - $105,000•Hybrid

About The Position

The Lead Bioinformatician will provide scientific and technical analysis for a translational biorepository focused on identifying biomarkers of disease subtype and treatment response. The role leads development and implementation of reproducible analytical workflows that integrate high-dimensional immunologic, proteomic, genomic, and clinical datasets. Initial work will focus on the biorepository’s multi-omic data, including PhIP-Seq, high-dimensional cytometry, plasma proteomics, clinical phenotypes, and whole-genome sequencing results analyzed by collaborating analysts. The position will help define the analytical scope and long-term data strategy, establish reusable data and computational infrastructure, and support the biorepository’s growth. The position also supports collaborative projects involving single-cell RNA sequencing, spatial transcriptomics, and other emerging multi-omic technologies. The successful candidate works with clinical investigators, laboratory scientists, statisticians, geneticists, and computational collaborators to define research questions, develop analysis plans, interpret findings, and communicate results. The role provides technical leadership, code review, training, and mentorship to approximately one to three analysts or trainees. Approximately 90% of initial work will support the biorepository, with the portfolio expected to broaden over the first two to three years. The position is eligible for a hybrid schedule, with three days per week on campus and in-person attendance as needed for meetings. The hiring range is $90,000–$105,000.

Requirements

Master’s degree in bioinformatics, computational biology, biostatistics, or data science.
Four years of professional or research experience conducting bioinformatic or computational analyses, including substantial independent responsibility for study analysis and interpretation.
Demonstrated experience with complex analyses involving multiple biological data modalities.
Proficiency in multiple coding languages, including R and Python.
Experience developing reproducible bioinformatic workflows and using version control.
Experience performing quality control, statistical analysis, visualization, and biological interpretation of high-dimensional biomedical data.
Experience collaborating with computational and non-computational investigators.
Ability to independently organize work, manage timelines, and contribute to multiple concurrent research projects.
Advanced knowledge of statistical and computational principles relevant to multi-omic biomedical research.
Ability to independently evaluate complex research questions and recommend rigorous analytical approaches.
Ability to distinguish exploratory, confirmatory, and predictive analyses and communicate their limitations.
Strong programming, data-management, troubleshooting, and scientific-visualization skills.
Commitment to reproducibility, transparency, documentation, and code quality.
Ability to translate complex analytical results for clinicians, laboratory scientists, trainees, and other stakeholders.
Ability to work independently and contribute effectively within multidisciplinary teams.
Strong organizational, project-management, and time-management skills; ability to balance projects, identify dependencies, communicate delays or risks, and establish realistic timelines.
Ability to provide constructive technical feedback and mentorship without formal supervisory authority.
Scientific curiosity and willingness to evaluate new analytical approaches as technologies and research questions evolve.

Nice To Haves

Five or more years of relevant bioinformatics or computational research experience.
Experience leading collaborative projects.
Demonstrated experience integrating three or more omics or high-dimensional data types.
Experience analyzing single-cell RNA sequencing, spatial transcriptomics, affinity-based proteomics, mass-spectrometry proteomics, high-dimensional cytometry, PhIP-Seq or other serologic assays, germline genomic data, or longitudinal clinical or treatment-response data.
Experience with CCA, MOFA or related latent-factor methods, WGCNA, pseudobulk analysis, cell–cell communication analysis, and predictive modeling.
Experience with Nextflow, Snakemake, or comparable workflow-management systems.
Experience using containers such as Docker, Singularity, or Apptainer.
Experience with high-performance computing and/or cloud-computing environments.
Experience designing metadata standards, harmonizing data across studies, or developing reusable research data infrastructure.
Publication record demonstrating substantive contributions to biomedical data analysis.
Experience contributing analytical sections to grant applications.
Experience training or mentoring analysts, students, or research staff.
Experience in immunology, translational research, autoimmune or inflammatory disease, ophthalmology, or biomarker discovery.

Responsibilities

Lead analysis and integration of PhIP-Seq, CYTOF, affinity-based and mass-spectrometry proteomics, clinical data, and externally analyzed whole-genome sequencing results.
Develop analysis plans addressing biomarkers of disease subtype, prognosis, and treatment response.
Apply and evaluate multi-omic methods, including CCA, MOFA and related latent-factor models, WGCNA, pseudobulk analysis, cell-state and cell–cell communication analysis, and predictive modeling.
Lead secondary projects involving single-cell RNA sequencing and spatial transcriptomics.
Perform raw-data processing, quality assessment, normalization, statistical analysis, biological interpretation, and visualization.
Work with investigators to refine research questions, assess feasibility, select methods, estimate timelines, and communicate analytical limitations.
Prepare publication-quality figures and contribute to manuscripts, abstracts, presentations, and grant applications; serve as a coauthor on collaborative work and potentially lead analytically focused manuscripts.
Design reusable analytical and data infrastructure to support expansion of the biorepository.
Develop, validate, document, and maintain reproducible computational pipelines using workflow-management systems.
Work in high-performance computing and cloud-computing environments.
Develop and maintain data dictionaries, metadata standards, sample identifiers, provenance records, analysis documentation, and quality-control procedures.
Harmonize clinical, laboratory, and multi-omic datasets across projects and collection periods.
Establish standards for code quality, versioning, documentation, archiving, and reproducible reporting.
Provide technical guidance, code review, training, and mentorship to one to three junior analysts, trainees, or research staff.
Teach best practices in bioinformatics, statistical analysis, reproducible research, data management, and scientific visualization.
Promote consistent analytical standards across projects.
Participate in project meetings and communicate findings to computational and non-computational collaborators.
Help prioritize analytical requests and support effective management of multiple concurrent projects.