Bioinformaticist - Research Scientist

Job Summary:

This Lead Bioinformatician will provide scientific and technical analysis for a growing translational biorepository focused on identifying biomarkers of disease subtype and treatment response. The position will lead the development and implementation of reproducible analytical workflows integrating high-dimensional immunologic, proteomic, genomic, and clinical datasets.

Initial work will be heavily focused on the biorepository’s multi-omic data, including PhIP-Seq, high-dimensional cytometry, plasma proteomics, clinical phenotypes, and integration with whole-genome sequencing results generated by collaborating analysts. This role will help shape the analytical scope and long-term data strategy of the biorepository, establish reusable data and computational infrastructure, and support its continued growth in size and scientific scope.

The position will also support additional collaborative projects involving single-cell RNA sequencing, spatial transcriptomics, and other emerging multi-omic technologies. The successful candidate will work closely with clinical investigators, laboratory scientists, statisticians, geneticists, and other computational collaborators to define research questions, develop analysis plans, interpret biological findings, and communicate results. The position will provide technical leadership, code review, training, and mentorship to approximately one to three analysts or trainees.

Key Responsibilities:

Multi-Omic Research and Analysis 65%

• Lead analysis and integration of PhIP-Seq, CYTOF, affinity-based and mass-spectrometry proteomics, clinical data, and externally analyzed WGS 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 appropriate 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, with opportunities to lead analytically focused manuscripts.

Data Infrastructure, Reproducibility, and Governance 25%

• Design a reusable analytical and data infrastructure that supports continued 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.

Technical Leadership, Mentoring, and Collaboration 10%

• 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 both computational and non-computational collaborators.

• Help prioritize analytical requests and support effective management of multiple concurrent projects.

Approximately 90% of the roles’s initial work will support the biorepository, with the portfolio expected to broaden over the first two to three years.

Work Location:

Hybrid – this role is eligible for a hybrid schedule of 3 days per week on campus and as needed for in-person meetings.

Why Join Us:

Our department’s home to the Sue Anschutz Rodgers Eye Center on the Anschutz Medical Campus. This state-of-the-art facility is one of the largest eye centers in the country and serves not only patients in the Rocky Mountain region but also patients all over the world.

The technological innovations conceived and developed by Departmental faculty have changed the practice of eye care throughout the world. Our educational programs train the next generation of leaders in ophthalmology. Our specialists have developed national and international reputations for excellence in routine and complex ophthalmic care. We have invested heavily in tracking our clinical outcomes and we are proud that our clinicians perform at the highest levels in their respective fields.

Why work for the University?

We have AMAZING benefits and offer exceptional amounts of holiday, vacation and sick leave! The University of Colorado offers an excellent benefits package including:

• Medical: Multiple plan options
• Dental: Multiple plan options
• Additional Insurance: Disability, Life, Vision
• Retirement 401(a) Plan: Employer contributes 10% of your gross pay
• Paid Time Off: Accruals over the year (based on percentage of time)
• Vacation Days: 22/year (maximum accrual 352 hours)
• Sick Days: 15/year (unlimited maximum accrual)
• Holiday Days: 11/year
• Tuition Benefit: Employees have access to this benefit on all CU campuses
• ECO Pass: Reduced rate RTD Bus and light rail service

There are many additional perks & programs with the CU Advantage.

Qualifications:

Minimum Qualifications:

Applicants must meet minimum qualifications at the time of hire.

• Masters 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.

• Demonstrated 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 both computational and non-computational investigators.

• Experience independently organizing work, managing timelines, and contributing to multiple concurrent research projects.

Preferred Qualifications:

• Masters degree in bioinformatics, computational biology, biostatistics, or data science.

• 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 the following:

  • single-cell RNA sequencing;

  • spatial transcriptomics;

  • affinity-based proteomics;

  • mass-spectrometry proteomics;

  • high-dimensional cytometry;

  • PhIP-Seq or other serologic assays;

  • germline genomic data;

  • 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.