Bioinformaticist - Research Scientist

Department: Ophthalmology

Job Title: 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.

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.
• Experience using containers such as Docker, Singularity, or Apptainer.
• Experience with HPC 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.

Knowledge, Skills and Abilities:

• 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 the limitations of each.
• 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 while contributing effectively within multidisciplinary teams.
• Strong organizational, project-management, and time-management skills.
• Ability to balance multiple 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.

How to Apply:

For full consideration, please submit the following document(s):

1. A letter of interest describing relevant job experiences as they relate to listed job qualifications and interest in the position

2. Curriculum vitae / Resume

3. Five professional references including name, address, phone number (mobile number if appropriate), and email addre

Applications are accepted electronically ONLY at www.cu.edu/cu-careers.

Questions should be directed to: Ashley Woodhouse Ashley.woodhouse@cuanschutz.edu

Screening of Applications Begins:

Applications will be accepted until finalists are identified, but preference will be given to complete applications received by November 13, 2026. Those who do not apply by this date may or may not be considered.

Anticipated Pay Range:

The starting salary range (or hiring range) for this position has been established as HIRING RANGE:

$90,000 - $105,000

The above salary range (or hiring range) represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting. This position is not eligible for overtime compensation unless it is non-exempt.

Your total compensation goes beyond the number on your paycheck. The University of Colorado provides generous leave, health plans and retirement contributions that add to your bottom line.

Total Compensation Calculator: http://www.cu.edu/node/153125