Data Scientist

Data Scientist 1

The Data Scientist 1 supports Cancer Center investigators by applying advanced analytical methods to biological and clinical research datasets. Primary responsibilities include analyzing bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data; developing and maintaining reproducible workflows; performing quality control, and visualization; and communicating results through written reports and PowerPoint presentations. The individual will work closely with principal investigators (PIs) and research teams to help answer biological questions by applying computing and data science tools to large data sets, and iterate efficiently to deliver reproducible, publication-quality results.

• Analyze bulk RNA-seq datasets for Cancer Center PI projects, including quality control, normalization, differential expression, pathway/enrichment analyses, and biological interpretation.
• Analyze single-cell RNA-seq datasets, including QC, normalization, integration/batch correction when appropriate, clustering, cell type/state annotation, differential expression/abundance analyses, and figure generation.
• Analyze spatial transcriptomics datasets, including QC, spatial feature detection, region-based analyses, spatially varying gene identification, cell type mapping/deconvolution (as applicable), and spatial interaction/neighborhood analyses.
• Work collaboratively with Cancer Center PIs and research teams to refine scientific questions, define analysis plans, and iterate on methods and results until the biological objectives are addressed.
• Communicate analytical results clearly to technical and non-technical audiences through oral presentations, collaborative working sessions, and well-structured written reports. Contribute written sections suitable for manuscripts, grants, and presentations.
• Develop, maintain, and document reproducible analysis workflows using R and Python; apply version control and best practices for reproducibility and transparency.
• Use established bioinformatics software and tool chains to support RNA-seq, single-cell, and spatial analyses; select and justify methods appropriate to each study design and dataset.
• Operate effectively in high-performance computing (HPC) environments, including Linux command-line workflows, secure data transfer, job scheduling, and resource-aware computation for large datasets.
• Maintain data integrity and provenance by validating inputs/metadata, documenting assumptions and parameters, and ensuring repeatable analytic outputs.
• Contribute to team knowledge-sharing through SOPs, documentation, and internal training on analysis methods and computational workflows, as needed.

• Bachelor’s degree in Biology, Computer Science, Bioinformatics, Biostatistics, Data Science, or a closely related field.
• Relevant experience performing data analysis and/or bioinformatics workflows.
• Working proficiency in R and Python for data analysis, visualization, and workflow development.
• Ability to work in Linux and HPC environments (command line, remote systems, batch jobs) and to manage large datasets efficiently.
• Strong written and verbal communication skills, including the ability to explain analytical results and limitations to biomedical researchers and to produce clear, actionable reports.
• Demonstrated ability to work collaboratively with investigators and project teams, manage priorities across multiple projects, and meet deadlines.

• Strong analytical reasoning, statistical thinking, and structured problem-solving for complex, ambiguous research questions.
• Ability to design and implement integrated analytical solutions; sound judgment in method selection and interpretation.
• Ability to create clear, publication-quality visualizations and to communicate results and caveats effectively.
• Familiarity with common bioinformatics data formats and software ecosystems used for RNA-seq, single-cell, and spatial analyses.
• Proficiency in programming and scripting; ability to write maintainable, well-documented code and to troubleshoot effectively.
• Ability to work effectively with diverse stakeholders and maintain productive, consultative relationships with PIs and research staff.
• Organizational skills to manage multiple concurrent projects and maintain reproducible records of analyses, parameters, and deliverables.

The University of New Mexico Comprehensive Cancer Center is the Official Cancer Center of New Mexico and the only National Cancer Institute-designated Cancer Center in a 500-mile radius. Its 136 board-certified oncology specialty physicians include cancer surgeons in every specialty (abdominal, thoracic, bone and soft tissue, neurosurgery, genitourinary, gynecology, and head and neck cancers), adult and pediatric hematologists/medical oncologists, gynecologic oncologists, and radiation oncologists.

They, along with more than 600 other cancer healthcare professionals (nurses, pharmacists, nutritionists,…