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How much do single cell rna sequencing phd jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for single cell rna sequencing phd in Phoenix, AZ is $35.26, according to ZipRecruiter salary data. Most workers in this role earn between $28.89 and $40.10 per hour, depending on experience, location, and employer.

What is a single cell RNA sequencing PhD?

A Single Cell RNA Sequencing PhD is a doctoral degree focused on the study and application of single-cell RNA sequencing (scRNA-seq) technologies. This field involves analyzing the gene expression profiles of individual cells, allowing researchers to understand cellular heterogeneity and complex biological processes at a granular level. PhD students in this area typically conduct original research, develop computational methods, and advance our understanding of cell biology, disease mechanisms, and potential therapeutic targets. Graduates often pursue careers in academia, biotechnology, or pharmaceutical research.

What are the key skills and qualifications needed to thrive as a single cell RNA sequencing PhD, and why are they important?

To thrive as a Single Cell RNA Sequencing PhD, you need a strong background in molecular biology, bioinformatics, and genomics, typically supported by a PhD in a relevant field. Proficiency with single-cell sequencing platforms (e.g., 10x Genomics), next-generation sequencing (NGS) technologies, and computational analysis tools like R or Python is essential. Critical thinking, problem-solving, and effective communication are crucial soft skills for interpreting complex data and collaborating within multidisciplinary teams. These skills and qualifications are vital for designing robust experiments, analyzing high-dimensional data, and translating findings into impactful biological insights.

What are some common challenges faced by researchers in a single cell RNA sequencing PhD role, and how can they be addressed?

One of the main challenges in a Single Cell RNA Sequencing PhD role is managing and interpreting large, complex datasets generated from single-cell experiments. Researchers must be proficient in both wet-lab techniques and bioinformatics analysis, often requiring collaboration with computational biologists. Another challenge is ensuring sample quality and minimizing technical variability, which can significantly impact data reliability. Staying updated with rapidly evolving sequencing technologies and analytical tools is crucial, as is developing strong problem-solving skills to troubleshoot experimental or computational issues.

What is the difference between Single Cell Rna Sequencing Phd vs Single Cell Data Analyst?

AspectSingle Cell Rna Sequencing PhdSingle Cell Data Analyst
Required CredentialsPhD in Biology, Genetics, or related fieldBachelor's or Master's in Data Science, Biology, or related field
Work EnvironmentResearch labs, biotech companies, academic institutionsBiotech firms, research organizations, healthcare companies
Industry UsageDesigning experiments, interpreting sequencing data, publishing researchAnalyzing sequencing datasets, creating reports, data visualization

The Single Cell Rna Sequencing Phd typically involves designing experiments and interpreting complex sequencing data, requiring advanced research skills. In contrast, a Single Cell Data Analyst focuses on analyzing datasets, generating insights, and visualizing data, often with less emphasis on experimental design. Both roles are vital in the biotech industry but differ in their focus and required expertise.

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Single Cell Rna Sequencing Phd jobs near you
Infographic showing various Single Cell Rna Sequencing Phd job openings in Phoenix, AZ as of August 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 16% Part Time, and 4% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $73,342 per year, or $35.3 per hour.

Data Scientist, Department of Internal Medicine (Phoenix)

Phase2 Technology

Phoenix, AZ • On-site

$80 - $100/hr

Other

Medical, Dental, Vision, Life, PTO

Posted yesterday

New

Job description

Data Scientist, Department of Internal Medicine (Phoenix)Location

Greater Phoenix Area

Address

475 N. 5th Street, Phoenix, AZ 85004 USA

Position Highlights

The University of Arizona College of Medicine - Phoenix, seeks a highly motivated Research Data Scientist to support cutting-edge biomedical research focused on genomics, single-cell biology, and spatial transcriptomics in the Wondisford Laboratory, Department of Internal Medicine. The successful candidate will work with faculty investigators, including members of Dr. Wondisford's laboratory and Dr. Shenfeng Qiu, Director of Spatial Transcriptomics Core Facility, to analyze and interpret large-scale multi-omics datasets generated from diverse biological systems and disease models.This position will play a critical role in advancing research projects involving single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq), spatial transcriptomics, and related genomic technologies. The candidate will develop and implement computational workflows for data processing, quality control, cell type annotation, differential expression analysis, integration of multimodal datasets, machine learning applications, visualization, and biological interpretation.

The successful candidate will collaborate closely with investigators throughout the research lifecycle, from experimental design and sample processing through data analysis, figure generation, manuscript preparation, and grant development. While the primary focus is computational analysis, opportunities may exist to participate in wet-lab activities related to tissue collection, sample preparation, library construction, spatial transcriptomics workflows, and coordination of sample submission to external sequencing facilities. The candidate will work in a highly collaborative and interdisciplinary research environment utilizing state-of-the-art single-cell and spatial transcriptomics platforms, including 10x Genomics Chromium, Visium, Visium HD, and Xenium technologies.

Visa sponsorship is not available for this positions.

Outstanding U of A benefits include health, dental, and vision insurance plans; life insurance and disability programs; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; retirement plans; access to U of A recreation and cultural activities; and more!

Duties & Responsibilities
  • Develop, maintain, and optimize computational pipelines for analysis of single-cell RNA sequencing, single-nucleus RNA sequencing, and spatial transcriptomics datasets.
  • Process and analyze large-scale genomic datasets generated from 10x Genomics Chromium, Visium, Visium HD, Xenium, and related platforms.
  • Perform quality control, clustering, cell type annotation, differential gene expression analysis, trajectory analysis, data integration, and multimodal analyses.
  • Apply machine learning, statistical, and bioinformatics approaches to identify biologically meaningful patterns and generate testable hypotheses.
  • Develop reproducible analysis workflows using Linux-based computing environments, high-performance computing resources, and version-controlled code repositories.
  • Generate publication-quality figures, visualizations, summaries, and reports for manuscripts, grant applications, presentations, and progress reports.
  • Work directly with faculty investigators to interpret results, troubleshoot analyses, and develop data-driven research strategies.
  • Assist with management, organization, storage, and archival of large genomic datasets.
  • Collaborate with laboratory personnel regarding experimental design, sample preparation, sequencing strategies, and downstream analyses.
  • Coordinate data transfer, sequencing submissions, sample tracking, and communication with sequencing and genomics service providers.
  • Contribute to preparation of manuscripts, abstracts, presentations, and extramural grant applications.
  • Train students, staff, and investigators in computational analysis methods and best practices for genomic data analysis.
  • Participate in laboratory meetings, research seminars, and collaborative project discussions.
  • May assist with tissue collection, sample preparation, library construction, spatial transcriptomics workflows, and related laboratory activities as needed.
Knowledge, Skills, and Abilities
  • Strong computational and analytical skills with demonstrated experience in biological, genomic, transcriptomic, or other large-scale scientific data analysis.
  • Proficiency in Linux/Unix operating systems and command-line environments.
  • Experience with Bash scripting and workflow automation.
  • Proficiency in R and/or Python programming for scientific computing and data visualization.
  • Experience with commonly used single-cell and spatial transcriptomics software packages.
  • Knowledge of machine learning, statistical analysis, dimensionality reduction, clustering methods, data visualization techniques and biological data integration approaches.
  • Ability to communicate complex computational findings to investigators with diverse scientific backgrounds, and work effectively in a collaborative multidisciplinary research environment.
  • Ability to manage multiple collaborative projects simultaneously while meeting deadlines.
  • Strong organizational skills, attention to detail, excellent written and verbal communication skills.

This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.

Minimum Qualifications
  • Master's degree or equivalent advanced learning attained through professional level experience required.
  • Minimum of 5 years of relevant work experience, or equivalent combination of education and work experience.
Preferred Qualifications
  • Bachelor's degree, Master's degree and/or Ph.D. in Bioinformatics, Computational Biology, Genomics, Biomedical Informatics, Computer Science, Statistics, Systems Biology, Neuroscience, Biomedical Sciences, or a related field.
  • Experience analyzing single-cell RNA sequencing and/or single-nucleus RNA sequencing datasets.
  • Experience analyzing spatial transcriptomics datasets generated using 10x Genomics Visium, Visium HD, Xenium, MERFISH, CosMx, or related platforms.
  • Experience using Seurat, Scanpy, scvi-tools, CellChat, Monocle, Harmony, Azimuth, SingleR, or related software packages.
  • Experience with machine learning, deep learning, artificial intelligence, or multimodal data integration methods.
  • Experience utilizing high-performance computing clusters and cloud-based computing environments.
  • Familiarity with wet-laboratory techniques related to genomics, next-generation sequencing, single-cell technologies, or spatial transcriptomics.
  • Experience contributing to peer-reviewed publications, grant applications, and collaborative research projects.
  • Experience developing reproducible computational workflows and software tools for biological data analysis.
FLSA

Exempt

Full Time/Part Time

Full Time

Number of Hours Worked per Week

40

Job FTE

1.0

Work Calendar

Fiscal

Job Category

Research

Benefits Eligible

Yes - Full Benefits

Rate of Pay

$75,540 - $98,201

Compensation Type

salary at 1.0 full-time equivalency (FTE)

Grade

10

Compensation Guidance

Rate of Pay Field represents the University of Arizona's good faith and reasonable estimate of the range of possible compensation at the time of posting. The University considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, key skills, and internal equity. The Grade Range represent a full range of career compensation growth over time. The university offers compensation growth opportunities within its career architecture. To learn more about compensation, please review our Applicant Compensation Guide and our Total Rewards Calculator.

Career Stream and Level

PC3

Job Family

Research & Data Analysis

Job Function

Research

Type of criminal background check required

Name-based criminal background check (non-security sensitive)

Number of Vacancies

1

Contact Information for Candidates

Office of Human Resources, Talent Acquisition

talent@arizona.edu

Documents Needed to Apply

Resume and Cover Letter

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