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Gene Data Scientist Jobs (NOW HIRING)

Integrated Resources INC

Scientist-II

Spring House, PA · On-site

The candidate must have a current knowledge of the bioinformatics scientific literature (especially ... Statistical skills and experience with large datasets ( gene expression, proteomic data, and NGS ...

Cellarity

Machine Learning Scientist

Somerville, MA · On-site

As a lead ML Scientist the candidate will develop and apply AI methods to identify novel ... Experience with high-dimensional biological data analysis (bulk/single-cell RNA-seq, gene ...

Cellarity

Machine Learning Scientist

Somerville, MA · On-site

As a lead ML Scientist the candidate will develop and apply AI methods to identify novel ... Experience with high-dimensional biological data analysis (bulk/single-cell RNA-seq, gene ...

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Gene Data Scientist information

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How much do gene data scientist jobs pay per year?

As of Sep 9, 2026, the average yearly pay for gene data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are popular job titles related to Gene Data Scientist jobs?

For Gene Data Scientist jobs, the most frequently searched job titles are:

Infographic showing various Gene Data Scientist job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Data Scientist, Department of Internal Medicine (Phoenix)

Phoenix, AZ

University of Arizona
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about University of Arizonaexternal

Full-time

Posted 6 days ago

University Of Arizona rating

7.4

Company rating: 7.4 out of 10

Based on 69 frontline employees who took The Breakroom Quiz

339th of 633 rated colleges and universities

Job description

  • 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.

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