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Genomic Data Science Jobs in California (NOW HIRING)

ROSALIND

Bioinformatics Engineer

San Diego, CA · On-site

ROSALIND empowers Scientists globally to discover life's unknowns through genomic data interpretation. As a Bioinformatics Engineer, you will develop software for analyzing and visualizing complex ...

Moffitt Cancer Center

$77K - $117K/yr

This role works with large-scale clinical, imaging, pathology, molecular, genomic, and outcomes ... Computer Science, Data Science, Statistics, Biostatistics, Mathematics, Bioinformatics, Biomedical ...

New

Tempus

Data Architect, Next Platform

Redwood City, CA · On-site +1

$150K - $200K/yr

... genomic and imaging data-are seamlessly accessible to autonomous AI agents. Our goal is to move ... Education: Bachelor's Degree in Computer Science, Health Informatics, or a related field.

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Genomic Data Science information

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$16

$56

$80

How much do genomic data science jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for genomic data science in California is $56.07, according to ZipRecruiter salary data. Most workers in this role earn between $46.01 and $66.44 per hour, depending on experience, location, and employer.

What is genomic data science?

Genomic data science is an interdisciplinary field that focuses on analyzing and interpreting vast amounts of genetic and genomic data using computational and statistical methods. Professionals in this field use bioinformatics tools, machine learning, and data analysis techniques to uncover insights about genes, diseases, evolution, and biological processes. Genomic data scientists often work with large datasets from next-generation sequencing and collaborate with biologists, clinicians, and researchers to advance our understanding of genetics and its applications in medicine and research.

How does a genomic data scientist typically collaborate with multidisciplinary teams in research or healthcare settings?

Genomic Data Scientists often work closely with biologists, clinicians, statisticians, and software engineers to interpret complex genomic datasets. Collaboration usually involves translating biological questions into analytical tasks, developing and applying computational pipelines, and communicating findings in accessible terms. Regular meetings, shared project management tools, and cross-disciplinary workshops are common, fostering an environment where diverse expertise is integrated to advance research or clinical objectives. This collaborative approach not only enhances the quality of insights but also provides opportunities for professional growth and learning from adjacent fields.

What are the key skills and qualifications needed to thrive as a genomic data scientist, and why are they important?

To excel as a Genomic Data Scientist, you need a strong background in bioinformatics, genetics, and statistical analysis, typically supported by a degree in biology, computational science, or a related field. Familiarity with tools like Python, R, next-generation sequencing (NGS) platforms, and databases such as Ensembl is crucial, as is experience with cloud computing or high-performance computing environments. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are vital soft skills in this role. These competencies enable accurate data interpretation, effective collaboration, and innovative research that advances our understanding of genomics.

What is the difference between Genomic Data Science vs Bioinformatics?

AspectGenomic Data ScienceBioinformatics
Required CredentialsDegree in Data Science, Bioinformatics, or related fields; programming skillsDegree in Bioinformatics, Biology, or related fields; computational skills
Work EnvironmentResearch labs, biotech companies, healthcare institutionsResearch labs, academic institutions, biotech firms
Industry UsageAnalyzing large genomic datasets, developing algorithmsSequence analysis, genome annotation, biological data interpretation
Common Search IntentData analysis in genomics, computational methods for geneticsGenomic sequence analysis, biological data processing

While both roles involve working with genomic data, Genomic Data Science focuses on applying data science techniques, machine learning, and statistical analysis to large genomic datasets. Bioinformatics emphasizes biological sequence analysis, genome annotation, and biological interpretation. Both fields often overlap but differ mainly in their core focus: data science methods versus biological data analysis.

What are popular job titles related to Genomic Data Science jobs in California?

For Genomic Data Science jobs in California, the most frequently searched job titles are:

What job categories do people searching Genomic Data Science jobs in California look for?

The top searched job categories for Genomic Data Science jobs in California are:

Genomic Data Science jobs near you
Infographic showing various Genomic Data Science job openings in California as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, 2% Temporary, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $116,623 per year, or $56.1 per hour.

Data Scientist (Statistician)

Brain Resource

San Francisco, CA

Full-time

Re-posted 12 days ago

Job description

Company Description

Brain Resource's mission is to make sense of the brain's complexities and provide a coherent model for measuring brain health. Healthcare requires evidence-based data and reliable benchmarks in order to improve the way decisions in brain health. Brain Resource established the first and largest international human brain database.


Our research sector conducted the International Studies to Predict Optimized Treatment (iSPOT) Response - in Depression and ADHD and is focused on a single goal: to improve the treatment outcomes for millions with Depression and ADHD by identifying objective tests to predict treatment response.

Job Description

We are seeking a data scientist (statistician) to aid with the analysis of the data collected in our iSPOT studies (see http://www.brainresource.com/research/ispot). The Depression component of this study aims to identify biomarkers that can objectively predict which antidepressants are most likely to help which people with depression, and to develop a range of tests that have real-world application. The ADHD component aims to identify biomarkers that can predict which children/adolescents have ADHD and will respond to stimulant medications. Taking an integrative neuroscience approach, these studies span clinical, cognitive, psychophysiological, imaging and genomic data, and has 20 peer-reviewed publications to date. You will be involved in the design and running of analyses, and writing up these methods and results.
Additionally we require a range of analyses that further explore data in the Brain Resource International Database, which now has several hundred thousand datasets.
This is a high profile position in a world leading study, with a global multidisciplinary team from a range of academic institutions and backgrounds.
This position is based in San Francisco, CA.

Qualifications

Required Attributes
Graduate degree or equivalent, with experience in research and statistical analysis
Experience using the R statistical package (or Python/Scala) at an advanced level, including scripting, automated analyses and data visualization.
Strong experience with statistical data analysis for the social sciences or equivalent human clinical datasets
Sound knowledge of and experience with predictive statistics (with preferred experience in cross validation or similar techniques)
Experience with large datasets (with preference for multidisciplinary data experience)
Highly skilled in distilling complicated results and openly communicating challenges
Excellent communication and interpersonal skills, with demonstrated ability to liaise effectively with a range of people (e.g., senior management and other researchers from a range of disciplines)
Highly developed organizational skills, including prioritization and time management
Strong ability to work both independently and as part of a team
Demonstrated problem solving skills and a strong focus on solutions
Ability to contribute to publications
Ability to present outcomes to a range of interested audiences
Preferred Attributes
Preferred statistical experience includes a good understanding and use of logistic regression, predictive models, multiple imputation, spline regression, linear mixed models for dose/regime type trial designs, simulations, cross validation, correction for multiple testing, principle components analysis.
Experience with bash scripting and pipeline development. programming for parallel computing
Experience in pharmaceutical or large clinical trials, particularly in the planning, design or data analysis.
Experience with submissions to the FDA
Experience in the field of neuroscience, psychology or psychometrics.
Knowledge of genetics microarray/SNP data or EEG data analysis

Additional Information

This is a full-time position. Salary will be dependent on the experience and skills of the applicant. The position is located at our San Francisco office in the Financial District.
To apply, please include a CV and cover letter addressing the necessary skills and experience outlined above. Applications and further enquiries should be directed to:  Donna Palmer.

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