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Genomic Data Science Jobs in California (NOW HIRING)

Natera

Director of Data Science and Bioinformatics

San Carlos, CA · On-site

$195K - $244K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.

Bristol Myers Squibb

Associate Director, Data Science

Brisbane, CA · On-site

$71K - $71K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

As an Associate Director, you will provide scientific leadership across diverse data types generated in drug development - including clinical trial data, genomics, proteomics, imaging, flow cytometry ...

New

Novartis

Senior Expert I, Data Science

San Diego, CA

$126K - $234K/yr

  • Medical

  • Life

  • Retirement

  • PTO

The Oncology Data Science group within Biomedical Research supports the Oncology Disease Area with ... Lead profiling strategies and analysis of high-throughput genomic and phenotypic screening data to ...

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All JobsGenomic Data Science JobsGenomic Data Science Jobs in California

Genomic Data Science information

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$56

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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.

Director of Data Science and Bioinformatics

Natera

San Carlos, CA

Full-time

Posted 3 days ago

New

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 38 frontline employees who took The Breakroom Quiz

56th of 120 rated laboratories

Job description

This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.

PRIMARY RESPONSIBILITIES:

Strategy and Vision

  • Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.
  • Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices

Infrastructure and Automation

  • Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.
  • Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.

Cross-Functional Collaboration

  • Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization

Team Leadership

  • Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.
  • Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.

QUALIFICATIONS:

  • Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).
  • Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.
  • Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.
  • Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.
  • Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.
  • Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.

PREFERRED QUALIFICATIONS:

  • Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).
  • Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).
  • Advanced knowledge of statistical inference, machine learning, and genomic data processing.

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