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Director Of Data Science Bioinformatics Jobs (NOW HIRING)

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How much do director of data science bioinformatics jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for director of data science bioinformatics in the United States is $54.79, according to ZipRecruiter salary data. Most workers in this role earn between $44.95 and $62.50 per hour, depending on experience, location, and employer.

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For Director Of Data Science Bioinformatics jobs, the most frequently searched job titles are:

Infographic showing various Director Of Data Science Bioinformatics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $113,966 per year, or $54.8 per hour.

Director of Data Science and Bioinformatics

San Carlos, CA • On-site

Natera
Biotechnology Research and Development • 1 - 5K employees

Full-time

Posted 27 days ago


Natera rating

7.8

Company rating: 7.8 out of 10

Based on 39 frontline employees who took The Breakroom Quiz

56th of 121 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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