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Biotech Data Science Jobs in California (NOW HIRING)

Amgen

Sr. Data Scientist

Thousand Oaks, CA

Since 1980, we've helped pioneer the world of biotech in our fight against the world's toughest ... Our award-winning culture is collaborative, innovative, and science based. If you have a passion ...

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Biotech Data Science information

See California salary details

$37K

$121.1K

$193.9K

How much do biotech data science jobs pay per year?

As of Jul 13, 2026, the average yearly pay for biotech data science in California is $121,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $134,200.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Biotech data scientists can potentially earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and bioinformatics, and working in senior or specialized roles. Compensation varies based on location, company size, and individual expertise, with some senior-level positions reaching or exceeding this salary level.

What are the most common challenges faced by professionals in Biotech Data Science roles?

One of the primary challenges in Biotech Data Science is working with large, complex, and sometimes incomplete biological datasets, which require advanced analytical approaches and careful data curation. Professionals often need to stay current with rapidly evolving technologies and methods, which can be demanding but also rewarding for those who enjoy continuous learning. Collaboration with scientists, engineers, and regulatory teams is common, so adapting communication styles and translating technical findings to diverse audiences is key. Overcoming these challenges leads to meaningful scientific discoveries and significant career growth opportunities.

What are the key skills and qualifications needed to thrive in the Biotech Data Science position, and why are they important?

To thrive in Biotech Data Science, you need a solid background in biology or biotechnology, strong statistical and analytical skills, and experience with data analysis languages like Python or R. Familiarity with bioinformatics tools, sequencing platforms, and data visualization software is often expected, with certifications in data science or related fields considered a plus. Excellent problem-solving, communication, and collaboration skills are essential when working across multidisciplinary teams. These competencies enable effective interpretation of complex biological data, driving innovation and insights in the biotech industry.

What is a biotech data scientist?

A biotech data scientist analyzes biological and medical data to support research and development in the biotechnology industry. They use skills in statistics, programming, and machine learning, often working with tools like Python, R, and SQL to interpret complex datasets and inform decision-making.

What is a Biotech Data Science job?

A Biotech Data Science job involves analyzing complex biological and pharmaceutical data to drive research, innovation, and decision-making. Professionals in this field use machine learning, statistical modeling, and bioinformatics tools to extract insights from genomics, clinical trials, and drug discovery datasets. They collaborate with scientists, engineers, and healthcare professionals to improve treatments, develop new therapies, and optimize bioprocesses. Strong programming skills, domain knowledge in biology or biotechnology, and expertise in data analysis are essential for success in this role.

How can data science be used in biotechnology?

Biotech data scientists analyze large biological datasets to identify patterns, develop predictive models, and optimize processes such as drug discovery, genetic research, and personalized medicine. They use tools like machine learning, statistical analysis, and bioinformatics software to support research and development efforts in biotechnology companies and labs.

Can a biotechnologist become a data scientist?

A biotechnologist can become a data scientist by acquiring skills in programming, statistics, and machine learning, often through additional training or education such as online courses or advanced degrees. Their background in biology and laboratory data can provide a strong foundation for analyzing complex datasets in data science roles within biotech and healthcare industries.
What are the most commonly searched types of Biotech Data Science jobs in California? The most popular types of Biotech Data Science jobs in California are:
What are popular job titles related to Biotech Data Science jobs in California? For Biotech Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Biotech Data Science jobs in California look for? The top searched job categories for Biotech Data Science jobs in California are:
What cities in California are hiring for Biotech Data Science jobs? Cities in California with the most Biotech Data Science job openings:
Biotech Data Science jobs near you
Associate Director, Quality Engineer/Data Science

Associate Director, Quality Engineer/Data Science

Allogene Therapeutics

South San Francisco, CA • On-site

$170K - $210K/yr

Full-time

Medical

Posted 7 days ago

Job description

Job Description
About Allogene:
Allogene Therapeutics, with headquarters in South San Francisco, is a clinical-stage biotechnology company pioneering the development of allogeneic chimeric antigen receptor T cell (AlloCAR T) products for cancer and autoimmune disease. Led by a management team with significant experience in cell therapy, Allogene is developing a pipeline of "off-the-shelf" CAR T cell product candidates with the goal of delivering readily available cell therapy on demand, more reliably, and at greater scale to more patients.
About the role:
We are seeking a highly motivated Associate Director, Quality Engineer/Data Science to work in this exciting new area of allogeneic cell therapy. In this role, you will report to the head of Validation and Quality Engineering, and you will be responsible for developing and leading the Quality Data Science/Quality Engineering function to embed the principles and practices of applied statistics, data science and machine learning to further Allogene's efforts in leading the establishment of an allogeneic CAR T platform, and to provide guidance and data driven insights across Allogene's programs. You will work closely with respective project teams to establish a quality data science roadmap to implement a lifecycle approach for product quality control strategies, including tools and technologies to drive innovation and continuous improvement. The position is based out of our headquarters in South San Francisco, CA.
Responsibilities include, but are not limited to:
  • Develop and execute strategy and roadmap to establish quality data science as a core capability for building allogeneic platform knowledge and establish data analytics to enable predict and prevent capabilities across GMP operations.
  • Provide direction and input to project teams utilizing the principles of applied science and statistics to drive strategies for process and analytical understanding and overall product quality control, including approaches for product comparability, process validation, product monitoring, stability trending and lifecycle management/improvement.
  • Contribute to the build out of the Allogene GMP Quality Management System by developing procedures and providing significant input and support to Product Quality Review and Management Review tools and practices.
  • Serve as company authority on quality data science and QE activities in support of regulatory submissions and inspections, including assuring inspection readiness.
  • Support root cause investigations and ensure data-driven decision making.
  • Champion the use of data analytics and operational excellence tools to drive process improvement and innovation across the Allogene enterprise.
  • Integral member of cross-functional teams to garner interdisciplinary insights (e.g. technical, clinical) to further accelerate Allogene's leading approach to the establishment of an "off-the-shelf" allogeneic CAR T platform.
  • Collaborate across functions and stakeholders to develop a Knowledge Management approach for Allogene.
  • Routinely scan external environment and engage in industry forums to proactively identify and implement best practices and new technologies.

Position Requirements & Experience:
  • BS/MS degree in Statistics, Data Science, or related field (advanced degree preferred), ASQ Quality Engineer certification preferred, with at least 6-8 years of relevant experience in the biotechnology or pharmaceutical industry.
  • Strong expertise in applied statistics (e.g., DoE, multivariate analysis, regression, time-series).
  • Hands-on experience with tools such as R, Python, JMP, SAS, or equivalent.
  • Experience working with complex data from laboratory, process, or manufacturing systems and ability to translate complex data insights into actionable business recommendations.
  • Familiarity with visualization tools.
  • Comprehensive knowledge of GMP regulations, guidance, and industry best practices.
  • Proven success influencing cross-functional teams and senior stakeholders without direct authority.
  • Comfortable in a fast-paced small company environment and able to adjust workload based upon changing priorities.
  • Candidates must be authorized to work in the U.S.

We offer a chance to work with talented people in a collaborative environment and provide a top-notch compensation and benefits package, which includes an annual performance bonus, equity, health insurance, generous time off (including 2 annual holiday company-wide shutdowns) and much more. The expected salary range for this role is $170,000 to $210,00 per year. Actual pay will be determined based on experience, qualifications, geographic location, business needs, and other job-related factors permitted by law.
As an equal opportunity employer, Allogene is committed to a diverse workforce. Employment decisions regarding recruitment and selection will be made without discrimination based on race, color, religion, national origin, gender, age, sexual orientation, physical or mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non-job-related characteristics or other prohibited grounds specified in applicable federal, state and local laws. We also embrace differences in experience and background, and welcome diversity of opinions and thought, designed to create a stronger and better Allogene that is focused on developing life-changing products for patients.
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