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Biotech Data Science Jobs in California (NOW HIRING)

Fullpower Technologies

Data Scientist

Santa Cruz, CA · Remote

$130K - $170K/yr

... and biotechnology. Fullpower's platform is vetted and deployed as a PaaS, backed by a patent ... The ideal candidate will have a strong background in machine learning and data science and a proven ...

Fullpower Technologies, Inc.

Data Scientist

Santa Cruz, CA · On-site

$130K - $170K/yr

... and biotechnology. Fullpower's platform is vetted and deployed as a PaaS, backed by a patent ... The ideal candidate will have a strong background in machine learning and data science and a proven ...

Allogene Therapeutics

MSAT Data Science Engineer

Newark, CA · On-site

$120K - $140K/yr

Allogene Therapeutics, with headquarters in South San Francisco, is a clinical-stage biotechnology ... We are seeking a highly motivated individual to join us as a Data Science Engineer, Manufacturing ...

Allogene Therapeutics

MSAT Data Science Engineer

Newark, CA · On-site

$120K - $140K/yr

Allogene Therapeutics, with headquarters in South San Francisco, is a clinical-stage biotechnology ... We are seeking a highly motivated individual to join us as a Data Science Engineer, Manufacturing ...

BIOMARIN PHARMACEUTICAL

Data Scientist 2

Novato, CA

This role blends advanced technical skills in Data Science-covering statistics, Modelling, AI/ML-with deep domain expertise in highly regulated Biotech industry. These should be complemented by soft ...

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Biotech Data Science information

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$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.
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MSAT Data Science Engineer

MSAT Data Science Engineer

Allogene Therapeutics

Newark, CA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
Allogene Therapeutics is a clinical-stage biotechnology company pioneering the development of allogeneic chimeric antigen receptor T cell products for cancer and autoimmune disease. They are seeking a highly motivated Data Science Engineer to manage manufacturing process data, ensure robust data infrastructure, and support technical initiatives for process understanding and qualification activities.
Responsibilities:
• Ownership of data lake infrastructure, including data ingestion, cleaning, organization, visualization and integration with data analysis tools.
• Translate process understanding into data and develop data analysis methodology to inform process and quality decisions.
• Generate data packages to support PPQ-readiness, PPQ execution and BLA submission for allogeneic CAR-T therapies and critical starting materials
• Able to apply and develop advanced technologies, scientific principles, theories and concepts to meet the needs of the Process Development and Manufacturing teams, including support of technology transfers
• In-depth knowledge of GMP and regulatory expectations and experience with regulatory inspections
• Work with Quality, Facilities & Engineering, Process Development and IT to ensure cross-functional alignment
• Closely partner with the Process Development and Quality groups to ensure continuity of data, robust process design and monitoring of product quality
• Contribute to company-wide AI initiatives as a thought leader for introduction and implementation of new AI tools for continuous improvement and innovation.
• Engage with broader manufacturing team to enable accomplishment of department goals
• Other duties as assigned
Qualifications:
Required:
• Minimum of 6 years of experience within a GMP pharmaceutical manufacturing space
• 2 years of direct data science experience within a GMP pharmaceutical manufacturing space
• Proficiency in programming languages including SQL, Python and R and data analytics tools, including JMP, Spotfire, Tableau, R-Studio
• Knowledge of pharmaceutical manufacturing processes and GMP requirements for data integrity.
• Excellent written and verbal communication skills
• Excellent organizational skills and an ability to prioritize effectively to deliver results within established timelines
• Ability to work independently and as part of a team
• Ability to travel up to 10%
• Candidates must be authorized to work in the U.S.
Preferred:
• Bachelor’s degree in science or engineering (with a focus on computer science or data science preferred)
• Late-stage clinical and commercial experience preferred.
• Cell therapy experience preferred.
• Experience developing and implementing data solutions with machine learning and AI preferred.
Company:
Allogene Therapeutics is a biotechnology company catalyzing cancer treatment through the development of CAR T therapy. Founded in 2017, the company is headquartered in South San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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