ZipRecruiter

Log In

1

Computational Science Jobs in California (NOW HIRING)

Novartis

Computational Medicinal Chemist

San Diego, CA · On-site

$138K - $257K/yr

  • Medical

  • Life

  • Retirement

  • PTO

... computational scientist like you to join our ranks. Imagine the opportunity to unlock hidden ... Collaborating with a multidisciplinary group of scientists, you will be at the forefront of ...

Pfizer

Senior Computational Chemist

La Jolla, CA · On-site +1

$106K - $176K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Position Summary The Machine Learning & Computational Sciences (MLCS) organization within Medicine Design at Pfizer partners broadly across R&D to deliver computational, cheminformatics, and machine ...

Pfizer, Inc.

Senior Computational Chemist

La Jolla, CA · On-site

$106K - $176K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Position Summary The Machine Learning & Computational Sciences (MLCS) organization within Medicine Design at Pfizer partners broadly across R&D to deliver computational, cheminformatics, and machine ...

previous page

Showing results 41-60

All JobsComputational Science JobsComputational Science Jobs in California

Computational Science information

See California salary details

$55.8K

$82K

$96.7K

How much do computational science jobs pay per year?

As of Aug 18, 2026, the average yearly pay for computational science in California is $82,020.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,500.00 and $92,300.00 per year, depending on experience, location, and employer.

What is computational science?

Computational science is an interdisciplinary field that uses advanced computing capabilities to understand and solve complex problems. It combines elements of mathematics, computer science, and domain-specific knowledge to create simulations, analyze data, and model physical, biological, or social systems. Computational scientists develop algorithms and use high-performance computing to tackle problems that are difficult or impossible to solve analytically. This field is essential in areas such as climate modeling, drug discovery, engineering, and physics.

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

To thrive as a Computational Scientist, you need a strong background in mathematics, programming (such as Python, C++, or MATLAB), and domain-specific scientific knowledge, often supported by an advanced degree in a relevant field. Familiarity with high-performance computing (HPC) systems, parallel processing frameworks, and scientific data analysis tools is typically required. Excellent problem-solving skills, collaboration, and effective communication set top candidates apart in interdisciplinary research environments. These skills and qualities are crucial for driving innovative scientific discovery and translating complex data into actionable insights.

What are some common challenges faced by computational scientists when working on interdisciplinary projects?

Computational scientists often collaborate with experts from fields like biology, physics, or engineering, which can present challenges in bridging gaps in domain-specific knowledge and communication styles. Adapting computational models to fit the unique requirements of different disciplines, while ensuring accuracy and efficiency, is a frequent hurdle. Additionally, managing large datasets and integrating diverse computational tools requires strong technical and organizational skills. Open communication and a willingness to learn from colleagues are key to overcoming these challenges and achieving successful project outcomes.

What is the difference between Computational Science vs Data Scientist?

AspectComputational ScienceData Scientist
Required CredentialsDegree in science, engineering, or computational fields; often requires advanced degreesDegree in statistics, computer science, or related fields; often requires knowledge of programming and analytics
Work EnvironmentResearch labs, universities, industry R&D departmentsTech companies, finance, healthcare, consulting firms
Industry UsageScientific research, simulation, modelingData analysis, predictive modeling, business insights
Common Search/ComparisonYesYes

Computational Science focuses on developing models and simulations to solve scientific and engineering problems, often requiring advanced degrees and research environments. Data Scientists analyze large datasets to extract insights and support decision-making, typically working in business or tech sectors. While both roles involve programming and data handling, their primary goals and work settings differ significantly.

Is computational science a good career?

Computational science is a viable career that involves using computer models, simulations, and data analysis to solve complex scientific problems. It typically requires strong skills in programming, mathematics, and domain knowledge, and offers opportunities in research, industry, and academia with competitive salaries and growth potential.

What can you do with a computational science degree?

A computational science degree prepares individuals for roles such as computational scientist, data analyst, simulation engineer, or research scientist. Graduates often work in industries like technology, healthcare, finance, or government, utilizing skills in programming, modeling, and data analysis to solve complex problems. Knowledge of tools like Python, MATLAB, or high-performance computing environments is also valuable.

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

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

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

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

What cities in California are hiring for Computational Science jobs?

Cities in California with the most Computational Science job openings:

Computational Science jobs near you
Infographic showing various Computational Science job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution, with an average salary of $82,020 per year, or $39.4 per hour.

Computational Biologist

Verge Genomics

San Francisco, CA • On-site, Remote

Full-time

Re-posted 16 days ago

Job description

Who We Are
Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field's largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.
Your Mission
Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Computational Biologist (AI/ML) will be responsible for defining and enabling new product offerings leveraging Verge's drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers.
Your 12 Month Outcomes
  • Work with Verge's AI partners to deliver a best-in-class biology foundation model with Verge's proprietary datasets
  • Develop a novel approach that enables a powerful new product offering (patient stratification, biomarker discovery, etc.)
  • Deliver at least two CONVERGE-powered insights projects to pharma/biotech companies
  • Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration

You Will
  • Develop and evaluate cutting-edge computational methodologies integrating multi-omic datasets to develop predictive models for translational biology,
  • Lead high-impact projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration,
  • Lead partnerships with AI companies to co-develop next-generation foundation models for drug discovery
  • Frame biological problems in computational terms and design solutions that are biologically meaningful, interpretable, and experimentally testable,
  • Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications,
  • Translate between biological domain knowledge and machine learning objectives.

Requirements
Candidates must have:
  • Either:
    • PhD in computational biology, AI/ML, applied statistics, biophysics, or,
    • MS and professional experience in relevant fields.
  • ≥5 years of experience working in applied computational biology and integration of multi-omic datasets (RNA-seq, genotyping, clinical), with ≥2 years in a startup environment,
  • ≥2 years of experience in relevant areas of translational science, demonstrating a deep understanding of target identification, biomarker discovery, and/or patient stratification,
  • Proven ability to implement, evaluate, and/or create computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery,
  • Fluency with state of the art in systems biology workflows, including off-the-shelf biological databases and computational biology tools,
  • Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
  • Track record of individual innovation, with published research or shipped work influencing pharma R&D decisions
  • Experience running a significant number of end-to-end RNA-Seq data analyses (from QC, read quantification, normalization through to interpretation),
  • Excellent coding skills in Python, with experience in relevant ML/AI libraries (e.g., PyTorch, HuggingFace, scikit-learn, pandas, numpy). A demonstrable portfolio (e.g., GitHub, research code, or shared notebooks) is highly preferred,
  • Experience in building and evaluating machine learning models on biological data, ideally with transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a deep understanding of feature selection, model interpretability,
  • Professional experience with AI workflows, including natural language processing (NLP), retrieval-augmented generation (RAG), embeddings, vectorization of diverse data types, and working with large language models (e.g., GPT),
  • Demonstrated experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.

Finally, we seek candidates who embrace our values and way of working:
  • Ability to thrive in uncertainty with frequently changing priorities
  • Deep alignment with our values
  • A passion for making an impact on patients

Apply