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Research Data Analyst Jobs in California (NOW HIRING)

Biomedical Data Science

Palo Alto, CA · On-site

$110 - $170/hr

Stanford University is seeking a Research Data Analyst 2 - Biomedical Data Science to manage and analyze large amounts of information, typically technical or scientific in nature, independently with ...

Data Analyst

Vallejo, CA · On-site

$74K/yr

Knowledge of institutional research methodologies and data reporting requirements in higher ... analytical skills Strong written, verbal and interpersonal communication skills Ability to work ...

Understanding the client's problem and identifying the right research questions * Data: Corralling the data needed for analysis by cleaning, linking, and structuring large datasets * Analysis:

Understanding the client's problem and identifying the right research questions * Data : Corralling the data needed for analysis by cleaning, linking, and structuring large datasets * Analysis

Showing results 41-60

Research Data Analyst information

See California salary details

$38K

$73K

$98.2K

How much do research data analyst jobs pay per year?

As of Sep 6, 2026, the average yearly pay for research data analyst in California is $72,996.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,300.00 and $97,200.00 per year, depending on experience, location, and employer.

What is a research data analyst?

A Research Data Analyst is a professional who collects, processes, and analyzes data to support research projects across various fields, such as healthcare, social sciences, or business. They use statistical tools and programming languages to interpret complex data sets and help researchers draw meaningful conclusions. Their responsibilities often include data cleaning, statistical analysis, and visualization of results for reports or publications. Research Data Analysts play a crucial role in ensuring the quality and integrity of the data used in research studies.

What are the key skills and qualifications needed to thrive as a research data analyst, and why are they important?

To excel as a Research Data Analyst, you need strong analytical skills, proficiency in statistics, and a relevant degree in fields such as mathematics, statistics, or computer science. Familiarity with statistical software (e.g., R, SAS, SPSS), data visualization tools (like Tableau or Power BI), and programming languages (such as Python) is typically required. Attention to detail, critical thinking, and the ability to clearly communicate complex findings are crucial soft skills. These competencies ensure accurate data interpretation, effective collaboration with research teams, and impactful decision-making based on reliable insights.

What are some common challenges research data analysts face when working with large datasets, and how are they addressed?

Research Data Analysts often encounter challenges such as data inconsistencies, missing values, and integrating data from multiple sources when working with large datasets. Addressing these challenges typically involves implementing rigorous data cleaning protocols, utilizing statistical software for data validation, and collaborating closely with research teams to understand the context behind the data. Analysts may also create documentation and standardized procedures to streamline future data processing and ensure data integrity. Staying updated with best practices in data management can help mitigate many of these issues.

What is the difference between Research Data Analyst vs Data Scientist?

AspectResearch Data AnalystData Scientist
Required CredentialsBachelor's degree in data analysis, statistics, or related field; often certifications in data toolsBachelor's or master's in computer science, statistics, or related; often advanced certifications or training
Work EnvironmentResearch institutions, universities, or corporate research divisionsTech companies, finance, healthcare, or any industry leveraging big data
Employer & Industry UsageAcademic, government, and research organizationsPrivate sector, startups, and large corporations

Research Data Analysts focus on analyzing data to support research projects, often working within academic or research settings. Data Scientists have a broader scope, including building predictive models and advanced analytics across various industries. While both roles require strong analytical skills and familiarity with data tools, Data Scientists typically have more advanced technical expertise and work on complex data modeling tasks.

What are the most commonly searched types of Research Data Analyst jobs in California?

The most popular types of Research Data Analyst jobs in California are:

What cities in California are hiring for Research Data Analyst jobs?

Cities in California with the most Research Data Analyst job openings:

Infographic showing various Research Data Analyst job openings in California as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 15% Part Time, and 5% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $72,996 per year, or $35.1 per hour.

Research Engineer, Economic Research Data Platform

Anthropic

San Francisco, CA

Full-time

Re-posted 29 days ago


Job description

About the role

As a Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis.

The Economic Research team is part of the Anthropic Institute, and studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data, including the Anthropic Economic Index, for the benefit of the public - helping policymakers, businesses, and workers understand and navigate the transition to powerful AI. The questions we work on include: how is AI changing jobs and economic activity, who is adopting it and why, and what determines whether a region or industry captures value from it.

In this role, you will work closely with teams across Anthropic - including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy - to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting and implementing high-quality internal infrastructure, working in a fast-paced environment, and navigating ambiguity.

Responsibilities:

  • Build and operate the data pipelines that turn raw usage data into clean, reusable, privacy-preserving datasets
  • Design new systems - including developing classifiers, training probes on model internals, and building the ML pipelines behind them - for understanding how Claude is used and the impact it's having on the economy
  • Build self-serve workflows to ingest and integrate external data sources so they're interoperable with internal datasets
  • Develop the APIs, libraries, and interfaces that serve data to researchers and the public
  • Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic's safety mission
  • Contribute to the team roadmap, documentation, and practices that enable self-serve data access while maintaining safety and governance standards
  • Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure

You might be a good fit if you:

  • Have significant experience building data-intensive applications, pipelines, or internal tooling in production
  • Have experience with cloud infrastructure platforms such as AWS or GCP, and take pride in writing clean, well-documented code in Python that others can build upon
  • Have intuition for analytics workflows and empathy for how researchers and data scientists work
  • Are comfortable making technical decisions with incomplete information while keeping engineering standards high
  • Have a "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description
  • Have strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise
  • Care about the societal impacts of your work, and are interested in AI's economic implications

Bonus qualifications:

  • Experience with modern data transformation, orchestration, and query frameworks
  • Building systems and products on top of LLMs
  • Privacy-preserving data systems, or data governance and lineage tooling
  • Building and operating web services and the infrastructure underneath them
  • Full-stack development or complex data visualization
  • Background in econometrics, statistics, or quantitative social science
  • Working in environments where engineers partner closely with quantitative users - research labs, trading firms, analytics companies
Some Examples of Our Recent Work
    • Anthropic Economic Index report: Learning curves 
    • Labor market impacts of AI: A new measure and early evidence 
    • Anthropic Economic Index Report: Economic Primitives
    • Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption
    • Estimating AI productivity gains from Claude conversations
    • The Anthropic Economic Index

Deadline to apply: None. Applications are reviewed on a rolling basis