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Grid Dynamics Holdings

Research Scientist

Advanced degree (MS/PhD) in Linguistics, Information Science, Computational Social Science, Cognitive Science, or a related socio-technical field. * Strong understanding of taxonomy design ...

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How much do computational social science jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for computational social science in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What is computational social science?

Computational social science is an interdisciplinary field that uses computational methods, such as data analysis, simulations, and modeling, to study social phenomena and human behavior. It combines tools from computer science, statistics, and social sciences to analyze large-scale social data, like social media activity, online interactions, or census records. This approach helps researchers uncover patterns and trends that would be difficult or impossible to detect using traditional social science methods alone.

What are the key skills and qualifications needed to thrive as a Computational Social Scientist, and why are they important?

To thrive as a Computational Social Scientist, you need a solid background in social science research methods, statistics, and programming—often supported by an advanced degree in a relevant field. Familiarity with data analysis tools such as Python, R, machine learning libraries, and experience with large datasets or social network analysis software is typical. Strong analytical thinking, interdisciplinary collaboration, and effective communication skills help you interpret results and convey insights to diverse audiences. These competencies are crucial for generating impactful, data-driven insights into complex social phenomena and informing decision-making.

What is the difference between Computational Social Science vs Data Scientist?

AspectComputational Social ScienceData Scientist
Required CredentialsSocial science background, programming skillsStatistics, programming, domain knowledge
Work EnvironmentResearch institutions, academia, social research firmsTech companies, finance, healthcare
Employer & Industry UsageUniversities, government agencies, social research organizationsCorporations, startups, consulting firms
Common Search & ComparisonYesYes

Computational Social Science focuses on analyzing social phenomena using computational methods rooted in social science theories, often within academic or research settings. Data Scientists, however, apply statistical and machine learning techniques to large datasets across various industries. While both roles require programming skills, Computational Social Science emphasizes social theory and research, whereas Data Science centers on data analysis and business insights.

What types of data sources and analytical methods are commonly used in Computational Social Science roles?

In Computational Social Science positions, professionals typically work with large and diverse datasets, including social media feeds, digital communication records, surveys, and online behavioral data. Analytical methods often involve a mix of quantitative techniques such as network analysis, machine learning, natural language processing, and agent-based modeling. Collaborative projects may require integrating insights from computer science, sociology, and statistics, making interdisciplinary teamwork a frequent part of the role. Adapting to evolving data privacy guidelines and ensuring ethical data use are also important daily considerations.
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Computational Social Science jobs near you
Infographic showing various Computational Social Science job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Temporary. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.
Research Scientist

Research Scientist

Grid Dynamics Holdings

Remote

Full-time

Medical, Dental, Vision

Posted 4 days ago

Job description

This role is for a Research Scientist to support the design and development of safety evaluation methodologies for generative AI features that will reach multiple languages and markets.
You will play a crucial role in building upon product safety requirements to create taxonomies, compose and curate exemplar safety evaluation datasets, and ensure that evaluation frameworks are culturally and linguistically grounded.
An ideal candidate possesses a strong understanding of technology evaluation design principles and practices, experiences designing evaluations to support policies and/or product requirements, and classification systems, and annotation and/or study participant guidelines.
Essential functions
  • Taxonomy Development: Design, refine, and maintain safety-relevant taxonomies that capture risk categories, content types, and policy distinctions, achieved through collaborations with subject matter experts who bring knowledge across languages and cultural contexts. You will work collaboratively to ensure taxonomies are comprehensive, internally consistent, and actionable for downstream evaluation work.
  • Exemplar Curation: Develop and validate exemplar sets that illustrate taxonomy categories, edge cases, and boundary conditions. Collaborate with language and cultural experts to ensure exemplars are culturally appropriate and representative across target markets.
  • Policy-to-Data Translation: Partner with policy, product, and engineering teams to translate responsible AI policies and guidelines into concrete data requirements, annotation schemas, and evaluation criteria that can be operationalized across markets.
  • Documentation & Communication: Produce clear, detailed documentation of taxonomies, sampling methodologies, and design rationale. Present findings and recommendations to cross-functional stakeholders including engineering, product, and policy teams.
  • Canonical Guideline Development: Author and maintain canonical evaluation guidelines that standardize task definitions, rating criteria, and edge-case handling. These assets will be adapted to scale across languages and markets, with the support of multi-lingual and operations experts. You will ensure guidelines are clear, complete, and adaptable.
  • Task Setup & Configuration: Collaborate with partners to configure evaluation tasks, including platform setup, workflow design, annotator assignment, and quality control mechanisms. Ensure task configurations align with research design specifications.
  • Pilot Design & Execution: Design and run pilot evaluations to validate task setups, identify guideline ambiguities, calibrate annotator understanding, and surface issues before full-scale deployment. Analyze pilot results and iterate on guidelines and configurations accordingly.
  • Monitoring & Data Quality: Develop and implement monitoring frameworks to track evaluation progress, annotator performance, inter-rater agreement, and data quality in real time. Flag anomalies and implement corrective actions to maintain data integrity across markets.

Qualifications
  • 2+ years of experience in an applied research setting related to evaluation design, AI ethics, Responsible AI, AI safety, computational social science, content analysis, or a closely related field.
  • Advanced degree (MS/PhD) in Linguistics, Information Science, Computational Social Science, Cognitive Science, or a related socio-technical field.
  • Strong understanding of taxonomy design, classification systems, and annotation methodology.
  • Experience developing evaluation guidelines and exemplar sets for human annotation or labeling tasks.
  • Demonstrated ability to collaborate with subject matter experts (e.g., linguists, cultural consultants, multi-lingual annotators) to inform research design.
  • Able to work independently to drive outcomes among cross-functional teams, with minimal direction.
  • Organized, highly attentive to detail, and manages time well.
  • Excellent written and oral communication skills.
  • Experience working in industry.

Would be a plus
  • Experience designing evaluation frameworks for multilingual or cross-cultural contexts.
  • Familiarity with responsible AI, AI safety, or content moderation policy frameworks.
  • Experience with experimental design methodologies, inter-rater reliability data analysis and annotation quality assessment methods.
  • Prior experience working with localization, internationalization, or language service teams.
  • Experience with survey design, psychometrics, or structured content analysis methodologies.

We offer
  • Opportunity to work on cutting-edge projects
  • Work with a highly motivated and dedicated team
  • Competitive salary
  • Flexible schedule
  • Benefits package - medical insurance, vision, dental, etc.
  • Corporate social events
  • Professional development opportunities
  • Well-equipped office

About us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI , supported by profound expertise and ongoing investment in data , analytics , cloud & DevOps , application modernization and customer experience . Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

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