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Interos Inc.

Lead Computational Social Scientist

Role: Lead Computational Social Scientist   About interos.ai:  interos.ai is the standard for ... You will apply quantitative social-science methods to analyze global supply chains, financial ...

Protagonist

Computational Social Scientist

Washington, DC · On-site

$70K - $102K/yr

Our work lives at the intersection of data science and social science: we pair computational techniques with an understanding of the social, political, and information dynamics behind the data. This ...

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How much do computational social science jobs pay per hour?

As of Sep 14, 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 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.

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 can I do with a computational social science degree?

A computational social science degree prepares individuals for roles such as data analyst, social data scientist, or research analyst, where they analyze social data using statistical tools, programming languages like Python or R, and modeling techniques. Graduates can work in academia, government agencies, or private sector organizations focused on social research, policy analysis, or market research. Strong skills in data analysis, machine learning, and social theory are often essential for these positions.
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Computational Social Science jobs near you
Infographic showing various Computational Social Science job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

PostDoctoral Associate in Computational Social Science

Boulder, CO • On-site

Complex Systems Society
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Posted 13 days ago

Job description

PostDoctoral Associate in Computational Social Science

PostDoctoral Associate in Computational Social Science at University of Colorado, USA

The research groups of Professors Daniel Larremore and Aaron Clauset at the University of Colorado Boulder are seeking exceptional candidates for a postdoctoral research associate, to work on an innovative project modeling the dynamics of the scientific ecosystem. This project will combine theories and methods from Computational Social Science, Statistical Inference, Dynamical Systems, Ecology, and Evolutionary Biology to better understand the way that science and knowledge production work.

The initial term of the position is one year, with the possibility of up to two renewals, and will begin as early as January 2020 and no later than August 2020.

Ideal candidates will have a strong mathematical, statistical, and computing background; a strong track record of innovative research and publications in selective venues; and expertise in computational social science, ecology, and/or data science. The project will focus on developing new statistical and mathematical models of the causal forces that shape the structure and dynamics of the scientific workforce, spanning individual researchers and their careers, competition among departmental units, and the evolution of entire fields. Our main tools are probabilistic models, random walks, causal inference, and statistical algorithms, coupled with ideas from statistical physics, social science, and ecology. Familiarity with one or more of these techniques is desirable, but is not a requirement.

Qualifications
  • A Ph.D. (or equivalent) in Applied Mathematics, Computer Science, Statistics, or Physics, or in a quantitative branch of Ecology, Sociology, or Computational Social Science, or in a similar field, conferred no later than August 2020
  • Education or training in statistics, data analysis, and programming
  • Strong communication and presentation skills
  • A commitment to working in an interdisciplinary and collaborative environment.

For additional information, please contact Dan Larremore daniel.larremore@colorado.edu or Aaron Clauset with subject line "Science of Science Postdoc".

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