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How much do computational social scientist jobs pay per year?

As of Sep 1, 2026, the average yearly pay for computational social scientist in Indiana is $105,950.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,900.00 and $130,400.00 per year, depending on experience, location, and employer.

What is a computational social scientist?

A Computational Social Scientist applies computational methods, data analysis, and social science theories to study human behavior, societal trends, and complex social systems. They use techniques like machine learning, network analysis, and simulations to analyze large-scale data from sources such as social media, surveys, and public records. Their work helps inform policy, business decisions, and academic research by uncovering patterns and insights in social data.

What does a computational social scientist do?

A typical workday for a Computational Social Scientist often involves gathering and processing large datasets, running quantitative analyses or simulations, and interpreting the results to address social science questions. You might collaborate closely with other researchers, data analysts, and domain experts, as well as presenting findings to non-technical stakeholders. Responsibilities can also include writing research reports, developing new computational models, and staying current with methodological advancements. The pace and focus may vary depending on the stage of a project, but the role offers a dynamic blend of independent research and collaborative teamwork.

What are the key skills and qualifications needed to thrive as a computational social scientist?

To thrive as a Computational Social Scientist, you need a strong background in social science, statistical analysis, and computational methods, typically supported by an advanced degree such as a master's or Ph.D. in a related field. Proficiency with programming languages like Python or R, experience with data analysis platforms, and familiarity with database management systems are essential. Strong communication, problem-solving, and teamwork skills set candidates apart, especially when translating complex findings for diverse audiences. These competencies are critical for extracting meaningful insights from large datasets and effectively collaborating in interdisciplinary research environments.

What are the most commonly searched types of Computational Social Scientist jobs in Indiana?

The most popular types of Computational Social Scientist jobs in Indiana are:

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For Computational Social Scientist jobs in Indiana, the most frequently searched job titles are:

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Computational Social Scientist jobs near you
Infographic showing various Computational Social Scientist job openings in Indiana as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $105,950 per year, or $50.9 per hour.

Assistant Research Professor, Lucy Family Institute for Data & Society

Notre Dame, IN • On-site

University of Notre Dame
Colleges, Universities, and Professional Schools • 5 - 10K employees

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Company rating: 7.4 out of 10

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Full-time

Re-posted 3 days ago

Job description

Description
The Lucy Family Institute for Data & Society at the University of Notre Dame seeks an assistant-level research professor (non tenure-track). We prefer candidates with strong expertise in advanced causal inference and computational social science methods including econometric modeling of observational data, survey design, randomized control trials and digital experiments, machine learning (ML) to construct ML-based regressors, and causal ML. The faculty member will be part of a new digital engagement for learning analytics research group within Lucy. As such, research experience with digital engagement measurement, online gaming analytics, and AI-enabled policy impact analysis will be beneficial. Given the policy-oriented learning translation analytics aspect of some of the research, ideal candidates will also have a robust track record of teaching excellence demonstrated through multiple years and sections of lead-instructor course delivery (with high teaching evaluations). The research faculty will be working closely with Professors Ahmed Abbasi (Director of the Institute), Rick Johnson (Associate Director of the Institute), and Sugana Chawla (Data Science Education Program Director).
The ideal candidate would have evidence of excellence in research and scholarship. The ideal candidate would have also demonstrated an interest in interdisciplinary work, as evidenced through projects or research publications.
Expectations
  • Help establish a research program for robust measurement and causal inference in digital settings including but not limited to online video games.
  • Publish in top venues, and/or pipeline evidence such as revise-and-resubmits at top academic journals (e.g., UTD-24, Economics, Science/Nature/PNAS, etc.);
  • Pursue interdisciplinary research by building collaborations;
  • Mentor or co-mentor graduate and undergraduate students;
  • Lead and collaborate on research grants;
  • Teach / co-teach courses on related topics;

Qualifications
  • Requires a PhD in with methodological expertise in causal inference via econometric modeling, machine learning, analysis of digital trace and survey data, and experiment design.
  • Research experience (projects, papers) related to digital engagement, online gaming telemetry data, and analysis of policy impact and implications, with a track record of experience beyond doctoral studies in the domain.
  • Strong demonstrated teaching skills.

Application Instructions
Please submit a CV, a research statement, teaching statement, and three confidential letters of recommendation via Interfolio.

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