Computational Social Science Intern (Summer 2026)

Protagonist is a company that specializes in data-driven communication strategies and innovative solutions. As a Computational Social Science Intern, you will apply data science methodologies to real-world challenges, assisting in data collection, analysis, and visualization to generate actionable insights for clients.

Responsibilities:

• Collect and curate data from a variety of media content aggregators, open-source platforms, and APIs using scripts, scrapers, and automated tools
• Build datasets that are relevant to the customer’s project scope and need area
• Use Python, SQL, or similar languages to clean, structure, and transform datasets for analysis
• Apply machine learning and natural language processing techniques to large, unstructured text datasets to uncover meaningful patterns and insights
• Implement topic modeling, classification, clustering, and other core NLP/ML workflows aligned with customer analysis needs
• Experiment with and evaluate different models and algorithms, including both traditional statistical approaches and modern AI techniques (e.g., transformer-based models, embeddings, stance detection)
• Contribute to the design and refinement of analytical frameworks and research methodologies that structure how we assess complex information environments
• Help translate social science research questions into computational workflows, bridging theory and applied analysis
• Create visualizations and interactive dashboards using tools like Tableau, Superset, Power BI, or similar platforms to communicate analytic findings
• Translate complex data outputs into accessible formats that support client understanding and decision-making
• Collaborate across teams—including engineering, product, and client delivery—to ensure analytics align with broader project goals
• Contribute to client deliverables by packaging and explaining relevant data insights
• Participate in the continued development of our Narrative Analytics® platform and workflows, bringing a data-driven mindset to each step of the process
• Demonstrate professionalism, ownership, and timely execution across tasks, communication, and deliverables
• Adapt to evolving project priorities and fast-paced client timelines, while maintaining quality and clarity
• Communicate findings clearly and translate complex technical data science needs into actionable guidance for both external clients and internal team members across technical and cross-functional domains
• Collaborate with respect, openness to feedback, and a commitment to continuous learning