Data Scientist

Volunteer Role: Data Scientist (Knowledge Graphs, Intelligence & Visualization)

Who We Are

Unruled Masses (UM) is a 501(c)(3) nonprofit public-interest intelligence organization focused on exposing corruption, abuse of power, and systemic exploitation — while helping communities peacefully reclaim agency, accountability, and civic life. We are looking for people who wish to stand up for the cause of democracy.

We combine rigorous intelligence tradecraft with public-facing analysis, community education, and real-world civic action. Our work is published globally through reports, newsletters, social media, and webcasts. But our mission goes beyond exposure: we aim to help people reconnect, organize, and act together — grounded in facts, not fear.

We serve as a support network for those suffering under captured institutions and markets focused on extraction rather than the delivery of public value.

Who You Are

You are a highly motivated and independent volunteer seeking to apply data science expertise — qualitative and technical — to real-world corruption intelligence. You understand that behind every data point is a story of systemic abuse, and you are disciplined in ensuring that information is processed with the highest level of accuracy and ethical integrity.

You may come from a background in data science, computer science, social and behavioral sciences, or human factors engineering. You are comfortable working with unstructured, large-scale datasets and enjoy the challenge of architecting the data models that make complex relationships — between people, organizations, transactions, and events — legible and analyzable. You're just as interested in how corruption networks can be represented as interconnected entities and edges as you are in the statistical analysis and visualization that surfaces patterns within them.

Most importantly, you believe that credible, validated, well-modeled data is the essential "fuel" for nonviolent civic pressure and democratic renewal.

What You'll Do

As a Data Scientist (Knowledge Graphs, Intelligence & Visualization), you will help architect the data backbone of UM's corruption intelligence work — from the graph models and schemas that structure it, to the database it lives in, to the analytics and visualizations that make it usable. Specifically, you will:

• Data Architecture & Knowledge Graphs: Help design and refine the data models, schemas, and knowledge graph structures that underlie our corruption intelligence database — representing entities (people, organizations, assets), relationships (ownership, influence, transactions), and the taxonomy of abuse types behind every catalogued incident.
• Data Management & Processing: Lead data management and processing planning for large, multi-source, often unstructured datasets within our corruption research library, including entity resolution and record linkage across sources.
• Data Entry & Quality Control: Perform data entry and execute rigorous quality control measures to ensure intelligence meets global standards of legitimacy.
• Data Ingestion & Pipeline Design: Help move validated, processed entries into our production database and graph store, ensuring records are complete, consistent, correctly linked, and analysis-ready.
• Standards & Protocols: Contribute to the development of standards, protocols, and schemas for data processing, entity/relationship modeling, and thematic mapping.
• Complex Analytics & Visualization: Apply network analysis, statistical modeling, and other complex analytics techniques to database records, and collaborate on the visualization tools that turn that analysis into public-facing insight — surfacing patterns, networks, and trends in how corruption occurs.
• Strategic Direction: Shape the future direction of UM's data architecture and technical platform by identifying additional data sources, evaluating graph and database technologies, and refining analytical methods.
• Geolocation & Categorization: Process data relating to corruption intelligence to help geolocate, categorize, and index real-world abuses of power.
• Cross-Team Collaboration: Collaborate with our team of intelligence officers, legal experts, and human rights strategists to ensure data — and the relationships within it — is "narrative-ready" for public consumption.

Exceptional Volunteers May

• Lead the design of the knowledge graph schema and ontology underlying UM's corruption intelligence platform
• Lead the development of Inter-Rater Reliability (IRR) protocols to ensure analytical consistency across the volunteer team
• Help shape UM's long-term data strategy and technical platform architecture, including the public-facing corruption intelligence visualization platform
• Apply graph analytics (e.g., network centrality, community detection, link prediction) to surface hidden relationships in corruption networks
• Automate data pre-processing and ingestion workflows using R or Python
• Mentor newer data volunteers on data modeling, qualitative coding, and intelligence standards

Ideal Qualifications

• Experience: Background in data science, computer science, statistics, or a related quantitative field; backgrounds in social/behavioral sciences or human factors engineering with strong technical data skills are also welcome.
• Data Architecture: Strong grounding in data modeling — comfortable designing relational schemas as well as graph/network data models (nodes, edges, properties) for representing entities, relationships, and events.
• Knowledge Graphs: Familiarity with knowledge graph concepts and technologies (e.g., Neo4j, RDF/SPARQL, property graphs) and how they're used to model complex, interconnected data.
• Analytics: Experience with complex data analytics techniques — statistical analysis, network/graph analysis, and mixed-methods approaches combining quantitative and qualitative data.
• Methodology: Experience with qualitative data collection and processing (thematic mapping, discourse analysis) is a strong plus.
• Technical: Experience with Inter-Rater Reliability (IRR) measures is a plus.
• Databases: Familiarity with SQL/relational databases (e.g., PostgreSQL) and graph databases for data ingestion and management.
• Visualization: Experience with data visualization tools or libraries (e.g., Tableau, Python/R visualization libraries, D3, Leaflet/Mapbox, or graph visualization tools like Gephi/Cytoscape) is a strong plus.
• Would Be Nice: Proficiency in Python (preferred) or R/RStudio for data management, modeling, and pre-processing.
• Technical Skills: Familiarity with working with APIs, developing coding protocols, and web scraping.
• Values: Demonstrated interest in accountability, civil rights, and nonviolent civic action.

Compensation & Commitment

This is an unpaid volunteer role. We are flexible on time commitments.

Benefits of Volunteering with Unruled Masses

• Build the Platform: Architect the data models, knowledge graph, ingestion pipeline, and visualization tools for a flagship intelligence initiative.
• Skill Advancement: Enhance skills in data modeling, knowledge graph design, and complex analytics, while also strengthening qualitative data processing, web scraping, API integration, and visualization.
• Intellectual Community: Engage in group discussions and brainstorming with a diverse team of military, intelligence, and human rights professionals.
• Real-World Impact: See your work directly support public research and the Action Playbook, turning raw data into tools for community mobilization.
• Founding Team: Be a part of building the permanent infrastructure for nonviolent civic strength from the ground up.