Job Title:
Social Sciences Subject Matter Expert AI Training & Content Development
About Innodata:
Innodata (NASDAQ: INOD) is a global leader in data engineering and AI-driven technology solutions, serving 2,000+ enterprise customers worldwide, including 4 of the top 5 global technology companies. With operations in 13 cities and a global workforce of over 5,000 professionals spanning the United States, Canada, the UK, the Philippines, India, Sri Lanka, Israel, and Germany, Innodata is at the forefront of artificial intelligence (AI), natural language processing (NLP), and large language model (LLM) innovation.
By combining advanced ML/AI technologies, cross-disciplinary subject expertise, and high-security infrastructure, Innodata is helping shape the future of responsible, context-aware AI.
About the Role:
We are seeking Subject Matter Experts (SMEs) in Social Sciences to support the training, evaluation, and development of AI/LLM systems. This role is ideal for professionals with expertise in sociology, anthropology, political science, economics, history, communications, or cultural studies.
You will directly contribute to curating, validating, and developing high-quality social science content to improve AIs ability to reason about societal structures, human behavior, governance, and cultural dynamics.
Key Responsibilities:
Content Development & Knowledge Curation:
- Create domain-rich datasets in:
- Sociology (social stratification, inequality, migration, urbanization)
- Political Science & International Relations (governance, policy analysis, diplomacy, geopolitics)
- Anthropology (cultural systems, traditions, ethnography, cross-cultural interactions)
- Economics (microeconomics, macroeconomics, development studies)
- History & Cultural Studies (historical events, cultural identity, globalization)
- Psychology (Social & Behavioral) (group dynamics, communication, human interaction)
- Produce accurate, pedagogical content such as:
- Theoretical summaries and frameworks
- Case studies and cultural analysis
- Research abstracts, literature reviews, and academic writing
- Data-driven policy and governance scenarios
AI Evaluation & Model Improvement:
- Evaluate AI-generated outputs for factual accuracy, neutrality, cultural sensitivity, and bias reduction.
- Identify errors, incomplete reasoning, or harmful stereotypes in AI responses.
- Contribute to developing bias mitigation strategies for LLM training datasets.
Cross-Functional Collaboration:
- Collaborate with AI engineers, computational linguists, and ethicists.
- Provide domain-specific input to improve AI reasoning across social systems, governance, ethics, and global cultures.
- Participate in research and development initiatives on AI fairness, responsible AI, and socio-cultural adaptation.
Eligibility Criteria:
- Advanced degree required: Masters, PhD, or equivalent in Sociology, Political Science, Anthropology, Economics, History, Psychology, or other Social Sciences.
- Strong expertise in social theory, research methods (qualitative and/or quantitative), and applied social sciences.
- Proven experience in academic research, policy writing, social science teaching, or technical/scientific communication.
- Preferred knowledge of:
- Social data analysis, econometrics, or survey methodologies.
- Cross-cultural studies, global governance, or ethics in technology.
- AI fairness, bias detection, or computational social science.
Additional Information:
- Selected candidates must complete an English Language Assessment.
- Mandatory and non-billable step.
Would you like me to also split this into sub-specializations (e.g., Political Science SME, Sociology SME, Anthropology SME)
