Summary
The World Justice Project (WJP) is seeking a full-time Senior Data Scientist – Consultant to support its Data Analytics Unit. This position will contribute to WJP’s global research on rule of law issues by supporting data analysis and report production processes. The role involves designing and implementing machine learning models, developing AI agents, and working with large language models (LLMs) to support a range of research projects.
WJP’s global research and data team is based in Washington, DC, and this position will be remote.
Responsibilities
- Design and implement machine learning models and statistical analyses to support WJP research initiatives
- Lead technical infrastructure modernization including containerization and cloud deployment strategies
- Develop and maintain agentic AI systems and LLM-powered tools for data analysis workflows
- Provide technical mentorship to other team members on advanced analytics techniques
- Collaborate with research teams to translate complex data questions into analytical solutions
Qualifications
Technical Skills
- Advanced proficiency in Python and R for statistical analysis and machine learning
- Experience with LLMOps and deployment of large language model applications
- SQL for database management and complex queries (bonus)
- Git/GitHub for version control and collaborative development
- Docker and containerization technologies
- Understanding of cloud computing concepts (AWS, Azure, or GCP)
- Basic HTML/CSS for report customization
- Understanding of RESTful APIs and web scraping techniques
Preferred Qualifications
- Strong experience with scikit-learn and other ML frameworks
- Knowledge of agentic systems and AI workflow automation
- Strong statistical background including dimensionality reduction techniques (PCA, factor analysis, MDS, t-SNE)
- Expertise in data imputation methods and handling missing data
- Experience with latent variable modeling (SEM, IRT, mixture models)
- Master's degree or PhD in Computer Science, Statistics, Data Science, or related field