Data, AI & Global Risk Analytics Internship

Responsibilities

Fund Impact & Ecosystem Analytics

• Data Ecosystem Mapping: Expand our existing mapping of the crisis data ecosystem and financial‑flow analysis.
• Partner Database: Continuously update and analyze our partner‑network database, ensuring it remains current and actionable.
• Project Reporting: Consolidate and analyze historical and ongoing project data.
• Cost‑Effectiveness Analysis: Develop models to estimate the cost-effectiveness of project and analyze budget allocations across projects.
• Impact Estimation: Analyze how CRAF’d investments shape decision‑making and resource allocations; produce impact metrics for internal and external reporting.

Project Prioritization & Feasibility Assessment

• Application Process Design: Streamline and enhance the project priotization process.
• Scoring Methodology Development: Build a data‑driven, AI‑assisted framework to score prospective projects on impact potential, feasibility, and strategic fit.

Digital Tools & Reporting

• Dashboard & Framework Design: Create interactive dashboards (e.g., Power BI, Plotly) for real‑time tracking of portfolio metrics and risk indicators.
• Analytical Briefings: Prepare concise, data‑backed reports and slide decks to guide Fund Manager and Steering Committee decisions.
• Fund‑Management Tools: Co‑design and iterate on internal tools that optimize resource allocation, automate routine analyses, and ensure data integrity.

Skillset

• Be currently enrolled in a Master’s degree (or higher), OR in the final year of a Bachelor’s degree in one of the following fields: Computer Science, AI / ML, Data Science, Statistics, Economics, Public Policy, Political Science, International Affairs, Development Studies, Business, or a related field. OR have already graduated with a university degree and, if selected, must start the internship within one year of graduation.
• Programming & Scripting: Proficiency in Python and R for data cleaning, processing, statistical analysis, and modeling. Experience with version control using Git and collaboration via GitHub.
• Analytics & Visualization: Skilled in exploring, visualizing, and interpreting complex datasets using tools like ggplot2, matplotlib, Datawrapper, and Plotly; proficient in building interactive dashboards with Power BI or similar software.
• APIs & Integration: Familiarity with RESTful APIs for data ingestion and integration across systems.
• Data Management: Experience working with big data and relational (SQL) or graph databases (e.g., Neo4j). Familiar with common data standards and best practices for reproducible, transparent and ethical data handling.
• Machine Learning & AI: Understanding of core machine-learning techniques, experience training and evaluating models, and prompt engineering.
• Proficient in written and spoken English.