GeoSpatial Lead Analyst MAP - GSLANL

TekWissen•Kingston, WA

1d•Hybrid

About The Position

Translate how IMF economists work into a practical solution design and ensure the Cognitive Digital Twin produces results that are transparent, traceable, and defensible.

Requirements

3+ years delivering data, analytics, or AI solutions.
2+ years working with large language models, AI assistants, or agent-based systems.
Experience translating business processes into functional and technical specifications.
Demonstrated ability to work with researchers, economists, analysts, or other subject matter experts.
Strong facilitation and stakeholder engagement skills.
Ability to work onsite in Washington, DC using IMF-managed technology and development environments.
Python, with the ability to review and validate data-processing and AI-integration code.
Azure OpenAI and Retrieval-Augmented Generation (RAG) architectures, including grounding, retrieval strategies, and Model Context Protocol (MCP).
Azure AI Foundry evaluation capabilities and model assessment practices.
GitHub Copilot, agent specifications, instruction libraries, and AI-assisted development practices.
Claude Code and Claude Skills, including authoring and governance of instructions, skills, and behavioral guardrails.
Microsoft Fabric, OneLake, and Power BI, including architectural decision-making for enterprise data platforms.
Knowledge graph technologies, semantic modeling, ontology design, metadata management, and graph-based retrieval patterns.
Microsoft Entra ID and role-based access control.
Microsoft Purview or equivalent solutions for cataloging, lineage, governance, and auditability.

Nice To Haves

Azure API Management and Azure Monitor/Application Insights.
Responsible AI frameworks, content safety controls, and red-team testing practices.
Experience designing enterprise knowledge graphs and semantic layers for search, discovery, recommendation, and AI grounding.
Experience with Google Cloud AI services, including Vertex AI, Gemini, Agent Development Kit (ADK), and BigQuery, demonstrating cross-platform understanding of enterprise AI architectures.
Data Modeling

Responsibilities

Work directly with SPR economists to understand how survey analysis is performed today and identify which activities can be automated, which require analyst review, and where AI can assist decision-making.
Document business requirements, user journeys, and solution specifications that guide engineering and AI development teams.
Define and maintain role-based prompts, agent instructions, and approved interaction patterns.
Establish and enforce evidence standards so every output can be traced back to its source survey, reporting period, methodology, and underlying calculations.
Lead the design of the project's knowledge graph and semantic model, ensuring relationships between surveys, indicators, countries, methodologies, institutions, and analytical concepts are consistently represented and reusable across agents.
Define business use cases for graph-based reasoning, including cross-country comparisons, indicator lineage, concept discovery, policy-link analysis, and expert knowledge retrieval.
Own business validation criteria, benchmark scenarios, and "golden questions" used to determine whether the solution is ready to progress beyond prototyping.
Govern the lifecycle of agent assets, including instructions, skills, knowledge sources, evaluation criteria, and governance controls.
Review AI-generated code, configurations, and solution artifacts as the accountable business and architecture reviewer.
Prepare architecture, security, governance, and review materials for EARB and other approval bodies.
Partner with data architects and engineers to ensure the knowledge graph, retrieval mechanisms, and analytical models align with business expectations and governance requirements.