Data Analyst

This Data Analyst position supports Operations in the Information Environment (OIE) and Psychological Operations (PSYOP) by transforming diverse intelligence and behavioral data into actionable insights for operators and decision makers. It’s not a corporate analytics role — it’s an operational, mission driven intelligence analytics role. 

Core Responsibilities (What You Actually Do) 

• Multisource data aggregation — Combine data from OSINT, SIGINT, HUMINT, CEM, social media, web metrics, surveys, and focus groups into structured datasets. 

• Operational assessments — Support Measures of Effectiveness (MOE), Measures of Performance (MOP), and intermediate objectives tied to operational plans. 

• Cloud based analytics — Use cloud platforms to access, manage, and analyze behavioral and operational datasets. 

• Advanced data transformation — Apply SQL, Python, and R for cleaning, modeling, statistical analysis, and visualization. 

• Database administration — Perform data entry, system updates, troubleshooting, and documentation aligned with Government data standards and Security Implementation Guides. 

• Tagging structures — Implement tagging for Lines of Effort (LOE) and Lines of Persuasion (LOP) to organize and retrieve mission critical data. 

• Observational evidence analysis — Process usage data and observational evidence to support operational assessments. 

• AI enabled workflows — Support AI driven analysis pipelines to enhance speed and accuracy. 

• Data taxonomy compliance — Coordinate with the Fusion Synchronization Center and Data Integration Manager to ensure consistent taxonomy. 

• Dashboard development — Build dashboards and recurring reports that provide actionable insights to operators and decision makers. 

 This position is contingent on award of contract.  

• Clearance: TS/SCI active 

• Years of Exp/Degree: 5 years with BS/BA, 3 years with MS/MA or 0 years with PhD 

• Technical stack: SQL, Python, R, cloud analytics platforms. 

• Intelligence data familiarity: OSINT, SIGINT, HUMINT, CEM. 

• Data engineering basics: ingestion, cleaning, structuring, tagging, repository maintenance. 

• Assessment frameworks: MOE, MOP, IO/PSYOP operational planning. 

• Visualization & reporting: dashboards, longitudinal datasets, trend analysis. 

• Security & compliance: Government data standards, SIGs, controlled data environments.