About this role
Complex Adaptive Systems:
Clearance: TS with SCI eligibility. Active SAP eligibility preferred.
Location: Arlington, VA
Salary: Based on experience
The successful candidate will serve as a Quantitative Analyst SETA supporting a government program managers in the development, integration, and scaling of complex adaptive system modelling and data-intensive analytical capabilities. This role involves integrating complex, multi-source intelligence and commercial/open-source data streams into graph-based, network analytical frameworks to support strategic competition analysis, industrial base resilience, and national security decision-making.
Requirements:
Clearance: TS with SCI eligibility. Active SAP eligibility preferred.
Location: Arlington, VA
Salary: Based on experience
The successful candidate will serve as a Quantitative Analyst SETA supporting a government program managers in the development, integration, and scaling of complex adaptive system modelling and data-intensive analytical capabilities. This role involves integrating complex, multi-source intelligence and commercial/open-source data streams into graph-based, network analytical frameworks to support strategic competition analysis, industrial base resilience, and national security decision-making.
Requirements:
- Master’s degree or PhD in Economics, Data Science, Applied Mathematics, or a related quantitative field
- 5+ years of relevant experience in quantitative economic modeling, advanced econometrics, and network data science/data engineering applied to complex adaptive systems
- Demonstrated experience in large-scale data processing, multi-source data pipeline integration, and managing structured/unstructured data architectures
- Practical experience with graph analytics, network modeling tools, and interconnected data architectures (e.g., Python/R quantitative libraries, graph databases, or network science frameworks)
- Strong background working within or alongside the U.S. Intelligence Community (IC), including familiarity with IC mission environments, data workflows, and intelligence-derived datasets
- Understanding of diverse data sources across global economics, financial networks, trade flows, defense industrial supply chains, and multi-INT sources
- Strong communication skills—both written (including executive PowerPoint briefs) and oral—with the ability to translate complex econometric and data models for senior defense stakeholders
- Active Special Access Program (SAP) access and experience working at TS/SCI and SAP levels
- Experience with defense industrial base analysis, economic statecraft, strategic competition modeling, or macroeconomic resilience metrics
- Hands-on experience building decision-support tools, AI/ML-enabled analytical tools, cloud data engineering workflows, or large language model (LLM) research pipelines
- Prior background supporting new program start-ups, pilot demonstrations, and transition pathways within defense or intelligence organizations
- Experience in high paced private sector quantitative production environment such as systematic investing or trading, real time ad technology development or pharmacological optimization and customization
- Demonstrated ability to bridge communication and technical execution across academic economists, data engineers, software developers, and operational intelligence analysts
- Oversee the design, evaluation, and application of quantitative economic models, network analysis frameworks, and graph-based data integration architectures
- Oversee the integration of complex, multi-source intelligence and economic datasets into scalable, operational analytical pipelines
- Serve as primary technical liaison between academic researchers, software engineering teams, and IC stakeholders to ensure tools align with operational requirements
- Advise leadership on program execution risks, data architecture scalability, and capability transition strategy
- Prepare technical documentation, program roadmaps, and executive briefs to communicate program progress and analytical findings to senior decision-makers
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