Senior Research Associate, Research Data Analytics

Virginia Tech • Arlington, VA

1d

About The Position

Based on the foundation of the Ted and Karyn Hume Center for National Security and Technology, Virginia Tech launched the Virginia Tech National Security Institute in September 2021. With a presence in Blacksburg and the Washington, D.C., metro area, the institute aspires to be the nation's preeminent academic organization at the nexus of interdisciplinary research, technology, policy, and talent development, the national security institute will advance national security in pursuit of a secure America. Building on the foundation of Virginia Tech and VTNSI’s interdisciplinary mission, the Senior Research Associate, Operations Research & Data Analytics is technical research leader, responsible for executing and integrating data analyses and applied research that cuts across technical areas and mission domains relevant to national security stakeholders. The role is designed for a candidate who can span “data science” and “operations research” disciplines: structuring messy, multi-sourced information into analysis-ready data; applying rigorous quantitative methods (e.g., statistical modeling, optimization, simulation, scenario analysis); and delivering sponsor-trusted decision support artifacts (briefings, dashboards, repeatable analytic workflows). The position emphasizes cross-domain integration, analytic integrity, and stakeholder communication – ensuring that analytic outputs are not only technically sound, but also usable and traceable in real-world decision environments. The Operations & Data Analyst position is well-suited for individuals with strong technical and cross-domain backgrounds who excel at translating mission needs into high-impact sponsored research efforts, and are looking for opportunities to grow enduring national security partnerships at a leading national security institute.

Requirements

• PhD degree in operations research, data science, computer science, statistics, mathematics, or a closely related discipline with significant experience. An advanced degree may be considered in lieu of the doctorate.
• Demonstrated experience applying quantitative methods to real-world decision problems, including at least two of: optimization, simulation, statistical modeling / inference, scenario analysis, or machine learning.
• Proficiency in Python and SQL (or equivalent) for data wrangling, analysis, and reproducible workflows.
• Experience integrating multiple models, data sources, and/or analytic methods into coherent cross-domain assessments.
• Experience planning and executing sponsored research projects.
• Strong communications and stakeholder engagement skills.
• Work collaboratively in interdisciplinary research environments that span technical, operational, and policy domains.
• U.S. citizenship and an active secret clearance with the ability to maintain that clearance.

Nice To Haves

• Proficiency in synthesizing insights across events or studies, to include wargames, exercises, experiments, modeling & simulation, operational assessments.
• Experience with optimization solvers, simulation environments, or building decision-support tools that are reused across teams and efforts.
• Familiarity with data architectures that integrate distributed artifacts into searchable, reusable, decision-relevant enterprise knowledge.
• Knowledge of governance and risk-management practices to integrated data management, to include considerations of AI-enabled analytics.
• Experience serving as PI or co-PI on externally sponsored research programs.

Responsibilities

• Integrate data analyses and applied research by translating sponsor questions into analytic problem statements with clear objectives, constraints, assumptions, and success measures; developing and validating quantitative analyses using operations research and data science methods; and producing defensible tradeoff narratives and recommendations.
• Build and evolve mission-aligned data and knowledge structures by collecting, curating, and structuring multi-source artifacts into analysis-ready datasets; developing data models/metadata/tagging approaches to enable reuse and cross-study synthesis; and supporting creation of “searchable, decision-relevant” analytic environments (e.g., curated repositories, repeatable pipelines, interactive analytic workspaces) that reduce reinvention and enable cumulative learning.
• Apply mixed-method quantitative techniques by applying toolkits that may include optimization, simulation, mathematical modeling, forecasting, statistical inference, and ML/NLP – paired with verification/validation practices, sensitivity analysis, and transparent uncertainty communication to maintain sponsor trust.
• Develop stakeholder-ready decision products by designing and delivering dashboards, visuals, and briefing narratives that translate complex analyses into actionable insight, and will communicate limitations, assumptions, and key drivers with clarity appropriate to decision-maker needs.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees