Research Analyst - Operations Research and Military Campaign Analysis

Research Analyst

IDA has an immediate opening for a Research Analyst with strong capabilities in operations research and systems analysis. The analyst will formulate and investigate complex national security problems using mathematical modeling, simulation, statistical analysis, experimental design, optimization, and other quantitative and qualitative methods. The position applies these methods to campaign- and mission-level analyses of military operations, force structure, operational concepts, weapons systems, tactics, and risk.

Experience with military campaign models is valuable, but demonstrated analytical ability, intellectual curiosity, and the capacity to learn new tools are essential.

Specific research-related activities expected of this position include:

• Formulate complex defense problems. Work with sponsors, military subject-matter experts, modelers, and other analysts to translate operational questions into clearly defined research questions, hypotheses, assumptions, measures, and analytical approaches.
• Develop rigorous analytical methodologies. Apply operations research and systems analysis methods—including simulation, statistical analysis, optimization, decision analysis, experimental design, and uncertainty analysis—to evaluate military operations and systems.
• Design and execute statistically rigorous computational experiments. Develop scenarios, factors, response variables, experimental designs, and simulation run plans that efficiently explore operational and system trade spaces.
• Conduct campaign and mission analysis. Use Synthetic Theater Operations Research Model (STORM) and other models and analytical tools to assess operational concepts, force structures, weapons systems, tactics, operational plans, and risks.
• Analyze and interpret complex data. Process large simulation datasets, identify drivers of outcomes, distinguish meaningful effects from model artifacts, evaluate sensitivity to assumptions, and quantify uncertainty where appropriate.
• Assess model credibility. Examine model assumptions, limitations, data quality, verification and validation evidence, and the applicability of model results to the sponsor's decision.
• Develop decision-relevant findings. Integrate quantitative results, operational context, and subject-matter expertise into defensible conclusions and recommendations.
• Communicate research effectively. Produce clear, technically rigorous reports, briefings, visualizations, and presentations for both technical and senior decision-making audiences.
• Contribute to research quality. Review analytical methodologies and research products developed by other team members and help improve the division's analytical tools and practices.

Required Qualifications:

• Bachelor's degree in Operations Research, Applied Mathematics, Statistics, Industrial Engineering, Systems Engineering, Computer Science, Economics, Physics, Engineering, Data Science, or another quantitatively rigorous field, with 3+ years of relevant experience. An advanced degree may substitute for a portion of the experience requirement.
• Demonstrated ability to formulate ambiguous or complex problems, identify important assumptions and decision criteria, and develop defensible analytical approaches.
• Strong foundation in one or more operations research or systems analysis methods, such as simulation, optimization, statistical inference, design of experiments, decision analysis, uncertainty analysis, stochastic modeling, or trade-space analysis.
• Experience applying quantitative methods to real-world research, policy, engineering, operational, or decision-support problems.
• Proficiency in at least one analytical programming language, such as Python, R, MATLAB, Julia, or a comparable language, and the ability to learn additional tools as needed.
• Ability to evaluate data quality, model assumptions, analytical limitations, and the robustness of findings.
• Demonstrated ability to communicate analytical methods, results, uncertainty, and implications to both technical and nontechnical audiences.
• Ability to work independently and collaboratively on interdisciplinary research teams and to manage multiple…