The Operations Research Analyst with a specialization in Modeling & Simulation is responsible for delivering advanced process modeling, simulation, statistical analysis, and predictive analytics to enhance a Federal Agency's personnel vetting, industrial security, and counterintelligence operations. This role focuses on developing queueing models, discrete-event simulations, and predictive analytics to assist the agency in identifying process bottlenecks, forecasting operational outcomes, and shifting towards proactive, data-driven decision-making.
Key Responsibilities
Process Modeling & Simulation
Design and develop queueing models and discrete-event simulations to uncover inefficiencies within various operational workflows, such as personnel vetting and facility inspections.
Analyze current processes and recommend enhancements that minimize turnaround times while ensuring compliance and quality standards are maintained.
Conduct scenario analyses to assess the impacts of potential changes in processes, policies, or resource allocation.
Create simulation models that effectively capture variability, resource limitations, and policies to deliver realistic forecasts of operational performance.
Statistical & Predictive Modeling
Utilize statistical analysis and risk modeling techniques to prioritize risk assessments and optimize resource allocation.
Leverage advanced mathematical and statistical methods to detect trends and anomalies across complex data sets.
Develop predictive models that significantly improve the organization's capability to forecast workload and respond to emerging conditions.
Implement machine learning techniques for classification, anomaly detection, and pattern recognition to support counterintelligence initiatives.
Model Validation & Analysis
Validate modeling assumptions and outputs against historical data and insights from subject matter experts.
Perform sensitivity analyses to comprehend how changes in assumptions influence model outcomes.
Assess and articulate uncertainties in model predictions to stakeholders clearly.
Document methodologies, assumptions, and limitations for transparency and reproducibility purposes.
Collaboration & Communication
Collaborate closely with data engineering teams to establish data requirements and ensure their suitability for modeling initiatives.
Translate analytical outcomes into clear, data-driven recommendations that guide decision-making at both strategic and operational levels.
Present complex modeling results to diverse audiences using effective visualizations and narratives.
Engage in cross-functional team activities to uphold technical standards and share insightful knowledge.
Required Skills & Experience
A minimum of 8 years of hands-on experience in operations research, particularly focusing on queueing theory, simulation, and predictive analytics applied to real-world scenarios.
3-5 years of relevant experience within DoD or Intelligence Community sectors, particularly in areas like personnel vetting or counterintelligence.
Expertise in queueing theory and discrete-event simulation, with proven successes modeling complex processes.
Proficient with simulation software tools such as Arena, AnyLogic, or SimPy.
Strong background in statistical testing, experimental design, and time-series forecasting.
Solid programming skills in analytical languages (Python, R, or SAS) with proficiency in statistical and machine learning libraries.
Experience managing and validating predictive models to determine operational outcomes.
Adept at dealing with complex datasets, including those with missing or inconsistent information.
Ability to translate analytical findings into actionable, data-informed strategies.
Background in secure government environments and a active or attainable Secret clearance.
Desired Skills & Experience
An advanced degree in Operations Research, Applied Mathematics, Statistics, or a related quantitative field.
Understanding of NISP and clearance adjudication processes, as well as insider threat and counterintelligence frameworks.
Experience with machine learning frameworks for detecting anomalies and recognizing patterns.
Knowledge of Bayesian methods and techniques for quantifying uncertainty.
Familiarity with stochastic modeling and Monte Carlo simulations.
Experience with agent-based modeling methodologies.
Experience in model validation and verification best practices.
Proficiency with data visualization tools to present findings effectively.
A basic understanding of optimization methods and SQL/database querying skills to assist in data preparation.
Experience in feature engineering and preparing data for statistical and machine learning applications.
Application Deadline: July 31, 2026
Funding Level: Proposal
The salary range for this position is $123,000 to $206,000 USD. We prioritize investing in our people, offering competitive compensation, learning opportunities, and comprehensive benefits including health insurance and paid leave.
SMX® is an Equal Opportunity employer committed to a diverse workforce.
Selected candidates may undergo a background investigation. Please note that SMX does not sponsor new applicants for immigration-related support for this position.