Operations Research / Systems Analyst (ORSA) - Modeling & Simulation FocusPosition SummaryThe Operations Research System Analyst (ORSA) - Modeling & Simulation Focus provides advanced process modeling, simulation, statistical analysis, and predictive analytics across a Federal Agency's personnel vetting, industrial security, and counterintelligence operations. This position develops queueing models, discrete-event simulations, and predictive models that enable the Federal Agency to identify process bottlenecks, forecast operational outcomes, and transition from reactive reporting toward proactive, data-driven decision support.
Key Responsibilities:Process Modeling & Simulation- Develop queueing models and discrete-event simulations to identify bottlenecks and inefficiencies within operational pipelines (personnel vetting, facility inspections, investigative workflows).
- Analyze and recommend process improvements that reduce turnaround times while maintaining required quality and compliance standards.
- Conduct scenario analysis and what-if modeling to evaluate the impact of proposed process changes, policy modifications, or resource reallocations.
- Build simulation models that capture stochastic variation, resource constraints, and operational policies to provide realistic operational forecasts.
Statistical & Predictive Modeling- Apply statistical analysis and risk modeling to prioritize assessments, optimize resource deployment, and identify emerging risk or threat vectors.
- Utilize advanced mathematical and statistical modeling to detect anomalies, patterns, and trends within large, complex, and disparate data sets.
- Develop predictive models that enhance the organization's ability to forecast workload, prioritize cases, and respond to emerging conditions and threats.
- Apply machine learning techniques for classification, clustering, anomaly detection, and pattern recognition in support of counterintelligence and insider threat missions.
Model Validation & Analysis- Validate model assumptions and outputs against historical operational data and subject matter expert input.
- Conduct sensitivity analysis to understand model behavior under varying assumptions and parameter values.
- Quantify and communicate uncertainty in model predictions and recommendations.
- Document modeling methodologies, assumptions, and limitations to ensure transparency and reproducibility.
Collaboration & Communication- Work closely with data engineering specialists to define analytical dataset requirements and ensure data suitability for modeling.
- Translate analytical outputs into objective, data-driven recommendations that support strategic and operational decision-making.
- Present complex modeling results to technical and non-technical audiences through visualizations and clear narratives.
- Participate in cross-functional team activities to maintain technical standards and share knowledge.
Required Qualifications- 8+ years of progressive, hands-on operations research experience, including demonstrated application of queueing theory, simulation, statistical modeling, and predictive analytics to real-world operational problems.
- 3-5 years of that experience supporting DoD or Intelligence Community mission areas such as personnel vetting, industrial security (NISP), counterintelligence, or insider threat.
- Bachelor's degree in operations research, Applied Mathematics, Statistics, Industrial Engineering, or a related quantitative discipline.
- Expert-level knowledge of queueing theory and discrete-event simulation, with demonstrated ability to model complex operational processes.
- Hands-on experience with simulation tools (Arena, AnyLogic, SimPy, or similar).
- Strong foundation in statistical modeling, hypothesis testing, experimental design, and time-series analysis.
- Demonstrated, hands-on proficiency in an analytical programming language (Python, R, or SAS), including statistical and machine learning libraries.
- Proven ability to build and validate predictive models that forecast operational outcomes.
- Experience working with complex, messy real-world datasets (missing data, inconsistent formats, temporal misalignment).
- Ability to translate analytical findings into objective, data-backed recommendations for strategic decision-making.
- Experience working in secure (classified) government environments.
- Secret clearance required (active or ability to obtain).
Preferred Qualifications- Advanced degree in Operations Research, Applied Mathematics, Statistics, Industrial Engineering, or a related quantitative discipline.
- Familiarity with NISP, clearance adjudication processes, and/or insider threat/counterintelligence analytic frameworks.
- Experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch) for anomaly detection and pattern recognition.
- Knowledge of Bayesian statistical methods and uncertainty quantification.
- Experience with Monte Carlo simulation and stochastic modeling.
- Familiarity with agent-based modeling.
- Model validation and verification methodologies (V&V best practices).
- Data visualization tools (Tableau, Power BI, matplotlib, seaborn).
- Knowledge of optimization methods (linear programming, heuristics) to better integrate with optimization specialists.
- SQL and database querying skills to support data preparation for modeling.
- Experience with feature engineering and data preparation for statistical and machine learning models
