Operations Research / Systems Analyst (ORSA) - Modeling and Simulation (5403)

SMX

2h•$123,000 - $206,000

About The Position

The Operations Research (OR) Analyst - 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.

Requirements

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
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)

Nice To Haves

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

Responsibilities

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
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
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
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