Operations Research / Systems Analyst (ORSA) - Modeling & Simulation Focus

N2IA Technologies•Washington, DC

5d•$165 - $195

About The Position

The Operations Research / Systems 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. The role develops queueing models, discrete-event simulations, and predictive models to help identify process bottlenecks, forecast operational outcomes, and enable proactive, data-driven decision support. The analyst models operational pipelines, evaluates potential process and policy changes, applies statistical and machine-learning methods to complex datasets, and validates and communicates modeling results. The position collaborates with data engineering specialists and cross-functional teams, translating analyses into clear recommendations for strategic and operational decisions.

Requirements

At least 8 years of progressive, hands-on operations research experience, including applying queueing theory, simulation, statistical modeling, and predictive analytics to real-world operational problems.
3–5 years of 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 the ability to model complex operational processes.
Hands-on experience with simulation tools such as Arena, AnyLogic, SimPy, or similar.
Strong foundation in statistical modeling, hypothesis testing, experimental design, and time-series analysis.
Hands-on proficiency in Python, R, or SAS, including statistical and machine-learning libraries.
Ability to build and validate predictive models that forecast operational outcomes.
Experience working with complex, messy real-world datasets, including missing data, inconsistent formats, and 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, either active or obtainable.

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 or counterintelligence analytic frameworks.
Experience with machine-learning frameworks such as scikit-learn, TensorFlow, or 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.
Knowledge of model validation and verification methodologies and best practices.
Experience with data visualization tools such as Tableau, Power BI, matplotlib, or seaborn.
Knowledge of optimization methods, including linear programming and heuristics, to support integration with optimization specialists.
SQL and database querying skills for data preparation.
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 in personnel vetting, facility inspections, and investigative workflows.
Analyze and recommend process improvements that reduce turnaround times while maintaining quality and compliance standards.
Conduct scenario analysis and what-if modeling to evaluate proposed process changes, policy modifications, and resource reallocations.
Build simulation models that represent stochastic variation, resource constraints, and operational policies to produce realistic forecasts.
Apply statistical analysis and risk modeling to prioritize assessments, optimize resource deployment, and identify emerging risks or threat vectors.
Use mathematical and statistical modeling to detect anomalies, patterns, and trends in large, complex, disparate datasets.
Develop predictive models 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 support transparency and reproducibility.
Work with data engineering specialists to define analytical dataset requirements and ensure data suitability for modeling.
Translate analytical outputs into objective, data-driven recommendations for 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.