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

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

Our Company

N2IA Technologies is a consulting company specializing in acquisition/contracting support, cost/FinOps, and technology optimization for federal clients. We deliver tailored strategies, robust software solutions, and streamlined operations to help organizations achieve their goals. At N2IA, we are committed to developing innovative financial and compliance-based solutions that address evolving business and regulatory needs. If you are passionate about accounting, financial accuracy, and supporting government programs, N2IA is the place for you.

Position Summary

The 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