Note: The job is a remote job and is open to candidates in USA. SMX is a team of technical and domain experts dedicated to enabling mission success. The Operations Research Analyst - Modeling & Simulation Focus provides advanced process modeling and predictive analytics to enhance decision support in various operations within a Federal Agency.

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

Skills

• 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)
• 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

Benefits

• Health insurance
• Paid leave
• Retirement
• Learning & development opportunities

Company Overview