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
Company Overview