Applied Statistician, Forecasting & Simulation

Applied Statistician, Forecasting & Simulation

LMI is seeking an Applied Statistician, Forecasting & Simulation to support passport demand forecasting for the Department of State, Bureau of Consular Affairs, Passport Services.

This role is focused on applied statistics, forecasting, predictive modeling, econometrics, and simulation. The successful candidate will develop, evaluate, and refine statistical models used to forecast passport demand and help government stakeholders understand demand drivers, uncertainty, and potential future scenarios.

The ideal candidate has a strong statistical foundation, experience with time series and forecasting methods, and the ability to critically evaluate model assumptions, performance, and limitations.

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.

Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors-helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

• Develop and maintain statistical, econometric, and time series models to forecast passport demand.
• Apply forecasting methods such as regression, ARIMA, exponential smoothing, state space models, Bayesian methods, and other appropriate techniques.
• Develop simulations and scenario analyses to evaluate uncertainty and potential future outcomes.
• Perform model validation, backtesting, sensitivity analysis, residual analysis, and forecast error evaluation.
• Analyze historical demand, survey data, economic indicators, demographics, travel trends, policy changes, and other potential demand drivers.
• Evaluate model assumptions, data quality, structural changes, bias, and sources of uncertainty.
• Compare alternative modeling approaches and recommend methods based on accuracy, interpretability, and statistical rigor.
• Monitor actual demand against forecasts and identify opportunities to improve model performance.
• Communicate methodology, assumptions, uncertainty, and analytical findings to technical and nontechnical stakeholders.
• Maintain reproducible analytical workflows, model documentation, and technical artifacts.

• Bachelor's degree in Statistics, Econometrics, Economics, Applied Mathematics, Operations Research, or another highly quantitative discipline.
• Ability to operate efficiently using new and emerging tools of quantitative analysis.
• Experience in statistical analysis, forecasting, econometrics, predictive modeling, or related quantitative analysis.
• Strong knowledge of regression, probability, statistical inference, time series analysis, model validation, and uncertainty.
• Experience developing and evaluating forecasting models using ARIMA, regression-based forecasting, exponential smoothing, state space models, or comparable methods.
• Experience with simulation, scenario analysis, or probabilistic modeling.
• Experience validating models through backtesting, sensitivity analysis, residual analysis, or out-of-sample testing.
• Proficiency with Python, R, Stata, SAS, MATLAB, or comparable statistical tools.
• Strong analytical, problem-solving, and communication skills.
• Ability to independently structure and solve complex quantitative problems.

• Master's degree or PhD in Statistics, Econometrics, Economics, Applied Mathematics, Operations Research, or a related discipline.
• Experience with demand, economic, population, workload, or other longitudinal forecasting.
• Experience with Bayesian methods, Monte Carlo simulation, multivariate time series, or advanced econometric techniques.
• Experience analyzing survey data, including sampling, weighting, representativeness, and sampling error.
• Experience with Python statistical libraries, Stata, or R.
• Experience working with economic, demographic, travel, public policy, or government administrative data.
• Experience developing or evaluating simulation models and scenario-based forecasts.
• Experience supporting federal government customers.

The salary range displayed…