Data Scientist

We are seeking a Senior-level Data Scientist with a strong background in statistical and econometric analysis to support our passport demand forecasting activities with the Department of State Passport Bureau (CA/PPT). This role will play a critical part in developing, testing, and refining models used to forecast multiple forms of passport demand across the organization. The ideal candidate combines rigorous analytical thinking with creativity, technical curiosity, and sound judgment.

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.

• Design, develop, evaluate, and actively monitor statistical and time series models (including econometric approaches) for accurately forecasting demand 23-36 months into the future.
• Rapidly prototype and iterate on new models, methodologies, and analytical libraries that rely on cutting edge statistical and other tools. Assess their strengths and limitations.
• Critically evaluate data sources, modeling assumptions, and results, identifying risks, biases, and sources of uncertainty.
• Actively monitor US passport demand; survey results; policy developments; travel, economic, geopolitical, and other trends; and creatively explore means of using these data to forecast impacts on passport demand.
• Translate complex analytical findings into clear, actionable insights for technical and non-technical stakeholders.
• Work independently to explore novel approaches while applying healthy skepticism and methodological discipline.
• Stay current with emerging technologies, tools, and best practices in forecasting, statistics, and data science.
• Explore customer needs and help identify ways to add value.

Required Qualifications

• Advanced degree (PhD or Masters) from an accredited college or university in a data-related field of study.
• Ability to communicate well with senior leadership, peers, and staff at all levels to facilitate excellent teamwork.
• Advanced experience in statistical analysis and time series modeling, including econometric techniques, seasonal regression, and other statistical models.
• Demonstrated ability to think critically about models, data, and assumptions rather than relying on off-the-shelf solutions.
• Strong problem-solving skills and comfort working in ambiguous or evolving problem spaces.
• Ability to learn new tools, frameworks, and technologies quickly and apply them effectively.
• Proven track record of independent work and ownership of analytical projects.

Desired Attributes

• Creative and innovative mindset, balanced by risk awareness and analytical rigor.
• Willingness to challenge existing approaches and push boundaries while maintaining methodological soundness.
• Curiosity-driven learner with a pragmatic approach to adopting new technologies.
• Comfortable iterating quickly, testing ideas, and discarding approaches that do not meet standards.
• Experience in Python statistics and machine learning framework and libraries, including statsmodels, pandas, numpy, xgboost, scipy, matplotlib.
• Experience working with STATA.
• Experience applying statistical methods (time series analysis) including autoregressive integrated moving average (ARIMA) and vector autoregression (VAR).
• Experience applying machine-learning models to time series analysis in data-poor environments.
• Experience applying Bayesian statistical models to time series analysis.
• Experience leveraging cloud environments for data science tasks, specifically using serverless technologies in the AWS cloud.

The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.   

The salary range for this position is $90,000-$150,000