The Data Scientist – Journeyman will support the Army's ammunition planning and resupply mission through the development, sustainment, and refinement of data-driven analytics and machine learning models. This role will work within a team to maintain and enhance ML-driven demand forecasts, support dashboard metrics in Army Vantage, and contribute to workflow automation that improves ammunition distribution efficiency and unit readiness.

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.

Responsibilities

• Support development, testing, and refinement of forecasting and analytics models for ammunition consumption and resupply.
• Maintain and enhance existing Vantage-based analytics, dashboards, and data products, including incorporating new data sources.
• Assist in integrating and validating data into Vantage pipelines to centralize munitions data.
• Conduct descriptive, diagnostic, and predictive analytics on historical ammunition consumption and unit forecast data to identify trends, anomalies, and opportunities for improvement.
• Support requirement-gathering sessions with logisticians, ammunition SMEs, and Government stakeholders to define analytical use cases, data needs, and model enhancements.
• Develop and maintain reproducible analytical workflows using Python, SQL, and related tools; document model logic, assumptions, and limitations.
• Assist in monitoring model performance and key metrics.
• Prepare clear visualizations and summary analyses to support leadership decision-making, including monthly status reports and briefings.
• Support project backlog management by creating and updating user stories and technical tasks related to data science work.

Required Qualifications

• Bachelor’s degree in data science, statistics, mathematics, computer science, engineering, business analytics, or related discipline.
• 2–5 years of experience in data science, analytics, or a closely related field.
• Demonstrated experience with:
  • Python (e.g., pandas, scikit-learn, numpy) and SQL for data preparation, analysis, and basic modeling.
  • Building and validating regression, time series, or classification models.
  • Working with large, structured datasets in relational databases or data warehouses.
• Strong quantitative and critical thinking skills; ability to translate ambiguous business questions into analytical problems and solutions.
• Ability to communicate analytical findings to non-technical stakeholders in clear, concise terms.
• Self-directed, detail-oriented, and comfortable working in an Agile environment with shifting priorities.
• Active or attainable SECRET clearance; please note, only US Citizens can obtain a clearance.

Desired Qualifications

• Experience supporting an enterprise data analytics platform (e.g., Army Vantage, Army Data Platform, or similar).
• Familiarity with Army logistics processes, ammunition management, or supply chain analytics.
• Experience with dashboarding/BI tools (e.g., Power BI, Tableau, or Vantage-native visualization tools).
• Experience with version control (e.g., Git) and Agile/Scrum practices (e.g., JIRA, Confluence).
• Exposure to ML model monitoring and MLOps concepts.

Target salary range: $130,000-$160,000

Disclaimer: 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.

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