LMI seeks an experienced AI/ML Engineer (Data Scientist) to support the U.S. Army’s Holistic Health & Fitness (H2F) initiative as a member of the Analytics functional team within the H2F Program Support Team.
The AI/ML Engineer (Data Scientist) is responsible for developing, validating, and operationalizing analytic models, statistical methods, and machine learning approaches that support readiness assessment, injury-risk analysis, and user engagement insights within the Holistic Health and Fitness Management System (H2FMS). This role focuses on applied analytics and model implementation, not independent analytic strategy or policy-setting.
The AI/ML Engineer works closely with the Technical Project Manager, data engineers, data governance specialists, epidemiologists, research psychologists, tactical sports scientists, and software teams to translate Government-directed analytic requirements into reproducible, interpretable, and scalable analytic solutions.
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.
Required Qualifications
Desired Qualifications
Location & Travel
Target salary range: $110,986 - $195,154
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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