AI/ML Engineer - Clearance Required

Logistics Management Institute

29d•$110,986 - $195,154•Remote

About The Position

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.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related field.
Demonstrated experience applying statistical analysis or machine learning techniques to real-world data problems.
Familiarity with data preparation, feature engineering, and model evaluation concepts.
Experience working with structured and semi-structured data.
Ability to collaborate effectively within multidisciplinary teams spanning analytics, research, and software development.
Strong analytical reasoning and communication skills.
Ability to obtain and maintain a Secret security clearance.

Nice To Haves

Experience supporting applied analytics in health, human performance, readiness, or research-driven environments.
Familiarity with integrating analytic outputs into dashboards or operational systems.
Experience working alongside data engineering or software development teams.
Prior experience supporting DoW or federal customers.

Responsibilities

Develop and implement statistical and machine learning models supporting readiness assessment, performance analysis, and injury-risk awareness.
Apply appropriate analytic methods based on data characteristics, scientific guidance, and Government priorities.
Ensure analytic approaches are transparent, interpretable, and suitable for operational decision support.
Support validation, testing, and refinement of analytic models under Government and senior scientific direction.
Assess model performance, limitations, assumptions, and potential bias.
Collaborate with epidemiologists and research psychologists to ensure analytic outputs align with scientific intent.
Work with data engineers to support feature development, data preparation, and analytic dataset construction.
Assist in identifying data quality issues that may impact model performance.
Ensure analytic inputs align with approved data definitions and governance standards.
Collaborate with software teams to support integration of analytic outputs into dashboards, reports, and user-facing decision-support tools.
Support development of repeatable analytic workflows and documentation.
Assist in troubleshooting analytic issues in coordination with data and software teams.
Contribute to documentation of analytic methods, assumptions, limitations, and results.
Support preparation of analytic summaries, briefings, and materials for Government stakeholders.
Clearly communicate analytic findings in a manner appropriate for non-technical audiences.