AI/ML Engineer - Clearance Required

LMI Government Consulting; Tysons (VA); Remote; USD 110,986 - 195,154; Full time

Job summary

A federal solutions provider is seeking an experienced AI/ML Engineer (Data Scientist) to support the U.S. Army’s Holistic Health initiative. This role emphasizes applied analytics and collaboration with teams to develop models that assist with readiness assessments and performance analysis. Candidates should hold a Bachelor's degree in a related field and have experience with statistical analysis or machine learning. This position offers a flexible working arrangement with potential remote options.

Qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, or Applied Mathematics.
• Experience applying statistical analysis or machine learning to real-world data.
• Ability to obtain and maintain a Secret security clearance.

Responsibilities

• Develop and implement statistical models for readiness assessment.
• Collaborate with multidisciplinary teams on analytic outputs.
• Support integration of analytic outputs into user-facing tools.

Skills

Statistical analysis

Machine learning techniques

Data preparation

Feature engineering

Analytical reasoning

Collaborative teamwork

Communication skills

Education

Bachelor’s degree in Data Science or related field

Job description

Overview

LMI seeks an experienced AI/ML Engineer (Data Scientist) to support the U.S. Army’s Holistic Health & Fitness (H2F) initiative as a member facilitatively within the Analytics functional team under 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 impactlide اخیر 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. Headquarters in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Responsibilities

• Develop and implement statistical and machine learning models supporting readiness assessment, performance analysis, and injury‑risk awareness calculator.
• 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.

Qualifications

Required Qualifications

• 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.

Desired Qualifications

• 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.

Location & Travel

• Duty Location: This position may be performed remotely.
• Travel: Limited travel to Fort Eustis, Virginia or LMI Headquarters may be required to support planning, integration, or stakeholder engagement.

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.