Data Scientist (SME) - Clearance Required

LMI seeks a Senior Data Scientist (Subject Matter Expert) to lead analytic model development, readiness scoring methodologies, and multidomain performance analytics for the U.S. Army Center for Initial Military Training's (CIMT) Holistic Health & Fitness Management System (H2FMS).

H2FMS is a secure analytics environment hosted in Army GovCloud that integrates data from the vendor-provided H2F data capture application. The Senior Data Scientist will serve as the primary expert responsible for conceptualizing, designing, validating, and implementing advanced analytic models used to assess Soldier and unit readiness across all five H2F domains: Physical, Nutritional, Mental, Sleep, and Spiritual.

This SME will work closely with Tactical Sports Scientists, Human Performance Specialists, Data Engineers, Cloud and DevSecOps personnel, UI/UX developers, and the Technical PM to ensure analytic workflows are scientifically sound, doctrinally aligned (FM 7-22), and operationally meaningful for Army stakeholders.

This role also provides analytic oversight to ensure accurate integration of vendor-provided H2F data into Army GovCloud pipelines and readiness models.

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.

Lead the design, development, and validation of advanced analytic models for multidomain Soldier readiness.
• Establish scientifically and statistically sound methodologies for:
  • Readiness scoring and risk indicators
  • Training load and fatigue modeling
  • Injury risk prediction
  • Recovery and resilience analytics
  • Sleep quality, stress, and behavioral readiness metrics
  • Nutrition and energy balance modeling
• Ensure all models align with FM 7-22, ACFT standards, and Army H2F governance.
• Provide SME-level guidance on integrating wearable and sensor-derived data (HR, HRV, GPS, accelerometry, sleep monitors).
• Define analytic requirements for transforming raw physiologic and biomechanical data into actionable readiness metrics.
• Advise Data Engineers on sampling rates, data normalization, smoothing techniques, artifact reduction, and quality checks.
• Review, validate, and document the scientific requirements for integrating vendor-provided H2F data streams into H2FMS.
• Ensure that data structures support analytic modeling, multidomain assessment, and long-term scalability.
• Serve as the SME for assessing analytic implications of changes to upstream vendor applications.
• Work directly with Data Engineers to translate scientific models into production-ready pipelines.
• Partner with the Tactical Sports Scientist to align scientific validity with operational relevance.
• Collaborate with UI/UX designers to ensure dashboards and visualizations reflect correct analytic logic and interpretability.
• Participate in Agile ceremonies and milestone planning with the TPM and broader H2FMS team.
• Conduct literature reviews, benchmarking, and best-practice analysis for model development.
• Validate analytic outputs through testing, simulation, and statistical analysis.
• Produce documentation for algorithm design, model assumptions, and analytic workflows to support cATO requirements.
• Brief analytic findings and readiness models to senior Army stakeholders and CIMT decision-makers.
• Support the development of training materials, SOPs, and analytic guides for field practitioners.
• Translate complex statistical concepts into operationally meaningful language for non-technical audiences.

Required Qualifications

• Master's degree in data science, Statistics, Computer Science, Applied Mathematics, Sports Science Analytics, Physiology, or related field.
• 8+ years of experience designing and implementing advanced analytics or predictive models, preferably in military, sports science, or human performance settings.
• Demonstrated expertise in:
• Predictive modeling
• Machine learning
• Statistical inference
• Physiological or performance modeling
• Wearable/sensor data analytics
• Experience working with multidomain human performance data (physical, nutritional, mental, sleep, spiritual).
• Ability to collaborate with multidisciplinary teams including scientists, engineers, and UI/UX developers.
• Ability to obtain and maintain a DoD Secret clearance.
• Location: Remote.
• Travel: Ability to travel to Fort Eustis, VA and/or LMI HQ in Tysons, VA 1-2 times per quarter for planning and team collaboration.

Desired Qualifications

• PhD in a relevant quantitative or performance science discipline.
• Experience supporting H2F brigades, CIMT, TRADOC, SOF human performance programs, or military analytics efforts.
• Experience applying ML/AI to performance, health, or readiness datasets.
• Proficiency with Python, R, SQL, and cloud-based analytics environments.
• Experience contributing to dashboards, scoring systems, or decision-support tools.
• Certifications such as AWS Data Analytics, AWS Developer, Azure Data Engineer, Security+, or equivalent.

The target salary range for this position is up to $109,242 - $189,108.

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.