LMI is seeking a Senior Data Scientist to build and deploy machine learning capabilities for a U.S. Army customer. You will move models from concept to production on LMI platforms, working alongside engineers, operators, and mission users to support Army analysis and decision-making. You will be responsible for the full scope of the work, from data preparation through models deployed and used in the customer’s environment.

This is a hybrid position based in Washington, DC, with regular time on client site and occasional travel to LMI HQ in Tysons, VA. This position requires an active Secret clearance, for which you must be a U.S. citizen.

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.

• Design, train, validate, and deploy machine learning models against Army data, from problem framing through production monitoring.
• Build production-caliber code and data pipelines that run on LMI platforms and within the customer’s environment.
• Translate ambiguous mission needs into well-scoped analytic problems, and translate model outputs into clear recommendations for Army decision-makers.
• Engineer features and data workflows across structured and unstructured sources; perform ETL, data quality assessment, and exploratory analysis to support modeling.
• Partner with software, platform, and DevSecOps engineers to integrate models into deployed systems, including evaluation, retraining, and drift monitoring after fielding.
• Apply MLOps practices (versioning, testing, CI/CD, reproducibility) to ensure models are auditable and meet federal security standards.
• Communicate methods, assumptions, and results clearly to both technical teams and senior, non-technical Army stakeholders.
• Mentor mid-level data scientists and analysts, and help set technical direction and standards for the team.

Required

• Active Secret clearance (or eligibility to obtain one); U.S. citizenship required.
• Bachelor’s degree in a quantitative field (computer science, statistics, mathematics, engineering, operations research, or related); advanced degree preferred.
• 7+ years of combined experience in data science, statistical modeling, analytics, or data engineering.
• Strong programming skills in Python and/or R, and SQL.
• Demonstrated experience across the analytic and ML lifecycle, including data preparation, feature engineering, modeling, and validation.
• Proven ability to communicate complex results clearly to senior decision-makers.

Preferred

• Experience supporting Army or broader DoD customers and familiarity with operating in classified environments.
• Active TS/SCI, or eligibility to upgrade clearance.
• Experience building, deploying, or maintaining models or data products in production environments, including monitoring and retraining.
• Familiarity with common ML libraries (e.g., scikit-learn, PyTorch, TensorFlow) and with MLOps practices, feature stores, or model governance.
• Experience with data pipelines and ETL (e.g., streaming or event-driven processing) and with cloud environments (AWS, Azure, or GCP) and containerized workflows (Docker, Kubernetes), or willingness to develop these skills.
• Background in NLP, computer vision, time-series forecasting, anomaly detection, or operations research applied to defense problems.
• Familiarity with Agile delivery and working on balanced, cross-functional product teams.

Target salary range: $113,560 - $192,050. Final compensation will be determined by a variety of factors including but not limited to your skills, experience, education, and/or certifications.