Data Scientist - Clearance Required

LMI•Fort Bragg, NC

2d•Onsite

About The Position

LMI is seeking a Data Scientist to support a Special Operations Command (SOCOM) mission partner with advanced analytics, predictive modeling, natural language processing, and artificial intelligence and machine learning (AI/ML) product development. The Data Scientist will analyze complex historical and operational datasets, convert data into machine-learning-ready formats, identify trends and predictive features, develop and validate statistical and machine learning models, and provide decision-quality insights that support resource forecasting, operational planning, and modernization. This position will work as part of a cross-functional data science product team to develop, integrate, govern, sustain, and document mission-relevant applications, dashboards, models, and research products. 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 achieve mission success.

Requirements

Active Secret security clearance with the ability to obtain a Top Secret clearance.
Ability to work on-site at Fort Bragg, North Carolina.
Bachelor’s degree in data science, statistics, mathematics, computer science, operations research, engineering, or a related quantitative field.
Five or more years of professional experience applying data science, advanced analytics, statistical modeling, or machine learning to complex real-world problems.
Demonstrated experience developing and validating predictive models, including time-series, regression, ensemble, or comparable forecasting methods.
Advanced proficiency with Python and SQL and practical experience with common data science and machine learning libraries; proficiency with R or a comparable analytical language may substitute where appropriate.
Strong knowledge of statistical analysis, feature engineering, model selection, hyperparameter tuning, cross-validation, error analysis, and performance measurement.
Experience preparing large, incomplete, inconsistent, structured, and unstructured datasets for repeatable analysis and model training.
Experience with natural language processing, generative AI, large language models, or retrieval-augmented generation in an applied environment.
Ability to evaluate model performance against stringent accuracy requirements and clearly communicate tradeoffs, risks, assumptions, and limitations.
Experience producing technical documentation, analytical reports, dashboards, briefings, and recommendations for technical and non-technical stakeholders.
Strong written and verbal communication skills and the ability to collaborate across data, engineering, software, security, governance, and operational teams.
Ability to independently manage multiple priorities and deliver high-quality analytical products in a fast-paced, mission-focused environment.
Applicants must meet eligibility requirements for a U.S. Government security clearance.
Only US Citizens are eligible for a security clearance.
For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work.

Nice To Haves

Master’s degree or doctorate in data science, statistics, mathematics, computer science, operations research, engineering, or a related quantitative field.
Experience supporting SOCOM, U.S. Special Operations Forces, the Department of War, or another national security mission partner.
Experience developing models for resource consumption, demand, readiness, utilization, program execution, or annual planning forecasts.
Experience integrating analytical models into production web applications through APIs, services, containers, or reusable software components.
Familiarity with MLOps, DevSecOps, model monitoring, version control, automated testing, and continuous integration and continuous delivery practices.
Experience with secure cloud analytics environments such as AWS GovCloud or Azure Government and data visualization platforms such as Power BI or Tableau.
Familiarity with Agile delivery methods and experience working in cross-functional product or software development teams.
Experience supporting data governance, model governance, application sustainment, user adoption, and knowledge transfer for government data products.

Responsibilities

Analyze historical operational records, program execution data, and related datasets to identify trends, relationships, anomalies, and key features for predictive modeling.
Clean, normalize, reconcile, label, and transform structured and unstructured data from multiple sources into traceable, machine-learning-ready datasets.
Develop, test, and refine predictive models to forecast resource consumption and annual rate projections with a maximum error margin of 1%.
Apply statistical analysis, feature engineering, time-series forecasting, regression, ensemble methods, and other appropriate techniques to improve model accuracy, reliability, explainability, and operational usefulness.
Establish model validation, back-testing, sensitivity analysis, error analysis, and performance-monitoring methods; document assumptions, limitations, risks, and sources of uncertainty.
Develop natural language processing and generative AI solutions, including large language models tailored to approved business, operational, and intelligence use cases.
Develop projects that automate or augment human cognitive workload and respond rapidly to emerging operational data and data science requirements.
Collaborate with AI/ML engineers, data engineers, software developers, cybersecurity personnel, and mission stakeholders to integrate validated models and analytical outputs into secure web-based applications and enterprise workflows.
Support enterprise synchronization, integration, governance, security, sustainment, and adoption of data science and AI/ML products across multiple mission teams and stakeholder organizations.
Translate complex analytical findings into clear, actionable insights and recommendations for technical teams, program managers, operational users, and senior mission-partner leaders.
Develop and maintain customer-focused data science products, including applications, dashboards, analytical models, and research projects, through their full product life cycle.
Produce analytical reports, dashboards, briefings, and decision-support products that communicate trends, insights, model performance metrics, and recommendations.
Maintain comprehensive documentation of data sources, methodologies, feature definitions, model logic, validation results, system dependencies, workflows, and repeatable analytical processes.
Develop user guides, training materials, demonstrations, and knowledge-transfer products sufficient for a qualified practitioner to assume future operation and sustainment of the application or capability.
Provide rapid-response analytical and product-level staff augmentation based on changes in mission priorities and the operational environment.