AI/ML Data Scientist

CGI•Quantico, VA

8h•$79,600 - $153,300

About The Position

The AI/ML Data Scientist serves as the technical backbone of the program analytics capability, responsible for building, training, and deploying machine learning models that transform raw vehicle sensor data into predictive maintenance insights. Operating within the Microsoft Azure MCEN and Jupiter/Advana cloud environments, this individual develops anomaly detection algorithms, predictive failure models, and health scoring systems that enable condition-based maintenance decisions. The role requires privileged access to Government cloud computing environments, and the ability to translate complex data science into operationally understandable outputs that Marine maintainers can act upon. This position works hand-in-hand with the Sr Maintenance Functional SME to ensure models are operationally validated, the Jr Maintenance SME & Information Specialist to ensure outputs are expressed in maintainer language, and the Data Visualization Developer to ensure insights are presented effectively. This position is located in Quantico, VA.

Requirements

Master's degree in Data Science, Computer Science, Applied Mathematics, or related quantitative field (or Bachelor's with 5+ additional years of experience)
5+ years of experience developing and deploying ML models in production environments
Proficiency in Python, R, or equivalent, with experience in scikit-learn, TensorFlow, PyTorch, or similar frameworks
Experience with Microsoft Azure cloud services, Azure Machine Learning, and DataBricks
Experience building ETL pipelines and working with structured and unstructured sensor/IoT data
Security+ certification or equivalent IAT-II certification (or ability to obtain within 90 days of start)
Active Secret clearance

Nice To Haves

Experience with CAN bus data, vehicle telemetry, or industrial IoT sensor analytics
Familiarity with predictive maintenance, condition monitoring, or reliability engineering domains
Experience with Qlik, Power BI, or similar visualization tools in government environments
Prior experience on the Marine Corps Enterprise Network (MCEN) or with Azure Government cloud
Understanding of DoD Cybersecurity Framework (RMF), NIST 800-53 controls, and STIG compliance
Experience with the Jupiter/Advana analytics environment

Responsibilities

Design, build, train, and deploy AI/ML models for predictive maintenance, anomaly detection, and equipment health scoring within Azure MCEN and Jupiter/Advana environments per PWS 3.3.1
Develop and maintain the data Extract, Transform, and Load (ETL) pipeline from data logger ingestion through the Azure cloud stack to dashboard outputs per PWS 3.3.2
Analyze CAN bus data (J1939/J1708 protocols) including thermal analysis, fuel system diagnostics, vibration/shock data, and component operating parameters
Collaborate with the Sr Maintenance Functional SME to validate model outputs against real-world maintenance outcomes and refine prediction accuracy targets (>80% positive predictive value)
Optimize cloud architecture for scalability as the program expands from initial deployment to 3,000–10,000+ data loggers per PWS 3.3.7
Incorporate additional data sources into the program data pipeline including GCSS-MC maintenance records, EFAS fluid analysis, and enterprise logistics data per PWS 3.3.8
Support current dashboards, data pipelines, policies, and procedures from data entry point to Jupiter/Advana per PWS 3.3.4
Develop and refine data query frameworks that enable Marine Corps data analysis requests for ad-hoc reporting and trend analysis
Perform daily monitoring of data processes during normal business hours to proactively identify pipeline issues per PWS 3.3.7
Support Jira task tracking, Wiki management, onboarding of contractor personnel to cloud environments, and data governance policies per PWS 3.3.7
Leverage Microsoft Azure's native AI/ML capabilities including Azure Machine Learning for model training and deployment, and DataBricks for advanced analytics