DoD AI/ML Engineer & Data Management Specialist

Synectic Solutions Inc; Maryland; On-site; USD 150,000 - 160,000; Full time

Job summary

Synectic Solutions, Inc. is seeking an AI/ML Engineer & Data Management Specialist (Journeyman) to support LO Life Cycle Management at NAS Patuxent River, MD. The role combines data science, AI, ML, and data management in a secure, on-site environment with limited travel.

The candidate will operate across multi-level security, integrate data from multiple offices, and develop predictive models to forecast readiness trends and identify gaps in LO sustainment.

Qualifications

• Accredited BA or BS degree relevant to data science, analytics, data management, or information technology.
• At least 3 years of data science, analytics, or data management experience.
• Active TS clearance with SAP eligibility required.
• Experience with multi-level security environments.

Responsibilities

• Operate in multi-level security environments and coordinate data from multiple offices.
• Structure, maintain, and ensure integrity of large multi-source datasets.
• Apply AI/ML models to support gap analyses and readiness assessments.
• Develop prompts to optimize AI outputs for project needs.
• Build, validate, and interpret predictive models to forecast readiness trends.
• Manage databases and ETL processes to ensure data availability and quality.
• Create clear data visualizations and technical reports for engineers and leadership.
• Ensure compliance with DoD/NAVAIR security and privacy requirements.

Skills

Data analysis

Predictive modeling

AI/ML

Data visualization

ETL

Education

BA/BS in data science or IT

Tools

Python

R

SQL

Tableau/Power BI

Job description

Synectic Solutions, Inc. is seeking an AI/ML Engineer & Data Management Specialist (Journeyman) to support LO Life Cycle Management at NAS Patuxent River, MD. The role combines data science, AI, ML, and data management in a secure, on-site environment with limited travel.

The candidate will operate across multi-level security, integrate data from multiple offices, and develop predictive models to forecast readiness trends and identify gaps in LO sustainment.