Mid-Level Telecoms Architect

AgileATS, LLC.; Corridor North (MD); On-site; USD 122,000 - 144,000; Full time

Job summary

AgileATS, LLC seeks a Data Science & Access Discovery Specialist with DNI/TechSIGINT background to apply ML techniques to large telecom and network datasets. You will bridge discovery analysis with automated data science, building scalable pipelines and interactive analytic prototypes.

You will deploy and refine ML models to uncover hidden intelligence, supporting mission-critical outcomes with advanced analytics.

Qualifications

• Background in target development, Digital Network Intelligence (DNI), or Technical SIGINT (TechSIGINT).
• Experience applying ML techniques, computational mathematics, and predictive modeling to large datasets.

Responsibilities

• Bridge discovery analysis with automated data science—build, deploy, and refine ML models.
• Create scalable data pipelines and interactive analytic prototypes to uncover hidden intelligence.

Skills

ML & Data Science

DNI/TechSIGINT background

Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED

We are seeking an inventive, mission-driven Data Science & Access Discovery Specialist with background in target development, Digital Network Intelligence (DNI), or Technical SIGINT (TechSIGINT). In this high-impact role, you will apply cutting-edge Machine Learning (AI/ML) techniques, computational mathematics, and predictive modeling directly to complex, large-scale telecommunications, mobile, and network datasets. You will bridge advanced discovery analysis with automated data science- building, deploying, and refining ML models, creating scalable data pipelines, and developing interactive analytic prototypes to uncover hidden intelligence.

The annual base salary range for this role is $122,000-$144,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.