Senior Principal Data Scientist — TS/SCI Mission Analytics

General Dynamics Information Technology, Inc.; Arlington (VA); On-site; USD 187,000 - 253,000; Full time

Job summary

Iron EagleX is seeking a Senior Principal Data Scientist to join our team in Crystal City, VA. You will work with large, diverse datasets to develop practical analytic methods and deliver insights that support critical customer requirements.

You will collaborate with analysts, software developers, and technical staff to convert prototypes into reusable pipelines and operational capabilities, ensuring outputs are reproducible and explainable.

Qualifications

• Current TS/SCI clearance with CI polygraph willingness.
• 10+ years of related experience.
• Bachelor's degree in CS/Statistics/Engineering or related field; advanced degrees a plus.
• Onsite in Crystal City, VA; travel to CONUS sites 60–90 days yearly; US Citizens only.

Responsibilities

• Translate large, complex datasets into actionable analytic insights.
• Develop repeatable analytic pipelines and dashboards for analysts.
• Apply statistical and ML methods for classification, clustering, and anomaly detection.
• Create analyst-ready outputs: tables, visualizations, and structured exports.
• Collaborate across analysts, engineers, and stakeholders to productionize methods.
• Ensure reproducibility and explainability of analytic results.

Skills

Python

SQL

Statistical analysis

Mathematical modeling

Data analytics

Pandas

NumPy

Scikit-learn

Machine learning

Data visualization

Education

Bachelor's degree in Computer Science, Statistics, Engineering, or related field

Tools

Trino

PostgreSQL

Hive

OpenSearch

Elasticsearch

Docker

FastAPI

Job description

Iron EagleX is seeking a Senior Principal Data Scientist to join our team in Crystal City, VA. You will work with large, diverse datasets to develop practical analytic methods and deliver insights that support critical customer requirements.

You will collaborate with analysts, software developers, and technical staff to convert prototypes into reusable pipelines and operational capabilities, ensuring outputs are reproducible and explainable.