Data Scientist - Security Automation

CACI International•Linthicum Heights, MD

1d•$131,800 - $290,000•Onsite

About The Position

CACI is seeking a Data Scientist to join a mission-driven team advancing the Secure the Enterprise initiative. In this role, you will help transform a traditionally manual system-security evaluation process into an automated, efficient, and intelligence-driven model. Your work will directly support continuous monitoring, rapid decision-making, and strengthened security across the full lifecycle of critical systems and projects.

Requirements

Active TS/SCI w/ Polygraph
10+ years of experience analyzing datasets and developing analytics
10 years of experience programming with data-analysis tools (such as R, Python, SAS, MATLAB)
Bachelor’s and Master’s degree (or higher) in a quantitative discipline such as statistics, mathematics, operations research, engineering, or computer science.
An additional 2 years of experience in software development, cloud development, dataset analysis, or analytics development in place of a Master’s degree
A PhD in a quantitative discipline for 4 years of experience.
Strong background in statistical analysis.
Experience developing, tuning, and testing predictive models.
Proficiency in creating analytic dashboards, visualizations, and charts.

Nice To Haves

Elasticsearch
Regular expressions (RegEx)
Machine Learning
Natural Language Processing (NLP)
Regression and predictive analytics
Python, MATLAB, or R
SQL or MongoDB
Metric databases (Grafana, Graphite, InfluxDB)

Responsibilities

Develop and enhance automated capabilities that replace manual security evaluation and authorization processes.
Use Python to process, analyze, and validate security-related data, ensuring systems and resources meet security requirements.
Detect anomalies and outliers to assess progress through the Risk Management Framework (RMF) lifecycle.
Identify system associations for resources based on various attributes and known system information.
Design, build, and maintain ETL pipelines to collect and integrate data from multiple sources (network sensors, security tools, compliance databases).
Transform and prepare data for reporting, analysis, and automated assessments.
Support data engineering operations including data quality checks, pipeline monitoring, workflow optimization, and timely delivery of security insights.
Enable scalable, reliable dataflows that support continuous monitoring and RMF automation.