Implement and refactor data pipelines at scale to improve efficiency and code correctness.
Monitor and enhance existing data science tools to facilitate the transition from development to production systems.
Design, implement, and enhance machine learning analytics using Python libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.
Train, test, track, and curate models using industry-standard tools and practices.
Integrate GitOps for continuous integration and deployment of models using Docker and Kubernetes.
Utilize AWS services such as EC2, S3, and RDS for building and deploying applications.
Integrate model and tool outputs within Computer Network Defense (CND) systems to enhance Security Management/Monitoring services.
Document all processes and code, and provide comprehensive reports on completed tasks.

Requirements

Active TS/SCI w/ Polygraph
Experience with Python
Experience with Docker and/or Kubernetes
14 years’ experience as a Software Engineer in programs and contracts of similar scope, type, and complexity, or a Bachelor’s degree in Computer Science or a related discipline plus 4 years of additional SWE experience.
Bachelor’s degree in Computer Science or a related discipline from an accredited college or university is required.
Alternatively, 4 years of additional Software Engineering experience on projects with similar software processes may substitute for a bachelor’s degree.
Experience with AWS
Experience with Machine Learning

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonMachine LearningData PipelinesModel TrainingModel TestingModel CurationGitOpsContinuous IntegrationContinuous DeploymentData Analytics

Soft Skills

DocumentationReporting

Certifications

TS/SCI w/ PolygraphBachelor’s degree in Computer Science