Data Scientist, Senior

Booz Allen Hamilton•Reston, VA

About The Position

As a senior Data Scientist, you will serve as the technical backbone of the data science mission by supporting the platforms and environments that enable data scientists to experiment, iterate, and operationalize insights. You will build and support scalable, cloud-native services, automated workflows, reproducible environments, governed data access, and secure infrastructure. You will shape environments for notebook use, dataset exploration, and analytical development, integrating these capabilities with the wider platform to help accelerate insights for mission stakeholders. The role supports data science and analytics environments in highly classified cloud settings. It involves cloud platform architecture and administration, access controls, CI/CD, Kubernetes-based workflow execution, infrastructure as code, compliance automation, configuration hardening, testing, vulnerability scanning, artifact management, and Python-based analytical environments. Applicants must have an active TS/SCI clearance and be willing to take a polygraph exam. The projected annual compensation range is $112,800 to $257,000. This is a Booz Allen position; the posting states that it will close within 90 days of its posting date.

Requirements

At least 8 years of experience supporting data science, analytics environments, platform engineering, or cloud-native operations.
Experience architecting and administering IL6 or IL7 cloud environments, such as AWS regions or Azure Government.
Experience designing and enforcing strict IAM and least-privilege access across multi-tenant environments.
Experience developing CI/CD pipelines using GitLab CI, Jenkins, AWS CodePipeline, Git, or GitHub Actions.
Experience with Kubernetes, such as EKS or AKS, including runner orchestration and containerized workflow execution.
Knowledge of infrastructure as code, compliance automation, and configuration hardening.
Knowledge of automated testing, vulnerability scanning, artifact management, and environment promotion.
Knowledge of Python-based environments such as Jupyter, conda, or venv, package management, and reproducible execution patterns.
Active TS/SCI clearance and willingness to take a polygraph exam.
Bachelor's degree with at least 8 years of data analytics experience, or at least 12 years of data analytics experience in lieu of a degree.

Nice To Haves

Experience deploying and orchestrating workloads in Kubernetes or Red Hat OpenShift clusters.
Experience building containerized analytical tools or reproducible scientific environments.
Experience configuring observability for platform performance using tools such as Prometheus, Grafana, or OpenTelemetry.
Experience guiding platform modernization efforts or leading technical delivery teams.
Experience enabling and supporting data scientists in AWS using services such as S3, EC2, IAM, Lambda, EKS or ECS, and CloudWatch.
Experience preparing and maintaining System Security Plans (SSPs) and documentation for Risk Management Framework (RMF) and Authority to Operate (ATO) processes.
Knowledge of mission-related datasets, including SIGINT, GEOINT, HUMINT, or OSINT.
Knowledge of Git, collaborative workflows, and version-control best practices.
Ability to lead cloud deployment workstreams and collaborate with data scientists, cloud engineers, and security teams.
AWS certifications such as Solutions Architect, DevOps Engineer, SysOps, or ML Specialty.

Responsibilities

Build and support cloud services and platforms designed around the needs of data scientists.
Enable data scientists to experiment, iterate, explore datasets, develop analytical logic, and operationalize insights.
Shape and integrate environments for notebook use and analytical development.
Support scalable compute, automated workflows, reproducible environments, governed data access, and secure cloud-native infrastructure.
Design and administer classified cloud environments and enforce strict IAM and least-privilege access across multi-tenant environments.
Develop CI/CD pipelines and support Kubernetes-based runner orchestration and containerized workflow execution.
Apply infrastructure as code, compliance automation, and configuration hardening.
Support automated testing, vulnerability scanning, artifact management, and environment promotion.