Full Stack Data Scientist

Boeing • Arlington, VA

15d • $137,700 - $186,300

About The Position

Boeing Defense, Space & Security (BDS) is seeking a Full Stack Data Scientist to support the Business Transformation organization in modernizing BDS through artificial intelligence, machine learning, and advanced digital capabilities. The role builds and scales the technical foundations for secure, reliable, production-ready AI/ML solutions across the enterprise. It combines data science, data engineering, MLOps, platform engineering, DevOps, and site reliability engineering, and supports infrastructure and platform services for data pipelines, model training and deployment, observability, and AI application delivery across on-premises, hybrid, and cloud environments.

The position partners with data scientists, software and data engineers, cybersecurity and infrastructure teams, and business stakeholders to accelerate AI/ML adoption. It supports traditional machine learning and generative AI use cases, including on-premises model serving, retrieval-augmented generation, and model lifecycle management. The successful candidate contributes strategically and hands-on by defining best practices, building reusable patterns, automating infrastructure and delivery workflows, improving reliability, and securely deploying mission-relevant AI/ML systems. Depending on level, the role may provide technical leadership, influence architecture decisions, and mentor other data scientists.

A U.S. Person as defined by 22 C.F.R. §120.62 is required for export-control compliance. The position may require travel up to 10% of the time. Relocation assistance is not negotiable; candidates must live in the immediate area or relocate at their own expense. The position does not require a security clearance and is not safety sensitive. Boeing will not sponsor employment visa status.

Requirements

• Bachelor’s degree or higher in computer science, computer engineering, software engineering, data engineering, information technology, mathematics, or a related technical field.
• 3 or more years of experience in software engineering, platform engineering, DevOps, site reliability engineering, data engineering, MLOps, machine learning infrastructure, or a related technical discipline.
• 3 or more years of experience developing software and automation solutions using Python.
• 3 or more years of experience building, deploying, operating, and orchestrating containerized applications using tools such as Docker, Podman, Buildah, and Kubernetes.
• 3 or more years of experience designing or supporting CI/CD pipelines and modern software delivery practices.
• 3 or more years of experience with data pipeline orchestration tools, production data workflows, databases, or data platforms in production environments.
• 3 or more years of experience implementing monitoring, logging, alerting, or observability solutions for production systems.
• 3 or more years of experience working in Linux-based environments and troubleshooting distributed systems.
• Must be a U.S. Person as defined by 22 C.F.R. §120.62 to meet U.S. export control compliance requirements.

Nice To Haves

• Master’s degree in Computer Science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics, or a closely related technical field.
• Experience applying foundational cybersecurity practices in application or infrastructure engineering environments.
• Experience designing and operating end-to-end AI/ML, data, and MLOps platforms, including data pipelines, experiment tracking, model registry, deployment, inference, monitoring, governance, and lifecycle management.
• Experience with production data engineering and analytics platforms, workflow orchestration tools, Python data processing libraries, and relational, NoSQL, graph, search, vector, object, and analytical data storage technologies.
• Experience with Kubernetes-based platform engineering, containerized application deployment, Helm, enterprise Kubernetes distributions, and GitOps/CI/CD workflows using tools such as ArgoCD and GitLab.
• Experience with observability, reliability engineering, and infrastructure automation, including Prometheus, Grafana, Loki, Tempo, Mimir, OpenTelemetry, SLIs/SLOs, incident response, Terraform, OpenTofu, Pulumi, Ansible, shell scripting, and Python automation.
• Experience with secure containerization, software supply chain security, and DevSecOps practices, including hardened container images, SBOM generation, image signing, vulnerability scanning, TLS, certificate management, secrets management, RBAC, least privilege, and policy enforcement.
• Experience supporting machine learning and generative AI workloads in enterprise environments, including model serving, model monitoring, evaluation, on-premises or hybrid LLM deployment, and retrieval-augmented generation architectures.
• Knowledge of AWS data and machine learning services.
• Experience in defense, aerospace, regulated, or other high-assurance environments requiring secure, reliable, and auditable technical solutions.
• Typical education/experience may include a bachelor’s degree and 9 or more years of related work experience, or an equivalent combination; another typical education/experience statement lists a bachelor’s degree and 5 or more years of related work experience, or an equivalent combination.

Responsibilities

• Design, build, deploy, and operate secure, scalable AI/ML platform capabilities for Boeing BDS users and programs.
• Develop and maintain data pipelines, workflow orchestration solutions, and data integration services for analytics and machine learning use cases.
• Build and support MLOps workflows for model training, experiment tracking, model registry, deployment, inference, monitoring, and lifecycle management.
• Engineer and operate infrastructure for traditional machine learning and generative AI workloads, including on-premises and hybrid model-serving environments.
• Deploy, administer, and optimize containerized applications and platform services using Kubernetes and associated ecosystem tools.
• Create and maintain Helm charts, deployment templates, automation scripts, and reusable platform patterns for AI/ML applications.
• Implement CI/CD and GitOps workflows for secure, repeatable, observable software and model delivery.
• Configure and support observability capabilities, including metrics, logs, traces, dashboards, alerting, and reliability reporting.
• Apply DevSecOps principles to application and infrastructure delivery, including certificate management, secrets handling, access control, least privilege, and defense-in-depth controls.
• Support administration and integration of relational, NoSQL, search, graph, object, and vector data storage technologies.
• Build and maintain secure container images and software supply chain controls, including vulnerability scanning and artifact management.
• Collaborate with data scientists and application teams to productionize models, improve runtime performance, and reduce friction in AI/ML development workflows.
• Troubleshoot complex platform, data, networking, deployment, and performance issues across distributed environments.
• Develop infrastructure-as-code and automation solutions to provision, configure, and manage platform resources.
• Contribute to architecture decisions, platform standards, technical documentation, operational runbooks, and engineering best practices.
• Support incident response, root cause analysis, reliability improvement efforts, and continuous service optimization.

Benefits

• Eligible employees may enroll in health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and programs providing paid and unpaid time away from work.
• Competitive base pay and variable compensation opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level