Full Stack Data Scientist

Full Stack Data Scientist

At Boeing, we innovate and collaborate to make the world better. Boeing Defense, Space & Security (BDS) is seeking a Full Stack Data Scientist to join the Business Transformation organization in support of modernizing how BDS operates through artificial intelligence, machine learning, and advanced digital capabilities. This role will help build and scale the technical foundations that enable secure, reliable, and production-ready AI/ML solutions across the enterprise.

This position is ideal for a data scientist who brings a blend of data science, data engineering, MLOps, platform engineering, DevOps, and site reliability engineering (SRE) experience. The selected candidate will help design, implement, secure, and operate the infrastructure and platform services that support data pipelines, model training, model deployment, observability, and AI application delivery. The role will work across on-premises, hybrid, and cloud-based environments, with a strong emphasis on enterprise-grade security, resiliency, traceability, and operational excellence.

The full stack data scientist in this role will partner closely with data scientists, software engineers, data engineers, cybersecurity teams, infrastructure teams, and business stakeholders to accelerate adoption of AI/ML capabilities within BDS. This includes enabling both traditional machine learning workflows and emerging generative AI use cases, including support for on-premises model serving, retrieval-augmented generation architectures, and modern model lifecycle management practices. The role requires strong systems thinking and the ability to translate business and mission needs into scalable platform solutions.

The successful candidate will be expected to contribute both strategically and hands-on. They will help define best practices, build reusable patterns, automate infrastructure and delivery workflows, improve platform reliability, and support secure deployment of mission-relevant AI/ML systems. Depending on level, this role may also provide technical leadership, influence architecture decisions, and mentor other data scientists across the organization.

This position offers the opportunity to shape the future of AI/ML enablement within Boeing BDS by building the secure and modern technical ecosystem needed to support transformation at scale.

Key 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 that support 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 to support secure, repeatable, and observable software and model delivery processes
• Configure and support observability capabilities including metrics, logs, traces, dashboards, alerting, and reliability reporting
• Apply DevSecOps principles across application and infrastructure delivery, including certificate management, secrets handling, access control, least privilege, and defense-in-depth controls
• Support administration and integration of data storage technologies including relational, NoSQL, search, graph, object, and vector data platforms
• 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

Basic Qualifications (Required Skills / Experience):

• Bachelor's degree or higher in computer science, computer engineering, software engineering, data engineering, information technology, mathematics, or a related technical field
• 3+ years of experience in software engineering, platform engineering, DevOps, site reliability engineering, data engineering, MLOps, machine learning infrastructure, or a related technical discipline
• 3+ years of experience developing software and automation solutions using Python
• 3+ years of experience building, deploying, operating, and orchestrating containerized applications using tools such as Docker, Podman, Buildah, and Kubernetes
• 3+ years of experience designing or supporting CI/CD pipelines and modern software delivery practices
• 3+ years of experience working with data pipeline orchestration tools, building production data workflows, databases, or data platforms in production environments
• 3+ years of experience implementing monitoring, logging, alerting, or observability solutions for production systems
• 3+ years of experience working in Linux-based environments and troubleshooting distributed systems

Preferred Qualifications (Desired Skills / Experience):

• Master's 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 using 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 and experience working in defense, aerospace, regulated, or other high-assurance environments requiring secure, reliable, and auditable technical solutions