Full Stack Data Scientist

Boeing; Arlington (VA); Presencial; USD 138 000 - 186 000; Tempo integral

Resumo da oferta

The Boeing Company is seeking a Full Stack Data Scientist to join the Business Transformation organization in support of modernizing how Boeing Defense, Space & Security (BDS) operates through AI, ML, and advanced digital capabilities. You will help design, build, secure, and operate the infrastructure and platform services that enable secure, reliable AI/ML solutions across the enterprise.

The role blends data science, data engineering, MLOps, platform engineering, DevOps, and SRE, and requires

Qualificações

• Bachelor's degree or higher in a technical field.
• 3+ years in software engineering, platform engineering, DevOps, SRE, data engineering, MLOps, or related discipline.
• 3+ years developing software and automation using Python.
• 3+ years containerized apps with Docker, Podman, Buildah, and Kubernetes.
• 3+ years designing or supporting CI/CD pipelines and modern software delivery.
• 3+ years working with production data pipelines and platforms.
• 3+ years implementing monitoring, logging, or observability for production systems.
• 3+ years Linux-based environment experience.

Responsabilidades

• Design, build, deploy, and operate secure, scalable AI/ML platform capabilities.
• Develop and maintain data pipelines, orchestration, and data integrations.
• Build and support MLOps workflows for model training, deployment, monitoring, and lifecycle.
• Engineer and operate infrastructure for traditional ML and generative AI workloads.
• Deploy, administer, and optimize containerized apps using Kubernetes and related tools.
• Create Helm charts, deployment templates, and automation scripts for AI/ML apps.
• Implement CI/CD and GitOps to enable secure, repeatable delivery processes.
• Configure observability with metrics, logs, traces, dashboards, and alerts.
• Apply DevSecOps practices across delivery, including certificate and secrets management.
• Support various data stores and data platforms (relational, NoSQL, search, graph, vector).
• Maintain secure container images and software supply chain controls.
• Collaborate with teams to productionize models and improve runtime performance.
• Troubleshoot platform, data, networking, and deployment issues across distributed environments.
• Develop infrastructure-as-code and automation to provision platform resources.
• Contribute to architecture decisions, standards, and runbooks.
• Support incident response and reliability improvement efforts.

Conhecimentos

Python

CI/CD

Linux

MLOps

Data pipelines

DevSecOps

Data engineering

Formação académica

Bachelor's degree or higher

Ferramentas

Docker

Kubernetes

Podman

Buildah

Helm

Descrição da oferta de emprego

Full Stack Data Scientist

Company:

The Boeing Company

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, & lifecycle management
• Engineer and operate infrastructure for traditional machine learning & 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, & 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, & reliability reporting
• Apply DevSecOps principles across application & infrastructure delivery, including certificate management, secrets handling, access control, least privilege, & defense‑in‑depth controls
• Support administration & integration of data storage technologies including relational, NoSQL, search, graph, object, & vector data platforms
• Build & maintain secure container images & software supply chain controls, including vulnerability scanning & artifact management
• Collaborate with data scientists & application teams to productionize models, improve runtime performance, & reduce friction in AI/ML development workflows
• Troubleshoot complex platform, data, networking, deployment, & performance issues across distributed environments
• Develop infrastructure‑as‑code & automation solutions to provision, configure & manage platform resources
• Contribute to architecture decisions, platform standards, technical documentation, operational runbooks & engineering best practices
• Support incident response, root cause analysis, reliability improvement efforts, & continuous service optimization

To meet U.S. export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120.62 is required. “U.S. Person” includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee

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 & automation solutions using Python
• 3+ years of experience building, deploying, operating, & orchestrating containerized applications using tools such as Docker, Podman, Buildah, & Kubernetes
• 3+ years of experience designing or supporting CI/CD pipelines & 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 & 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 & operating end‑to‑end AI/ML, data, & MLOps platforms, including data pipelines, experiment tracking, model registry, deployment, inference, monitoring, governance, & lifecycle management
• Experience with production data engineering & analytics platforms using workflow orchestration tools, Python data processing libraries, and relational, NoSQL, graph, search, vector, object, & analytical data storage technologies
• Experience with Kubernetes‑based platform engineering, containerized application deployment, Helm, enterprise Kubernetes distributions, & GitOps/CI/CD workflows using tools such as ArgoCD & GitLab
• Experience with observability, reliability engineering, & infrastructure automation, including Prometheus, Grafana, Loki, Tempo, Mimir, OpenTelemetry, SLIs/SLOs, incident response, Terraform, OpenTofu, Pulumi, Ansible, shell scripting, & Python automation
• Experience with secure containerization, software supply chain security, & DevSecOps practices, including hardened container images, SBOM generation, image signing, vulnerability scanning, TLS, certificate management, secrets management, RBAC, least privilege, & policy enforcement
• Experience supporting machine learning & generative AI workloads in enterprise environments, including model serving, model monitoring, evaluation, on‑premises or hybrid LLM deployment, & retrieval‑augmented generation architectures
• Knowledge of AWS data & machine learning services and experience working in defense, aerospace, regulated, or other high‑assurance environments requiring secure, reliable, & auditable technical solutions

Typical Education / Experience:

Education/experience typically acquired through advanced technical education (e.g. Bachelor) and typically 9 or more years’ related work experience, or an equivalent combination of technical education and experience (e.g. PhD + 4 years’ related work experience, Master + 7 years’ related work experience, 13 years’ related work experience, etc.).

Typical Education/Experience:

Education/experience typically acquired through advanced technical education (e.g. Bachelor) and typically 5 or more years’ related work experience or an equivalent combination of technical education and experience (e.g. PhD, Master+3 years’ related work experience, 6years’ related work experience, etc.)

Conflict of Interest:

Successful candidates for this job must satisfy the Company’s Conflict of Interest (COI) assessment process.

Relocation:

Relocation assistance is not a negotiable benefit for this position. Candidates must live in the immediate area or relocate at their own expense.

Travel:

Position may require travel up to 10% of the time.

Drug Free Workplace:

Boeingis a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies.

Pay & Benefits:

At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent.Elements of the Total Rewards package include competitive base pay and variable compensation opportunities.

The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work.

The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.

Pay is based upon candidate experience and qualifications, as well as market and business considerations.

Summary pay range: $137,700-$186,300

Language Requirements:

Not Applicable

Education:

Bachelor’s Degree or Equivalent

Relocation:

Relocation assistance is not a negotiable benefit for this position.

Export Control Requirement:

This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120.62 is required. “U.S. Person” includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee.

Safety Sensitive:

This is not a Safety Sensitive Position.

Security Clearance:

This position does not require a Security Clearance.

Visa Sponsorship:

Employer will not sponsor applicants for employment visa status.

Contingent Upon Award Program

This position is not contingent upon program award

Shift:

Shift 1 (United States of America)

Boeing Participates in E – Verify

• E-Verify (English)
• E-Verify (Spanish)

Right to Work Statement

• Right to Work(English)
• Right to Work(Spanish)

Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.

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