Senior Data Scientist

Boeing Boeing · Aerospace · Seattle, WA +1

Senior Data Scientist at Boeing to lead the development and deployment of advanced predictive and prescriptive analytics solutions. The role involves defining ML/AI strategies, building scalable architectures (MLOps, AIOps), designing and optimizing models, and integrating them into production systems. It also includes developing Generative AI use cases (LLMs, RAG, Agents, fine-tuning, vector databases, prompt engineering) and leading organizational change for analytics adoption.

What you'd actually do

  1. Define the strategy to build highly reliable and scalable ML and AI solutions that align with the organization’s business goals and objectives
  2. Lead the creation and implementation of scalable, robust, and high-performance ML architectures including MLOps, AIOps leveraging cloud native services (AWS, Azure, GCP) and open-source frameworks
  3. Design, build, and optimize machine learning models, ensuring accuracy, efficiency, and scalability
  4. Partner with product managers, engineers, and business stakeholders to define problem statements, success metrics, and deployment requirements
  5. Collaborate with data engineers, data architect, software developers, and DevOps teams to integrate ML models into production systems

Skills

Required

  • Ability to obtain a US Security Clearance
  • Bachelor’s degree or higher
  • 5+ years of experience with AI/ML technologies, frameworks, models and ensembles
  • 5+ years with container and container orchestration (Docker and Kubernetes)
  • 5+ years of experience with data engineering and data pipelines for On-Prem cloud, hybrid data models and data warehouses
  • 5+ years of experience with software programming/scripting (such as Python, Unix/Linux type batch scripting, FORTRAN, C / C++)

Nice to have

  • 5+ years of experience in the manufacturing or aviation domain
  • 5+ years of experience with big data technologies and data engineering practices
  • Experience in multi-cloud and hybrid AI architecture
  • Experience with generative AI, NLP, computer vision, or reinforcement learning
  • Experience with CI/CD pipelines, DevOps practices and containerized deployments
  • Experience with open-source ML projects or publications in relevant fields

What the JD emphasized

  • 5+ years of experience with AI/ML technologies, frameworks, models and ensembles
  • 5+ years with container and container orchestration (Docker and Kubernetes)
  • 5+ years of experience with data engineering and data pipelines for On-Prem cloud, hybrid data models and data warehouses
  • 5+ years of experience with software programming/scripting (such as Python, Unix/Linux type batch scripting, FORTRAN, C / C++)

Other signals

  • Define the strategy to build highly reliable and scalable ML and AI solutions
  • Lead the creation and implementation of scalable, robust, and high-performance ML architectures including MLOps, AIOps leveraging cloud native services (AWS, Azure, GCP) and open-source frameworks
  • Design, build, and optimize machine learning models, ensuring accuracy, efficiency, and scalability
  • Design and development of Generative AI and AI use cases (LLMs, RAG, Agentic, multi model AI, fine tuning. Vector databases and prompt engineering)