Lead Artificial Intelligence /Machine Learning Data Scientist (Data Science)

Seattle, Washington; Dallas, Texas Job ID JR2026511183 Category Data Science \& Analytics Role Type Hybrid Post Date Jun. 10, 2026 At Boeing, we innovate and collaborate to make the world a better place. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. The Boeing Company is currently seeking a **Lead Artificial Intelligence/Machine Learning** **Data Scientist**to join the team in **Seattle, WA** or **Dallas, TX**. The Boeing Company is looking for an experienced data scientist who can play lead role in brainstorming new Artificial Intelligence (AI) solutions, provide thought leadership in the AI products domain, develop and deploy next generation Artificial Intelligence (AI) and Machine Learning (ML) products and services. This position will work effectively with cross\-functional teams and technical leads to determine, define, and deploy analytics solutions to meet business goals. At Boeing, we are already making a difference across the industry using AI and ML to deliver superior product experience to our internal functions and commercial and government customers. We are expanding and growing in all areas on the frontier of the aerospace industry. We already have some of the best minds and talent in this field, and we’re looking for someone with applied experience in AI and ML, stay up\-to\-date on the latest trends in the AI domain (including Agentic AI, Natural Language Processing, Deep Learning, Conversational AI, and Computer Vision), to join our team in this lead data scientist role. **Position Responsibilities:** * Evaluate business objectives, collaborate in user research, understand process nuances, determine stakeholder needs, and identify requirements * Champion the AI cause with stakeholder groups, educate and excite sponsors about possibilities offered by the data science discipline * Choose best fit methods,