Lead Artificial Intelligence /machine Learning Data Scientist (data Science)

Boeing Boeing · Aerospace · Seattle, WA +1

Lead AI/ML Data Scientist at Boeing, focusing on developing and deploying AI/ML products and services across various business units like Aircraft operations, Engineering, Supply Chain, Manufacturing, Quality, and Safety. The role involves evaluating business objectives, defining requirements, choosing methods, deploying models, and providing thought leadership and mentoring within the data science community. Emphasis on applied experience, staying updated on AI trends (Agentic AI, NLP, Deep Learning, Conversational AI, Computer Vision), and delivering business value through advanced analytics solutions.

What you'd actually do

  1. Evaluate business objectives, collaborate in user research, understand process nuances, determine stakeholder needs, and identify requirements
  2. Champion the AI cause with stakeholder groups, educate and excite sponsors about possibilities offered by the data science discipline
  3. Choose best fit methods, define algorithms, validate, and deploy models to achieve business results
  4. Build AI solutions iteratively, in short cycles to enable a fail-fast, rapid learning environment. Be ready to experiment creatively and evoke trust in the process for partner teams and stakeholders
  5. Interface directly with senior business leaders and stakeholders to architect and lead the development of advanced analytics solutions supporting Aircraft operations, Engineering, Supply Chain, Manufacturing, Quality, and Safety

Skills

Required

  • 10+ years of experience working with projects, teams, and/or leaders in the analytics and data science space
  • 10+ years of experience with various machine learning methods such as regression, clustering, classification, decision trees, natural language processing, ensemble methods, support vector machines (SVM), deep learning, reinforcement learning, etc.
  • 5+ years of experience in designing and implementing Natural Language Processing (NLP) models using machine learning techniques including neural networks, deep learning and transformer architectures
  • 5+ years of experience researching and applying large language and generative AI models
  • Experience programming in one or more of the following: C, C++, Java, Python, Scala
  • Experience with statistical software and database languages (e.g., SQL)
  • Experience in advanced statistics (e.g., probability theory and distributions, descriptive statistics, matrix algebra, multivariate statistics, polynomial analysis, parametric and non-parametric methods, etc.)

Nice to have

  • Master’s degree in data science or equivalent
  • Experience developing and implementing end-to-end Agentic AI solutions
  • Experience in supply chain domain
  • Experience in agile and product-oriented development
  • Experience in building ML models and bringing them to full production use within the industry
  • Experience training machine learning models in a cloud computing environment such as: Amazon Web Services, Google Cloud Platform, Microsoft Azure

What the JD emphasized

  • 10+ years of experience working with projects, teams, and/or leaders in the analytics and data science space
  • 10+ years of experience with various machine learning methods such as regression, clustering, classification, decision trees, natural language processing, ensemble methods, support vector machines (SVM), deep learning, reinforcement learning, etc.
  • 5+ years of experience in designing and implementing Natural Language Processing (NLP) models using machine learning techniques including neural networks, deep learning and transformer architectures
  • 5+ years of experience researching and applying large language and generative AI models

Other signals

  • develop and deploy next generation Artificial Intelligence (AI) and Machine Learning (ML) products and services
  • applied experience in AI and ML
  • lead the development of advanced analytics solutions supporting Aircraft operations, Engineering, Supply Chain, Manufacturing, Quality, and Safety
  • lead the implementation of these applications to capture business value